#!/usr/bin/env python3
# -*- coding: utf-8 -*-

from __future__ import annotations

import ast
import logging
import argparse
import contextlib
import json
import os
import re
import sys
from enum import IntEnum
from pathlib import Path
from hashlib import sha256
from typing import TYPE_CHECKING, Any, Callable, ContextManager, Iterable, Iterator, Literal, Sequence, TypeVar, cast
from itertools import chain
from transformers import AutoConfig

import math
import numpy as np
import torch

if TYPE_CHECKING:
    from torch import Tensor

if 'NO_LOCAL_GGUF' not in os.environ:
    sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
import gguf
from gguf.vocab import MistralTokenizerType, MistralVocab

try:
    from mistral_common.tokens.tokenizers.base import TokenizerVersion # type: ignore[import-not-found, ty:unresolved-import]
    from mistral_common.tokens.tokenizers.multimodal import DATASET_MEAN as _MISTRAL_COMMON_DATASET_MEAN, DATASET_STD as _MISTRAL_COMMON_DATASET_STD # type: ignore[import-not-found, ty:unresolved-import]
    from mistral_common.tokens.tokenizers.tekken import Tekkenizer # type: ignore[import-not-found, ty:unresolved-import]
    from mistral_common.tokens.tokenizers.sentencepiece import ( # type: ignore[import-not-found, ty:unresolved-import]
        SentencePieceTokenizer,
    )

    _mistral_common_installed = True
    _mistral_import_error_msg = ""
except ImportError:
    _MISTRAL_COMMON_DATASET_MEAN = (0.48145466, 0.4578275, 0.40821073)
    _MISTRAL_COMMON_DATASET_STD = (0.26862954, 0.26130258, 0.27577711)

    _mistral_common_installed = False
    TokenizerVersion: Any = None
    Tekkenizer: Any = None
    SentencePieceTokenizer: Any = None
    _mistral_import_error_msg = (
        "Mistral format requires `mistral-common` to be installed. Please run "
        "`pip install mistral-common[image,audio]` to install it."
    )


logger = logging.getLogger("hf-to-gguf")


###### MODEL DEFINITIONS ######

class SentencePieceTokenTypes(IntEnum):
    NORMAL = 1
    UNKNOWN = 2
    CONTROL = 3
    USER_DEFINED = 4
    UNUSED = 5
    BYTE = 6


class ModelType(IntEnum):
    TEXT = 1
    MMPROJ = 2


AnyModel = TypeVar("AnyModel", bound="type[ModelBase]")


class ModelBase:
    _model_classes: dict[ModelType, dict[str, type[ModelBase]]] = {
        ModelType.TEXT: {},
        ModelType.MMPROJ: {},
    }

    dir_model: Path
    ftype: gguf.LlamaFileType
    fname_out: Path
    is_big_endian: bool
    endianess: gguf.GGUFEndian
    use_temp_file: bool
    lazy: bool
    dry_run: bool
    hparams: dict[str, Any]
    model_tensors: dict[str, Callable[[], Tensor]]
    gguf_writer: gguf.GGUFWriter
    model_name: str | None
    metadata_override: Path | None
    dir_model_card: Path
    remote_hf_model_id: str | None

    # subclasses should define this!
    model_arch: gguf.MODEL_ARCH

    # subclasses should initialize this!
    block_count: int
    tensor_map: gguf.TensorNameMap

    # Mistral format specifics
    is_mistral_format: bool = False
    disable_mistral_community_chat_template: bool = False
    sentence_transformers_dense_modules: bool = False

    def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, *, is_big_endian: bool = False,
                 use_temp_file: bool = False, eager: bool = False,
                 metadata_override: Path | None = None, model_name: str | None = None,
                 split_max_tensors: int = 0, split_max_size: int = 0, dry_run: bool = False,
                 small_first_shard: bool = False, hparams: dict[str, Any] | None = None, remote_hf_model_id: str | None = None,
                 disable_mistral_community_chat_template: bool = False,
                 sentence_transformers_dense_modules: bool = False,
                 fuse_gate_up_exps: bool = False):
        if type(self) is ModelBase or \
                type(self) is TextModel or \
                type(self) is MmprojModel:
            raise TypeError(f"{type(self).__name__!r} should not be directly instantiated")

        if self.is_mistral_format and not _mistral_common_installed:
            raise ImportError(_mistral_import_error_msg)

        self.dir_model = dir_model
        self.ftype = ftype
        self.fname_out = fname_out
        self.is_big_endian = is_big_endian
        self.endianess = gguf.GGUFEndian.BIG if is_big_endian else gguf.GGUFEndian.LITTLE
        self.use_temp_file = use_temp_file
        self.lazy = not eager or (remote_hf_model_id is not None)
        self.dry_run = dry_run
        self.remote_hf_model_id = remote_hf_model_id
        self.sentence_transformers_dense_modules = sentence_transformers_dense_modules
        self.fuse_gate_up_exps = fuse_gate_up_exps
        self._gate_exp_buffer: dict[int, Tensor] = {}
        self._up_exp_buffer: dict[int, Tensor] = {}
        self.hparams = ModelBase.load_hparams(self.dir_model, self.is_mistral_format) if hparams is None else hparams
        self.model_tensors = self.index_tensors(remote_hf_model_id=remote_hf_model_id)
        self.metadata_override = metadata_override
        self.model_name = model_name
        self.dir_model_card = dir_model  # overridden in convert_lora_to_gguf.py
        self._is_nvfp4 = False
        self._is_mxfp4 = False

        # Apply heuristics to figure out typical tensor encoding based on first tensor's dtype
        # NOTE: can't use field "torch_dtype" in config.json, because some finetunes lie.
        if self.ftype == gguf.LlamaFileType.GUESSED:
            for _, tensor in self.get_tensors():
                if tensor.dim() < 2:
                    continue

                if tensor.dtype == torch.bfloat16:
                    self.ftype = gguf.LlamaFileType.MOSTLY_BF16
                    logger.info("heuristics detected bfloat16 tensor dtype, setting --outtype bf16")
                    break
                elif tensor.dtype == torch.float16:
                    self.ftype = gguf.LlamaFileType.MOSTLY_F16
                    logger.info("heuristics detected float16 tensor dtype, setting --outtype f16")
                    break
            else:
                self.ftype = gguf.LlamaFileType.MOSTLY_F16
                logger.info("heuristics unable to detect tensor dtype, defaulting to --outtype f16")

        # Configure GGUF Writer
        self.gguf_writer = gguf.GGUFWriter(path=None, arch=gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=self.use_temp_file,
                                           split_max_tensors=split_max_tensors, split_max_size=split_max_size, dry_run=dry_run, small_first_shard=small_first_shard)

        # Mistral specific
        self.disable_mistral_community_chat_template = disable_mistral_community_chat_template

    @classmethod
    def add_prefix_to_filename(cls, path: Path, prefix: str) -> Path:
        stem, suffix = path.stem, path.suffix
        new_name = f"{prefix}{stem}{suffix}"
        return path.with_name(new_name)

    def find_hparam(self, keys: Iterable[str], optional: bool = False) -> Any:
        key = next((k for k in keys if k in self.hparams), None)
        if key is not None:
            return self.hparams[key]
        if optional:
            return None
        raise KeyError(f"could not find any of: {keys}")

    def index_tensors(self, remote_hf_model_id: str | None = None) -> dict[str, Callable[[], Tensor]]:
        tensors: dict[str, Callable[[], Tensor]] = {}

        if remote_hf_model_id is not None:
            is_safetensors = True

            logger.info(f"Using remote model with HuggingFace id: {remote_hf_model_id}")
            remote_tensors = gguf.utility.SafetensorRemote.get_list_tensors_hf_model(remote_hf_model_id)
            for name, remote_tensor in remote_tensors.items():
                data_gen = lambda r=remote_tensor: LazyTorchTensor.from_remote_tensor(r)  # noqa: E731
                if titem := self.filter_tensors((name, data_gen)):
                    tname, tgen = titem
                    tensors[tname] = tgen

            return tensors

        prefix = "model" if not self.is_mistral_format else "consolidated"
        part_names: list[str] = ModelBase.get_model_part_names(self.dir_model, prefix, ".safetensors")
        is_safetensors: bool = len(part_names) > 0
        if not is_safetensors:
            part_names = ModelBase.get_model_part_names(self.dir_model, "pytorch_model", ".bin")

        tensor_names_from_index: set[str] = set()
        tensor_names_from_parts: set[str] = set()

        if not self.is_mistral_format:
            index_name = "model.safetensors" if is_safetensors else "pytorch_model.bin"
            index_name += ".index.json"
            index_file = self.dir_model / index_name

            if index_file.is_file():
                logger.info(f"gguf: loading model weight map from '{index_name}'")
                with open(index_file, "r", encoding="utf-8") as f:
                    index: dict[str, Any] = json.load(f)
                    weight_map = index.get("weight_map")
                    if weight_map is None or not isinstance(weight_map, dict):
                        raise ValueError(f"Can't load 'weight_map' from {index_name!r}")
                    tensor_names_from_index.update(weight_map.keys())
                    part_dict: dict[str, None] = dict.fromkeys(weight_map.values(), None) # ty: ignore[invalid-assignment]
                    part_names = sorted(part_dict.keys())
            else:
                weight_map = {}
        else:
            weight_map = {}

        for part_name in part_names:
            logger.info(f"gguf: indexing model part '{part_name}'")
            ctx: ContextManager[Any]
            if is_safetensors:
                ctx = cast(ContextManager[Any], gguf.utility.SafetensorsLocal(self.dir_model / part_name))
            else:
                ctx = contextlib.nullcontext(torch.load(str(self.dir_model / part_name), map_location="cpu", mmap=True, weights_only=True))

            with ctx as model_part:
                assert model_part is not None

                for name in model_part.keys():
                    tensor_names_from_parts.add(name)
                    if is_safetensors:
                        data: gguf.utility.LocalTensor = model_part[name]
                        if self.lazy:
                            data_gen = lambda data=data: LazyTorchTensor.from_local_tensor(data)  # noqa: E731
                        else:
                            dtype = LazyTorchTensor._dtype_str_map[data.dtype]
                            data_gen = lambda data=data, dtype=dtype: torch.from_numpy(data.mmap_bytes()).view(dtype).reshape(data.shape)  # noqa: E731
                    else:
                        data_torch: Tensor = model_part[name]
                        if self.lazy:
                            data_gen = lambda data=data_torch: LazyTorchTensor.from_eager(data)  # noqa: E731
                        else:
                            data_gen = lambda data=data_torch: data  # noqa: E731
                    if titem := self.filter_tensors((name, data_gen)):
                        tname, tgen = titem
                        tensors[tname] = tgen

        # verify tensor name presence and identify potentially missing files
        if len(tensor_names_from_index) > 0:
            if len(tensor_names_from_parts.symmetric_difference(tensor_names_from_index)) > 0:
                missing = sorted(tensor_names_from_index.difference(tensor_names_from_parts))
                extra = sorted(tensor_names_from_parts.difference(tensor_names_from_index))
                missing_files = sorted(set(weight_map[n] for n in missing if n in weight_map))
                if len(extra) == 0 and len(missing_files) > 0:
                    raise ValueError(f"Missing or incomplete model files: {missing_files}\n"
                                     f"Missing tensors: {missing}")
                else:
                    raise ValueError("Mismatch between weight map and model parts for tensor names:\n"
                                     f"Missing tensors: {missing}\n"
                                     f"Extra tensors: {extra}")

        return tensors

    @staticmethod
    def _scale_is_trivial(scale: Tensor) -> bool:
        return scale.numel() <= 1 and abs(float(scale.float().sum()) - 1.0) < 1e-6

    def _write_scale_tensor(self, scale_name: str, scale: Tensor):
        if not self._scale_is_trivial(scale):
            scale_f32 = scale.float().numpy().flatten()
            logger.info(f"  + {scale_name} (per-tensor scale, shape [{scale_f32.size}])")
            self.gguf_writer.add_tensor(scale_name, scale_f32)

    def _write_scales_tensor(self, scale_name: str, scales: list[float]):
        if not np.allclose(scales, 1.0, atol=1e-6):
            scale_vals = np.array(scales, dtype=np.float32)
            logger.info(f"  + {scale_name} (per-expert scale, shape [{len(scales)}])")
            self.gguf_writer.add_tensor(scale_name, scale_vals)

    def dequant_model(self):
        # If all quantized tensors were already handled (e.g. pure NVFP4), skip
        if self._is_nvfp4 and not any(k.endswith((".weight_scale", ".weight_scale_inv")) for k in self.model_tensors):
            return

        tensors_to_remove: list[str] = []
        new_tensors: dict[str, Callable[[], Tensor]] = {}

        if (quant_config := self.hparams.get("quantization_config")) and isinstance(quant_config, dict):
            quant_method = quant_config.get("quant_method")

            def dequant_bitnet(weight: Tensor, scale: Tensor) -> Tensor:
                weight = weight.view(torch.uint8)
                orig_shape = weight.shape

                shift = torch.tensor([0, 2, 4, 6], dtype=torch.uint8).reshape((4, *(1 for _ in range(len(orig_shape)))))
                data = weight.unsqueeze(0).expand((4, *orig_shape)) >> shift
                data = data & 3
                data = (data.float() - 1).reshape((orig_shape[0] * 4, *orig_shape[1:]))

                # The scale is inverted
                return data / scale.float()

            def dequant_simple(weight: Tensor, scale: Tensor, block_size: Sequence[int] | None = None) -> Tensor:
                scale = scale.float()

                if block_size is not None:
                    dim_offset = scale.ndim - len(block_size)
                    for i, size in enumerate(block_size):
                        scale = scale.repeat_interleave(size, dim_offset + i)
                    # unpad the scale (e.g. when the tensor size isn't a multiple of the block size)
                    scale = scale[tuple(slice(0, size) for size in weight.shape)]

                # align scale dims to weight for correct broadcasting (e.g. [128] -> [128, 1, 1])
                while scale.ndim < weight.ndim:
                    scale = scale.unsqueeze(-1)

                return weight.float() * scale

            # ref: https://github.com/ModelCloud/GPTQModel/blob/037c5c0f6c9e33c500d975b038d02e7ca437546d/gptqmodel/nn_modules/qlinear/__init__.py#L437-L476
            def dequant_gptq(g_idx: Tensor, qweight: Tensor, qzeros: Tensor, scales: Tensor) -> Tensor:
                bits = quant_config["bits"]
                assert bits in (2, 3, 4, 8)
                assert qweight.dtype == qzeros.dtype
                maxq = (2 ** bits) - 1
                weight = None
                zeros = None
                pack_dtype_bits = qweight.dtype.itemsize * 8

                if bits in [2, 4, 8]:
                    pack_factor = pack_dtype_bits // bits
                    wf = torch.tensor(list(range(0, pack_dtype_bits, bits)), dtype=torch.int32).unsqueeze(0)
                    if self.lazy:
                        wf = LazyTorchTensor.from_eager(wf)

                    zeros = torch.bitwise_right_shift(
                        qzeros.unsqueeze(2).expand(-1, -1, pack_factor),
                        wf.unsqueeze(0)
                    ).to(torch.int16 if bits == 8 else torch.int8)
                    zeros = torch.bitwise_and(zeros, maxq).reshape(scales.shape)

                    weight = torch.bitwise_and(
                        torch.bitwise_right_shift(
                            qweight.unsqueeze(1).expand(-1, pack_factor, -1),
                            wf.unsqueeze(-1)
                        ).to(torch.int16 if bits == 8 else torch.int8),
                        maxq
                    )
                elif bits == 3:
                    raise NotImplementedError("3-bit gptq dequantization is not yet implemented")

                assert weight is not None
                assert zeros is not None

                weight = weight.reshape(weight.shape[0] * weight.shape[1], weight.shape[2])

                # gptq_v2 doesn't need to offset zeros
                if quant_config.get("checkpoint_format", "gptq") == "gptq":
                    zeros += 1

                return (scales[g_idx].float() * (weight - zeros[g_idx]).float()).T

            def dequant_packed(w: Tensor, scale: Tensor, shape_tensor: Tensor, zero_point: Tensor | None, num_bits: int, group_size: int):
                assert w.dtype == torch.int32
                shape = tuple(shape_tensor.tolist())
                assert len(shape) == 2
                mask = (1 << num_bits) - 1

                shifts = torch.arange(0, 32 - (num_bits - 1), num_bits, dtype=torch.int32)
                if self.lazy:
                    shifts = LazyTorchTensor.from_eager(shifts)

                if zero_point is None:
                    offset = 1 << (num_bits - 1)
                else:
                    assert len(zero_point.shape) == 2
                    offset = (zero_point.unsqueeze(1) >> shifts.reshape(1, -1, 1)) & mask
                    offset = offset.reshape(-1, zero_point.shape[1])
                    # trim padding, and prepare for broadcast
                    # NOTE: the zero-point is packed along dim 0
                    offset = offset[:shape[0], :].unsqueeze(-1)

                # extract values
                # NOTE: the weights are packed along dim 1
                unpacked = (w.unsqueeze(-1) >> shifts.reshape(1, 1, -1)) & mask
                unpacked = unpacked.reshape(shape[0], -1)

                # trim padding
                unpacked = unpacked[:, :shape[1]]

                # prepare for broadcast of the scale
                unpacked = unpacked.reshape(shape[0], (unpacked.shape[-1] + group_size - 1) // group_size, group_size)
                unpacked = unpacked - offset

                return (unpacked * scale.unsqueeze(-1).float()).reshape(shape)

            if quant_method == "bitnet":
                for name in self.model_tensors.keys():
                    if name.endswith(".weight_scale"):
                        weight_name = name.removesuffix("_scale")
                        w = self.model_tensors[weight_name]
                        s = self.model_tensors[name]
                        self.model_tensors[weight_name] = lambda w=w, s=s: dequant_bitnet(w(), s())
                        tensors_to_remove.append(name)
            elif quant_method == "fp8":
                block_size = quant_config.get("weight_block_size")
                for name in self.model_tensors.keys():
                    if name.endswith("_scale_inv"):
                        weight_name = name.removesuffix("_scale_inv")
                        w = self.model_tensors[weight_name]
                        s = self.model_tensors[name]
                        self.model_tensors[weight_name] = lambda w=w, s=s, bs=block_size: dequant_simple(w(), s(), bs)
                        tensors_to_remove.append(name)
                    if name.endswith(".activation_scale"):  # unused
                        tensors_to_remove.append(name)
                    if name.endswith("_activation_scale"):  # Mistral-Small-4-119B-2602, unused
                        tensors_to_remove.append(name)
                    # mistral format
                    if name.endswith(".qscale_weight"):
                        weight_name = name.removesuffix("qscale_weight") + "weight"
                        w = self.model_tensors[weight_name]
                        s = self.model_tensors[name]
                        self.model_tensors[weight_name] = lambda w=w, s=s, bs=block_size: dequant_simple(w(), s(), bs)
                        tensors_to_remove.append(name)
                    if name.endswith(".qscale_act"):
                        tensors_to_remove.append(name)
            elif quant_method == "gptq":
                for name in self.model_tensors.keys():
                    if name.endswith(".qweight"):
                        base_name = name.removesuffix(".qweight")
                        g_idx = self.model_tensors[base_name + ".g_idx"]
                        qweight = self.model_tensors[base_name + ".qweight"]
                        qzeros = self.model_tensors[base_name + ".qzeros"]
                        scales = self.model_tensors[base_name + ".scales"]
                        new_tensors[base_name + ".weight"] = (
                            lambda g=g_idx, z=qzeros, w=qweight, s=scales: dequant_gptq(
                                g(), w(), z(), s()
                            )
                        )
                        tensors_to_remove += [
                            base_name + n
                            for n in (
                                ".g_idx",
                                ".qzeros",
                                ".qweight",
                                ".scales",
                            )
                        ]
            elif quant_method == "compressed-tensors":
                quant_format = quant_config["format"]
                groups = quant_config["config_groups"]
                if len(groups) > 1:
                    raise NotImplementedError("Can't handle multiple config groups for compressed-tensors yet")
                weight_config = tuple(groups.values())[0]["weights"]

                if quant_format == "float-quantized" or quant_format == "int-quantized" or quant_format == "naive-quantized":
                    block_size = weight_config.get("block_structure", None)
                    strategy = weight_config.get("strategy")
                    assert strategy == "channel" or strategy == "block"
                    assert weight_config.get("group_size") is None  # didn't find a model using this yet
                    for name in self.model_tensors.keys():
                        if name.endswith(".weight_scale"):
                            weight_name = name.removesuffix("_scale")
                            w = self.model_tensors[weight_name]
                            s = self.model_tensors[name]
                            self.model_tensors[weight_name] = lambda w=w, s=s: dequant_simple(w(), s(), block_size)
                            tensors_to_remove.append(name)
                elif quant_format == "pack-quantized":
                    assert weight_config.get("strategy") == "group"
                    assert weight_config.get("type", "int") == "int"
                    num_bits = weight_config.get("num_bits")
                    group_size = weight_config.get("group_size")
                    assert isinstance(num_bits, int)
                    assert isinstance(group_size, int)
                    for name in self.model_tensors.keys():
                        if name.endswith(".weight_packed"):
                            base_name = name.removesuffix("_packed")
                            w = self.model_tensors[name]
                            scale = self.model_tensors[base_name + "_scale"]
                            shape = self.model_tensors[base_name + "_shape"]
                            zero_point = self.model_tensors.get(base_name + "_zero_point", lambda: None)
                            new_tensors[base_name] = (
                                lambda w=w, scale=scale, shape=shape, zero_point=zero_point: dequant_packed(
                                    w(), scale(), shape(), zero_point(), num_bits, group_size,
                                )
                            )
                            tensors_to_remove += [base_name + n for n in ("_packed", "_shape", "_scale")]
                            if (base_name + "_zero_point") in self.model_tensors:
                                tensors_to_remove.append(base_name + "_zero_point")
                else:
                    raise NotImplementedError(f"Quant format {quant_format!r} for method {quant_method!r} is not yet supported")
            elif quant_method == "modelopt":
                # Mixed-precision ModelOpt models: NVFP4 tensors are handled by
                # _generate_nvfp4_tensors; FP8 tensors have 1D weight_scale and
                # are dequantized here. k/v scale tensors are unused.
                for name in self.model_tensors.keys():
                    if name.endswith(".weight_scale"):
                        weight_name = name.removesuffix("_scale")
                        w = self.model_tensors[weight_name]
                        s = self.model_tensors[name]
                        self.model_tensors[weight_name] = lambda w=w, s=s: dequant_simple(w(), s(), None)
                        tensors_to_remove.append(name)
                    if name.endswith((".input_scale", ".k_scale", ".v_scale")):
                        tensors_to_remove.append(name)
            elif quant_method is not None:
                raise NotImplementedError(f"Quant method is not yet supported: {quant_method!r}")

        for name in tensors_to_remove:
            if name in self.model_tensors:
                del self.model_tensors[name]

        for name, value in new_tensors.items():
            self.model_tensors[name] = value

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        if name.endswith("e_score_correction_bias"):
            name = name.replace("e_score_correction_bias", "e_score_correction.bias")

        if "language_model." in name:
            name = name.replace("language_model.", "")

        return name, gen

    def get_tensors(self) -> Iterator[tuple[str, Tensor]]:
        for name, gen in self.model_tensors.items():
            yield name, gen()

    def format_tensor_name(self, key: gguf.MODEL_TENSOR, bid: int | None = None, suffix: str = ".weight") -> str:
        if key not in gguf.MODEL_TENSORS[self.model_arch]:
            raise ValueError(f"Missing {key!r} for MODEL_TENSORS of {self.model_arch!r}")
        name: str = gguf.TENSOR_NAMES[key]
        if "{bid}" in name:
            assert bid is not None
            name = name.format(bid=bid)
        return name + suffix

    def match_model_tensor_name(self, name: str, key: gguf.MODEL_TENSOR, bid: int | None, suffix: str = ".weight") -> bool:
        if key not in gguf.MODEL_TENSORS[self.model_arch]:
            return False
        key_name: str = gguf.TENSOR_NAMES[key]
        if "{bid}" in key_name:
            if bid is None:
                return False
            key_name = key_name.format(bid=bid)
        else:
            if bid is not None:
                return False
        return name == (key_name + suffix)

    def map_tensor_name(self, name: str, try_suffixes: Sequence[str] = (".weight", ".bias")) -> str:
        new_name = self.tensor_map.get_name(key=name, try_suffixes=try_suffixes)
        if new_name is None:
            raise ValueError(f"Can not map tensor {name!r}")
        return new_name

    def set_gguf_parameters(self):
        raise NotImplementedError("set_gguf_parameters() must be implemented in subclasses")

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        new_name = self.map_tensor_name(name)

        # Handle gate/up expert tensor fusion if enabled
        if self.fuse_gate_up_exps and bid is not None:
            if self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.FFN_GATE_EXP, bid):
                self._gate_exp_buffer[bid] = data_torch
            elif self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.FFN_UP_EXP, bid):
                self._up_exp_buffer[bid] = data_torch

            # Check if both gate and up are buffered for this layer
            if bid in self._gate_exp_buffer and bid in self._up_exp_buffer:
                gate_data = self._gate_exp_buffer.pop(bid)
                up_data = self._up_exp_buffer.pop(bid)
                # gate/up shape: (n_expert, n_ff, n_embd), concatenate to (n_expert, n_ff*2, n_embd)
                fused_data = torch.cat([gate_data, up_data], dim=1)
                fused_name = self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE_UP_EXP, bid)
                logger.info(f"Fused gate_exps and up_exps for layer {bid}")
                return [(fused_name, fused_data)]

            # If we buffered a gate/up tensor, wait for the other
            if self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.FFN_GATE_EXP, bid) or \
               self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.FFN_UP_EXP, bid):
                return []

        return [(new_name, data_torch)]

    def tensor_force_quant(self, name: str, new_name: str, bid: int | None, n_dims: int) -> gguf.GGMLQuantizationType | bool:
        del name, new_name, bid, n_dims  # unused

        return False

    # some models need extra generated tensors (like rope_freqs)
    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
        return ()

    @staticmethod
    def _nvfp4_pack(weight: Tensor, scale: Tensor) -> tuple[np.ndarray, list[int]]:
        """Repack NVFP4 ModelOpt tensors into ggml super-block layout.
        Preserves original E4M3 scale bits as UE4M3 (strip sign bit).
        The per-tensor scale2 factor is stored as a separate tensor and applied at inference time via ggml_mul().
        Returns (raw_data, logical_shape)."""

        out_features = weight.shape[0]
        n_blocks = scale.shape[1]

        # Unpack ModelOpt nibble-packed weights
        w = weight.reshape(out_features, n_blocks, 8)
        vals = torch.stack([w & 0x0F, w >> 4], dim=-1).reshape(out_features, n_blocks, 16)

        # Preserve original E4M3 scale bits as UE4M3 (strip sign bit)
        d_ue = scale.view(torch.uint8).numpy().reshape(out_features, n_blocks) & 0x7F
        qs = (vals[:, :, :8] | (vals[:, :, 8:] << 4)).to(torch.uint8).numpy()

        # Pack into super-blocks: [4 UE4M3 scales, 32 qs bytes] = 36 bytes per 64 elements
        n_super = n_blocks // 4
        d_grouped = d_ue.reshape(out_features, n_super, 4)
        qs_grouped = qs.reshape(out_features, n_super, 4, 8).reshape(out_features, n_super, 32)
        raw = np.concatenate([d_grouped, qs_grouped], axis=-1).reshape(out_features, n_super * 36)
        return raw, [out_features, n_super * 64]

    def _repack_nvfp4(self, name: str, weight: Tensor, scale: Tensor, scale2: Tensor, input_scale: Tensor):
        new_name = self.map_tensor_name(name)

        raw, shape = self._nvfp4_pack(weight, scale)
        logger.info(f"Repacked {new_name} with shape {shape} and quantization NVFP4")
        self.gguf_writer.add_tensor(new_name, raw, raw_dtype=gguf.GGMLQuantizationType.NVFP4)

        self._write_scale_tensor(new_name.replace(".weight", ".scale"), scale2)
        self._write_scale_tensor(new_name.replace(".weight", ".input_scale"), input_scale)

    def _generate_nvfp4_tensors(self):
        # Per-layer expert merging to avoid holding all experts in memory
        expert_blocks: dict[tuple[int, str], list[tuple[int, np.ndarray]]] = {}
        expert_scales: dict[tuple[int, str], list[tuple[int, float]]] = {}
        expert_input_scales: dict[tuple[int, str], list[tuple[int, float]]] = {}
        expert_shapes: dict[tuple[int, str], list[int]] = {}
        n_experts = self.find_hparam(["num_local_experts", "num_experts"], optional=True) or 0
        consumed: list[str] = []

        for name in self.model_tensors.keys():
            if not name.endswith(".weight"):
                continue
            scale_name = name.replace(".weight", ".weight_scale")
            scale2_name = name.replace(".weight", ".weight_scale_2")
            input_scale_name = name.replace(".weight", ".input_scale")
            if scale_name not in self.model_tensors:
                continue
            # Force eager materialization of lazy tensors
            weight = LazyTorchTensor.to_eager(self.model_tensors[name]())
            scale = LazyTorchTensor.to_eager(self.model_tensors[scale_name]())

            # Skip non-NVFP4 tensors (e.g. FP8 with per-channel 1D scales)
            if scale.ndim < 2:
                continue

            scale2 = LazyTorchTensor.to_eager(self.model_tensors.get(scale2_name, lambda: torch.tensor(1.0))())
            input_scale = LazyTorchTensor.to_eager(self.model_tensors.get(input_scale_name, lambda: torch.tensor(1.0))())

            # Mark tensors for removal from model_tensors (already written to gguf)
            consumed.extend([name, scale_name])
            if scale2_name in self.model_tensors:
                consumed.append(scale2_name)
            if input_scale_name in self.model_tensors:
                consumed.append(input_scale_name)

            # Check if this is a per-expert tensor
            m = re.search(r'\.experts\.(\d+)\.(gate_proj|up_proj|down_proj)\.weight$', name)
            if m:
                expert_id = int(m.group(1))
                proj_type = m.group(2)
                bid_m = re.search(r'\.layers\.(\d+)\.', name)
                bid = int(bid_m.group(1)) if bid_m else 0
                key = (bid, proj_type)

                raw, shape = self._nvfp4_pack(weight, scale)

                if key not in expert_blocks:
                    expert_blocks[key] = []
                    expert_scales[key] = []
                    expert_input_scales[key] = []
                    expert_shapes[key] = shape
                expert_blocks[key].append((expert_id, raw.copy()))
                # Collect per-expert scale2 (scalar per expert)
                expert_scales[key].append((expert_id, float(scale2.float().sum())))
                # Collect per-expert input_scale (scalar per expert)
                expert_input_scales[key].append((expert_id, float(input_scale.float().sum())))

                # Flush when all experts for this (layer, proj) are collected
                if n_experts > 0 and len(expert_blocks[key]) >= n_experts:
                    self._flush_nvfp4_experts(key, expert_blocks, expert_scales, expert_input_scales, expert_shapes, bid, proj_type)
            else:
                self._repack_nvfp4(name, weight, scale, scale2, input_scale)

        # Flush any remaining experts (fallback if n_experts was unknown)
        for bid, proj_type in list(expert_blocks.keys()):
            self._flush_nvfp4_experts((bid, proj_type), expert_blocks, expert_scales, expert_input_scales, expert_shapes, bid, proj_type)

        # Remove consumed tensors so get_tensors/modify_tensors won't see them
        for name in consumed:
            self.model_tensors.pop(name, None)

        # Remove any remaining unused auxiliary tensors
        for name in list(self.model_tensors.keys()):
            if name.endswith((".k_scale", ".v_scale")):
                del self.model_tensors[name]

    def _flush_nvfp4_experts(self, key, expert_blocks, expert_scales, expert_input_scales, expert_shapes, bid, proj_type):
        experts = expert_blocks.pop(key)
        scales = expert_scales.pop(key)
        input_scales = expert_input_scales.pop(key)
        shape = expert_shapes.pop(key)

        experts.sort(key=lambda x: x[0])
        merged = np.stack([e[1] for e in experts], axis=0)
        merged_name = f"model.layers.{bid}.mlp.experts.{proj_type}.weight"
        new_name = self.map_tensor_name(merged_name)
        logger.info(f"Repacked {new_name} with shape [{len(experts)}, {shape[0]}, {shape[1]}] and quantization NVFP4")
        self.gguf_writer.add_tensor(new_name, merged, raw_dtype=gguf.GGMLQuantizationType.NVFP4)

        scales.sort(key=lambda x: x[0])
        self._write_scales_tensor(new_name.replace(".weight", ".scale"), [s[1] for s in scales])

        input_scales.sort(key=lambda x: x[0])
        self._write_scales_tensor(new_name.replace(".weight", ".input_scale"), [s[1] for s in input_scales])

        del experts, merged

    def prepare_tensors(self):
        # detect NVFP4 quantization (ModelOpt format)
        quant_algo = (self.hparams.get("quantization_config") or {}).get("quant_algo")
        quant_method = (self.hparams.get("quantization_config") or {}).get("quant_method")
        quant_layers = (self.hparams.get("quantization_config") or {}).get("quantized_layers") or {}
        quant_config_file = self.dir_model / "hf_quant_config.json"

        if (not quant_algo or not quant_layers) and quant_config_file.is_file():
            with open(quant_config_file, "r", encoding="utf-8") as f:
                hf_quant_config = json.load(f)
                quant_config = hf_quant_config.get("quantization") or {}
                producer = hf_quant_config.get("producer") or {}
                producer_name = (producer.get("name") or "").lower()
                if quant_method is None:
                    self.hparams.setdefault("quantization_config", {})["quant_method"] = producer_name
                quant_algo = quant_config.get("quant_algo", quant_algo)
                quant_layers = quant_config.get("quantized_layers", quant_layers) or {}

        # Some models use per-tensor quant_algo (e.g. "MIXED_PRECISION" with
        # per-layer NVFP4/FP8) instead of a single global "NVFP4" value.
        if quant_algo != "NVFP4":
            if any(v.get("quant_algo") == "NVFP4" for v in quant_layers.values() if isinstance(v, dict)):
                quant_algo = "NVFP4"

        self._is_nvfp4 = quant_algo == "NVFP4"
        self._is_mxfp4 = quant_method == "mxfp4"

        # NVFP4 weights are repacked and written directly to gguf_writer.
        # This must run before dequant_model so NVFP4 tensors are removed
        # from model_tensors, leaving only non-NVFP4 (e.g. FP8) for dequant.
        if self._is_nvfp4:
            self._generate_nvfp4_tensors()

        self.dequant_model()

        # Handle empty tensor_map for models with block_count=0 (like MobileNetV5)
        if self.tensor_map.mapping:
            max_name_len = max(len(s) for _, s in self.tensor_map.mapping.values()) + len(".weight,")
        else:
            max_name_len = len("vision_encoder.weight,")  # Default reasonable length

        for name, data_torch in chain(self.generate_extra_tensors(), self.get_tensors()):
            # we don't need these
            if name.endswith((".attention.masked_bias", ".attention.bias", ".rotary_emb.inv_freq")):
                continue

            old_dtype = data_torch.dtype

            # convert any unsupported data types to float32
            if data_torch.dtype not in (torch.float16, torch.float32):
                data_torch = data_torch.to(torch.float32)

            # use the first number-like part of the tensor name as the block id
            bid = None
            for part in name.split("."):
                if part.isdecimal():
                    bid = int(part)
                    break

            for new_name, data_torch in (self.modify_tensors(data_torch, name, bid)):
                # TODO: why do we squeeze here?
                # data = data_torch.squeeze().numpy()
                data = data_torch.numpy()

                n_dims = len(data.shape)
                data_qtype: gguf.GGMLQuantizationType | bool = self.tensor_force_quant(name, new_name, bid, n_dims)

                # Most of the codebase that takes in 1D tensors or norms only handles F32 tensors
                if n_dims <= 1 or new_name.endswith("_norm.weight"):
                    data_qtype = gguf.GGMLQuantizationType.F32

                # Conditions should closely match those in llama_model_quantize_internal in llama.cpp
                # Some tensor types are always in float32
                if data_qtype is False and (
                    any(
                        self.match_model_tensor_name(new_name, key, bid)
                        for key in (
                            gguf.MODEL_TENSOR.FFN_GATE_INP,
                            gguf.MODEL_TENSOR.FFN_GATE_INP_SHEXP,
                            gguf.MODEL_TENSOR.POS_EMBD,
                            gguf.MODEL_TENSOR.TOKEN_TYPES,
                            gguf.MODEL_TENSOR.SSM_CONV1D,
                            gguf.MODEL_TENSOR.SHORTCONV_CONV,
                            gguf.MODEL_TENSOR.TIME_MIX_FIRST,
                            gguf.MODEL_TENSOR.TIME_MIX_W1,
                            gguf.MODEL_TENSOR.TIME_MIX_W2,
                            gguf.MODEL_TENSOR.TIME_MIX_DECAY_W1,
                            gguf.MODEL_TENSOR.TIME_MIX_DECAY_W2,
                            gguf.MODEL_TENSOR.TIME_MIX_LERP_FUSED,
                            gguf.MODEL_TENSOR.POSNET_NORM1,
                            gguf.MODEL_TENSOR.POSNET_NORM2,
                            gguf.MODEL_TENSOR.V_ENC_EMBD_POS,
                            gguf.MODEL_TENSOR.A_ENC_EMBD_POS,
                            gguf.MODEL_TENSOR.ALTUP_CORRECT_COEF,
                            gguf.MODEL_TENSOR.ALTUP_PREDICT_COEF,
                            # Kimi KDA conv weights should be F32
                            gguf.MODEL_TENSOR.SSM_CONV1D_Q,
                            gguf.MODEL_TENSOR.SSM_CONV1D_K,
                            gguf.MODEL_TENSOR.SSM_CONV1D_V,
                        )
                    )
                    or new_name[-7:] not in (".weight", ".lora_a", ".lora_b")
                ):
                    data_qtype = gguf.GGMLQuantizationType.F32

                if data_qtype is False and any(
                    self.match_model_tensor_name(new_name, key, bid)
                    for key in (
                        gguf.MODEL_TENSOR.TOKEN_EMBD,
                        gguf.MODEL_TENSOR.PER_LAYER_TOKEN_EMBD,
                        gguf.MODEL_TENSOR.OUTPUT,
                        gguf.MODEL_TENSOR.ALTUP_ROUTER,
                        gguf.MODEL_TENSOR.LAUREL_L,
                        gguf.MODEL_TENSOR.LAUREL_R,
                    )
                ):
                    if self.ftype in (
                        gguf.LlamaFileType.MOSTLY_TQ1_0,
                        gguf.LlamaFileType.MOSTLY_TQ2_0,
                    ):
                        # TODO: use Q4_K and Q6_K
                        data_qtype = gguf.GGMLQuantizationType.F16

                # No override (data_qtype is False), or wants to be quantized (data_qtype is True)
                if isinstance(data_qtype, bool):
                    if self.ftype == gguf.LlamaFileType.ALL_F32:
                        data_qtype = gguf.GGMLQuantizationType.F32
                    elif self.ftype == gguf.LlamaFileType.MOSTLY_F16:
                        data_qtype = gguf.GGMLQuantizationType.F16
                    elif self.ftype == gguf.LlamaFileType.MOSTLY_BF16:
                        data_qtype = gguf.GGMLQuantizationType.BF16
                    elif self.ftype == gguf.LlamaFileType.MOSTLY_Q8_0:
                        data_qtype = gguf.GGMLQuantizationType.Q8_0
                    elif self.ftype == gguf.LlamaFileType.MOSTLY_TQ1_0:
                        data_qtype = gguf.GGMLQuantizationType.TQ1_0
                    elif self.ftype == gguf.LlamaFileType.MOSTLY_TQ2_0:
                        data_qtype = gguf.GGMLQuantizationType.TQ2_0
                    else:
                        raise ValueError(f"Unknown file type: {self.ftype.name}")

                try:
                    data = gguf.quants.quantize(data, data_qtype)
                except gguf.QuantError as e:
                    logger.warning("%s, %s", e, "falling back to F16")
                    data_qtype = gguf.GGMLQuantizationType.F16
                    data = gguf.quants.quantize(data, data_qtype)

                shape = gguf.quant_shape_from_byte_shape(data.shape, data_qtype) if data.dtype == np.uint8 else data.shape

                # reverse shape to make it similar to the internal ggml dimension order
                shape_str = f"{{{', '.join(str(n) for n in reversed(shape))}}}"

                # n_dims is implicit in the shape
                logger.info(f"{f'%-{max_name_len}s' % f'{new_name},'} {old_dtype} --> {data_qtype.name}, shape = {shape_str}")

                self.gguf_writer.add_tensor(new_name, data, raw_dtype=data_qtype)

    def set_type(self):
        self.gguf_writer.add_type(gguf.GGUFType.MODEL)

    def prepare_metadata(self, vocab_only: bool):

        total_params, shared_params, expert_params, expert_count = self.gguf_writer.get_total_parameter_count()

        self.metadata = gguf.Metadata.load(self.metadata_override, self.dir_model_card, self.model_name, total_params)

        # If we are using HF model id, set the metadata name to the model id
        if self.remote_hf_model_id:
            self.metadata.name = self.remote_hf_model_id

        # Fallback to model directory name if metadata name is still missing
        if self.metadata.name is None:
            self.metadata.name = self.dir_model.name

        if self.ftype in (gguf.LlamaFileType.ALL_F32, gguf.LlamaFileType.MOSTLY_F16, gguf.LlamaFileType.MOSTLY_BF16):
            if self._is_nvfp4:
                self.ftype = gguf.LlamaFileType.MOSTLY_NVFP4
            elif self._is_mxfp4:
                self.ftype = gguf.LlamaFileType.MOSTLY_MXFP4_MOE

        # Generate parameter weight class (useful for leader boards) if not yet determined
        if self.metadata.size_label is None and total_params > 0:
            self.metadata.size_label = gguf.size_label(total_params, shared_params, expert_params, expert_count)

        self.set_type()

        logger.info("Set meta model")
        self.metadata.set_gguf_meta_model(self.gguf_writer)

        logger.info("Set model parameters")
        self.set_gguf_parameters()

        logger.info("Set model quantization version")
        self.gguf_writer.add_quantization_version(gguf.GGML_QUANT_VERSION)

    def write_vocab(self):
        raise NotImplementedError("write_vocab() must be implemented in subclasses")

    def write(self):
        self.prepare_tensors()
        self.prepare_metadata(vocab_only=False)
        self.gguf_writer.write_header_to_file(path=self.fname_out)
        self.gguf_writer.write_kv_data_to_file()
        self.gguf_writer.write_tensors_to_file(progress=True)
        self.gguf_writer.close()

    @staticmethod
    def get_model_part_names(dir_model: Path, prefix: str, suffix: str) -> list[str]:
        part_names: list[str] = []
        for filename in os.listdir(dir_model):
            if filename.startswith(prefix) and filename.endswith(suffix):
                part_names.append(filename)

        part_names.sort()

        return part_names

    @staticmethod
    def load_hparams(dir_model: Path, is_mistral_format: bool):
        if is_mistral_format:
            with open(dir_model / "params.json", "r", encoding="utf-8") as f:
                config = json.load(f)
            return config

        try:
            # for security reason, we don't allow loading remote code by default
            # if a model need remote code, we will fallback to config.json
            config = AutoConfig.from_pretrained(dir_model, trust_remote_code=False).to_dict()
        except Exception as e:
            logger.warning(f"Failed to load model config from {dir_model}: {e}")
            logger.warning("Trying to load config.json instead")
            with open(dir_model / "config.json", "r", encoding="utf-8") as f:
                config = json.load(f)
        if "llm_config" in config:
            # rename for InternVL
            config["text_config"] = config["llm_config"]
        if "lm_config" in config:
            # rename for GlmASR
            config["text_config"] = config["lm_config"]
        if "thinker_config" in config:
            # rename for Qwen2.5-Omni
            config["text_config"] = config["thinker_config"]["text_config"]
        if "language_config" in config:
            # rename for DeepSeekOCR
            config["text_config"] = config["language_config"]
        if "lfm" in config:
            # rename for LFM2-Audio
            config["text_config"] = config["lfm"]
        return config

    @classmethod
    def register(cls, *names: str) -> Callable[[AnyModel], AnyModel]:
        assert names

        def func(modelcls: AnyModel) -> AnyModel:
            model_type = ModelType.MMPROJ if modelcls.model_arch == gguf.MODEL_ARCH.MMPROJ else ModelType.TEXT
            for name in names:
                cls._model_classes[model_type][name] = modelcls
            return modelcls
        return func

    @classmethod
    def print_registered_models(cls):
        for model_type, model_classes in cls._model_classes.items():
            logger.error(f"{model_type.name} models:")
            for name in sorted(model_classes.keys()):
                logger.error(f"  - {name}")

    @classmethod
    def from_model_architecture(cls, arch: str, model_type = ModelType.TEXT) -> type[ModelBase]:
        try:
            return cls._model_classes[model_type][arch]
        except KeyError:
            raise NotImplementedError(f'Architecture {arch!r} not supported!') from None


class TextModel(ModelBase):
    model_type = ModelType.TEXT
    hf_arch: str

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        if not self.is_mistral_format:
            self.hf_arch = get_model_architecture(self.hparams, self.model_type)
        else:
            self.hf_arch = ""

        if "text_config" in self.hparams:
            # move the text_config to the root level
            self.hparams = {**self.hparams, **self.hparams["text_config"]}

        self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer", "num_layers"])
        self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)

        self.rope_parameters = self.hparams.get("rope_parameters", self.hparams.get("rope_scaling")) or {}

        rope_theta = self.find_hparam(["global_rope_theta", "rope_global_theta", "rope_theta_global", "rope_theta", "rotary_emb_base"], optional=True)
        local_rope_theta = self.find_hparam(["local_rope_theta", "rope_local_theta", "rope_theta_local", "swa_rope_theta", "rope_local_base_freq"], optional=True)

        # Ensure "rope_theta" and "rope_type" is mirrored in rope_parameters
        if "full_attention" not in self.rope_parameters and "sliding_attention" not in self.rope_parameters:
            if local_rope_theta is not None:
                self.rope_parameters["sliding_attention"] = {"rope_theta": local_rope_theta}
            if "rope_theta" not in self.rope_parameters and rope_theta is not None:
                self.rope_parameters["rope_theta"] = rope_theta
            if "rope_type" not in self.rope_parameters and (rope_type := self.rope_parameters.get("type")) is not None:
                self.rope_parameters["rope_type"] = rope_type

    @classmethod
    def __init_subclass__(cls):
        # can't use an abstract property, because overriding it without type errors
        # would require using decorated functions instead of simply defining the property
        if "model_arch" not in cls.__dict__:
            raise TypeError(f"Missing property 'model_arch' for {cls.__name__!r}")

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        # Skip multimodal tensors
        if name.startswith(("mlp", "vit.", "vpm.", "siglip2.", "conformer.", "merger.", "resampler.", "sound_encoder.", "sound_projection.", "speech_embeddings.")) \
                or "visual." in name or "vision." in name or "audio." in name or "talker." in name \
                or "vision_" in name or "audio_" in name or "sam_model" in name \
                or "token2wav." in name or "code2wav." in name \
                or "projector." in name or "pre_mm_projector_norm" in name \
                or "image_newline" in name or "view_seperator" in name \
                or "patch_embed" in name or "patch_embedding" in name \
                or "patch_merger." in name or "model.connector." in name:
            return None

        return super().filter_tensors(item)

    def set_vocab(self):
        self._set_vocab_gpt2()

    def prepare_metadata(self, vocab_only: bool):
        super().prepare_metadata(vocab_only=vocab_only)

        total_params = self.gguf_writer.get_total_parameter_count()[0]
        # Extract the encoding scheme from the file type name. e.g. 'gguf.LlamaFileType.MOSTLY_Q8_0' --> 'Q8_0'
        output_type: str = self.ftype.name.partition("_")[2]

        # Filename Output
        if self.fname_out.is_dir():
            # Generate default filename based on model specification and available metadata
            if not vocab_only:
                fname_default: str = gguf.naming_convention(self.metadata.name, self.metadata.basename, self.metadata.finetune, self.metadata.version, self.metadata.size_label, output_type, model_type="LoRA" if total_params < 0 else None)
            else:
                fname_default: str = gguf.naming_convention(self.metadata.name, self.metadata.basename, self.metadata.finetune, self.metadata.version, size_label=None, output_type=None, model_type="vocab")

            # Use the default filename
            self.fname_out = self.fname_out / f"{fname_default}.gguf"
        else:
            # Output path is a custom defined templated filename
            # Note: `not is_dir()` is used because `.is_file()` will not detect
            #       file template strings as it doesn't actually exist as a file

            # Process templated file name with the output ftype, useful with the "auto" ftype
            self.fname_out = self.fname_out.parent / gguf.fill_templated_filename(self.fname_out.name, output_type)

        logger.info("Set model tokenizer")
        self.set_vocab()

    def set_gguf_parameters(self):
        self.gguf_writer.add_block_count(self.block_count)

        if (n_ctx := self.find_hparam(["max_position_embeddings", "n_ctx", "n_positions", "max_length", "max_sequence_length", "model_max_length"], optional=True)) is not None:
            self.gguf_writer.add_context_length(n_ctx)
            logger.info(f"gguf: context length = {n_ctx}")

        if (n_embd := self.find_hparam(["hidden_size", "n_embd", "dim"], optional=True)) is not None:
            self.gguf_writer.add_embedding_length(n_embd)
            logger.info(f"gguf: embedding length = {n_embd}")

        if (n_ff := self.find_hparam(["intermediate_size", "n_inner", "hidden_dim"], optional=True)) is not None:
            self.gguf_writer.add_feed_forward_length(n_ff)
            logger.info(f"gguf: feed forward length = {n_ff}")

        if (n_head := self.find_hparam(["num_attention_heads", "n_head", "n_heads"], optional=True)) is not None:
            self.gguf_writer.add_head_count(n_head)
            logger.info(f"gguf: head count = {n_head}")

        if (n_head_kv := self.find_hparam(["num_key_value_heads", "n_kv_heads"], optional=True)) is not None:
            self.gguf_writer.add_head_count_kv(n_head_kv)
            logger.info(f"gguf: key-value head count = {n_head_kv}")

        if self.hparams.get("is_causal") is False:
            self.gguf_writer.add_causal_attention(False)
            logger.info("gguf: causal attention = False")

        # TODO: Handle "sliding_attention" similarly when models start implementing it
        rope_params = self.rope_parameters.get("full_attention", self.rope_parameters)
        if (rope_type := rope_params.get("rope_type")) is not None:
            rope_factor = rope_params.get("factor")
            rope_gguf_type = gguf.RopeScalingType.NONE
            if rope_type == "linear" and rope_factor is not None:
                rope_gguf_type = gguf.RopeScalingType.LINEAR
                self.gguf_writer.add_rope_scaling_type(rope_gguf_type)
                self.gguf_writer.add_rope_scaling_factor(rope_factor)
            elif rope_type == "yarn" and rope_factor is not None:
                rope_gguf_type = gguf.RopeScalingType.YARN
                self.gguf_writer.add_rope_scaling_type(rope_gguf_type)
                self.gguf_writer.add_rope_scaling_factor(rope_factor)
                self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_params["original_max_position_embeddings"])
                if (yarn_ext_factor := rope_params.get("extrapolation_factor")) is not None:
                    self.gguf_writer.add_rope_scaling_yarn_ext_factor(yarn_ext_factor)
                if (yarn_attn_factor := rope_params.get("attention_factor", rope_params.get("attn_factor"))) is not None:
                    self.gguf_writer.add_rope_scaling_yarn_attn_factor(yarn_attn_factor)
                if (yarn_beta_fast := rope_params.get("beta_fast")) is not None:
                    self.gguf_writer.add_rope_scaling_yarn_beta_fast(yarn_beta_fast)
                if (yarn_beta_slow := rope_params.get("beta_slow")) is not None:
                    self.gguf_writer.add_rope_scaling_yarn_beta_slow(yarn_beta_slow)
                # self.gguf_writer.add_rope_scaling_yarn_log_mul(rope_params["mscale_all_dim"])
            elif rope_type == "su" or rope_type == "longrope":
                rope_gguf_type = gguf.RopeScalingType.LONGROPE
                self.gguf_writer.add_rope_scaling_type(rope_gguf_type)
            elif rope_type == "dynamic":
                # HunYuan, handled in model class
                pass
            elif rope_type.lower() == "llama3":
                # Handled in generate_extra_tensors
                pass
            else:
                logger.warning(f"Unknown RoPE type: {rope_type}")
            logger.info(f"gguf: rope scaling type = {rope_gguf_type.name}")

        if "mrope_section" in self.rope_parameters:
            mrope_section = self.rope_parameters["mrope_section"]
            # Pad to 4 dimensions [time, height, width, extra]
            while len(mrope_section) < 4:
                mrope_section.append(0)
            self.gguf_writer.add_rope_dimension_sections(mrope_section[:4])
            logger.info(f"gguf: mrope sections: {mrope_section[:4]}")

        if (rope_theta := rope_params.get("rope_theta")) is not None:
            self.gguf_writer.add_rope_freq_base(rope_theta)
            logger.info(f"gguf: rope theta = {rope_theta}")
        if (local_rope_theta := self.rope_parameters.get("sliding_attention", {}).get("rope_theta")) is not None:
            self.gguf_writer.add_rope_freq_base_swa(local_rope_theta)
            logger.info(f"gguf: rope theta swa = {local_rope_theta}")
        if (f_rms_eps := self.find_hparam(["rms_norm_eps", "norm_eps"], optional=True)) is not None:
            self.gguf_writer.add_layer_norm_rms_eps(f_rms_eps)
            logger.info(f"gguf: rms norm epsilon = {f_rms_eps}")
        if (f_norm_eps := self.find_hparam(["layer_norm_eps", "layer_norm_epsilon", "norm_epsilon"], optional=True)) is not None:
            self.gguf_writer.add_layer_norm_eps(f_norm_eps)
            logger.info(f"gguf: layer norm epsilon = {f_norm_eps}")
        if (n_experts := self.find_hparam(["num_local_experts", "num_experts"], optional=True)) is not None:
            self.gguf_writer.add_expert_count(n_experts)
            logger.info(f"gguf: expert count = {n_experts}")
        if (n_experts_used := self.find_hparam(["num_experts_per_tok", "num_experts_per_token", "top_k_experts"], optional=True)) is not None:
            self.gguf_writer.add_expert_used_count(n_experts_used)
            logger.info(f"gguf: experts used count = {n_experts_used}")
        if (n_expert_groups := self.hparams.get("n_group")) is not None:
            self.gguf_writer.add_expert_group_count(n_expert_groups)
            logger.info(f"gguf: expert groups count = {n_expert_groups}")
        if (n_group_used := self.hparams.get("topk_group")) is not None:
            self.gguf_writer.add_expert_group_used_count(n_group_used)
            logger.info(f"gguf: expert groups used count = {n_group_used}")

        if (score_func := self.find_hparam(["score_function", "scoring_func", "score_func", "moe_router_activation", "moe_router_activation_func"], optional=True)) is not None:
            if score_func == "sigmoid":
                self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
            elif score_func == "softmax":
                self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SOFTMAX)
            else:
                raise ValueError(f"Unsupported expert score gating function value: {score_func}")
            logger.info(f"gguf: expert score gating function = {score_func}")

        if (head_dim := self.hparams.get("head_dim")) is not None:
            self.gguf_writer.add_key_length(head_dim)
            self.gguf_writer.add_value_length(head_dim)

        self.gguf_writer.add_file_type(self.ftype)
        logger.info(f"gguf: file type = {self.ftype}")

    def write_vocab(self):
        if len(self.gguf_writer.tensors) != 1:
            raise ValueError('Splitting the vocabulary is not supported')

        self.prepare_metadata(vocab_only=True)
        self.gguf_writer.write_header_to_file(path=self.fname_out)
        self.gguf_writer.write_kv_data_to_file()
        self.gguf_writer.close()

    def does_token_look_special(self, token: str | bytes) -> bool:
        if isinstance(token, (bytes, bytearray)):
            token_text = token.decode(encoding="utf-8")
        elif isinstance(token, memoryview):
            token_text = token.tobytes().decode(encoding="utf-8")
        else:
            token_text = token

        # Some models mark some added tokens which ought to be control tokens as not special.
        # (e.g. command-r, command-r-plus, deepseek-coder, gemma{,-2})
        seems_special = token_text in (
            "<pad>",  # deepseek-coder
            "<mask>", "<2mass>", "[@BOS@]",  # gemma{,-2}
        )

        seems_special = seems_special or (token_text.startswith("<|") and token_text.endswith("|>"))
        seems_special = seems_special or (token_text.startswith("<｜") and token_text.endswith("｜>"))  # deepseek-coder

        # TODO: should these be marked as UNUSED instead? (maybe not)
        seems_special = seems_special or (token_text.startswith("<unused") and token_text.endswith(">"))  # gemma{,-2}

        return seems_special

    # used for GPT-2 BPE and WordPiece vocabs
    def get_vocab_base(self) -> tuple[list[str], list[int], str]:
        tokens: list[str] = []
        toktypes: list[int] = []

        from transformers import AutoTokenizer
        tokenizer = AutoTokenizer.from_pretrained(self.dir_model)
        vocab_size = self.hparams.get("vocab_size", len(tokenizer.vocab))  # ty: ignore[unresolved-attribute]
        assert max(tokenizer.vocab.values()) < vocab_size  # ty: ignore[unresolved-attribute]

        tokpre = self.get_vocab_base_pre(tokenizer)

        reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()}  # ty: ignore[unresolved-attribute]
        added_vocab = tokenizer.get_added_vocab()  # ty: ignore[unresolved-attribute]

        added_tokens_decoder = tokenizer.added_tokens_decoder  # ty: ignore[unresolved-attribute]

        for i in range(vocab_size):
            if i not in reverse_vocab:
                tokens.append(f"[PAD{i}]")
                toktypes.append(gguf.TokenType.UNUSED)
            else:
                token: str = reverse_vocab[i]
                if token in added_vocab:
                    # The tokenizer in llama.cpp assumes the CONTROL and USER_DEFINED tokens are pre-normalized.
                    # To avoid unexpected issues - we make sure to normalize non-normalized tokens
                    if not added_tokens_decoder[i].normalized:
                        previous_token = token
                        token = tokenizer.decode(tokenizer.encode(token, add_special_tokens=False))  # ty: ignore[unresolved-attribute, invalid-assignment]
                        if previous_token != token:
                            logger.info(f"{repr(previous_token)} is encoded and decoded back to {repr(token)} using AutoTokenizer")

                    if added_tokens_decoder[i].special or self.does_token_look_special(token):
                        toktypes.append(gguf.TokenType.CONTROL)
                    else:
                        # NOTE: this was added for Gemma.
                        # Encoding and decoding the tokens above isn't sufficient for this case.
                        token = token.replace(b"\xe2\x96\x81".decode("utf-8"), " ")  # pre-normalize user-defined spaces
                        toktypes.append(gguf.TokenType.USER_DEFINED)
                else:
                    toktypes.append(gguf.TokenType.NORMAL)
                tokens.append(token)

        return tokens, toktypes, tokpre

    # NOTE: this function is generated by convert_hf_to_gguf_update.py
    #       do not modify it manually!
    # ref:  https://github.com/ggml-org/llama.cpp/pull/6920
    # Marker: Start get_vocab_base_pre
    def get_vocab_base_pre(self, tokenizer) -> str:
        # encoding this string and hashing the resulting tokens would (hopefully) give us a unique identifier that
        # is specific for the BPE pre-tokenizer used by the model
        # we will use this unique identifier to write a "tokenizer.ggml.pre" entry in the GGUF file which we can
        # use in llama.cpp to implement the same pre-tokenizer

        chktxt = '\n \n\n \n\n\n \t \t\t \t\n  \n   \n    \n     \n🚀 (normal) 😶\u200d🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български \'\'\'\'\'\'```````""""......!!!!!!?????? I\'ve been \'told he\'s there, \'RE you sure? \'M not sure I\'ll make it, \'D you like some tea? We\'Ve a\'lL'

        chktok = tokenizer.encode(chktxt)
        chkhsh = sha256(str(chktok).encode()).hexdigest()

        logger.debug(f"chktok: {chktok}")
        logger.debug(f"chkhsh: {chkhsh}")

        res = None

        # NOTE: if you get an error here, you need to update the convert_hf_to_gguf_update.py script
        #       or pull the latest version of the model from Huggingface
        #       don't edit the hashes manually!
        if chkhsh == "b6e8e1518dc4305be2fe39c313ed643381c4da5db34a98f6a04c093f8afbe99b":
            # ref: https://huggingface.co/THUDM/glm-4-9b-chat
            res = "chatglm-bpe"
        if chkhsh == "81d72c7348a9f0ebe86f23298d37debe0a5e71149e29bd283904c02262b27516":
            # ref: https://huggingface.co/THUDM/glm-4-9b-chat
            res = "chatglm-bpe"
        if chkhsh == "a1336059768a55c99a734006ffb02203cd450fed003e9a71886c88acf24fdbc2":
            # ref: https://huggingface.co/THUDM/glm-4-9b-hf
            res = "glm4"
        if chkhsh == "9ca2dd618e8afaf09731a7cf6e2105b373ba6a1821559f258b272fe83e6eb902":
            # ref: https://huggingface.co/zai-org/GLM-4.5-Air
            res = "glm4"
        if chkhsh == "cdf5f35325780597efd76153d4d1c16778f766173908894c04afc20108536267":
            # ref: https://huggingface.co/zai-org/GLM-4.7-Flash
            res = "glm4"
        if chkhsh == "1431a23e583c97432bc230bff598d103ddb5a1f89960c8f1d1051aaa944d0b35":
            # ref: https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0
            res = "minerva-7b"
        if chkhsh == "7e57df22b1fe23a7b1e1c7f3dc4e3f96d43a4eb0836d0c6bdc3436d7b2f1c664":
            # ref: https://huggingface.co/tencent/Hunyuan-A13B-Instruct
            res = "hunyuan"
        if chkhsh == "bba3b3366b646dbdded5dbc42d59598b849371afc42f7beafa914afaa5b70aa6":
            # ref: https://huggingface.co/tencent/Hunyuan-4B-Instruct
            res = "hunyuan-dense"
        if chkhsh == "a6b57017d60e6edb4d88ecc2845188e0eb333a70357e45dcc9b53964a73bbae6":
            # ref: https://huggingface.co/tiiuae/Falcon-H1-0.5B-Base
            res = "falcon-h1"
        if chkhsh == "60476e1243776c4fb1b993dbd7a5f15ac22f83c80afdf425fa5ae01c8d44ef86":
            # ref: https://huggingface.co/tiiuae/Falcon-H1-1B-Base
            res = "falcon-h1"
        if chkhsh == "3eda48b4c4dc7de733d1a8b3e3b4a85243dbbf704da2ee9d42c6beced8897896":
            # ref: https://huggingface.co/tiiuae/Falcon-H1-7B-Base
            res = "falcon-h1"
        if chkhsh == "48f8e02c0359c0bbdd82f26909171fac1c18a457bb47573ed1fe3bbb2c1cfd4b":
            # ref: https://huggingface.co/tiiuae/Falcon-H1-34B-Base
            res = "falcon-h1"
        if chkhsh == "81212dc7cdb7e0c1074ca62c5aeab0d43c9f52b8a737be7b12a777c953027890":
            # ref: https://huggingface.co/moonshotai/Kimi-K2-Base
            res = "kimi-k2"
        if chkhsh == "d4540891389ea895b53b399da6ac824becc30f2fba0e9ddbb98f92e55ca0e97c":
            # ref: https://huggingface.co/Qwen/Qwen3-Embedding-0.6B
            res = "qwen2"
        if chkhsh == "1444df51289cfa8063b96f0e62b1125440111bc79a52003ea14b6eac7016fd5f":
            # ref: https://huggingface.co/openbmb/MiniCPM-V-4_6
            res = "qwen35"
        if chkhsh == "66b8d4e19ab16c3bfd89bce5d785fb7e0155e8648708a1f42077cb9fe002c273":
            # ref: https://huggingface.co/alvarobartt/grok-2-tokenizer
            res = "grok-2"
        if chkhsh == "b3d1dd861f1d4c5c0d2569ce36baf3f90fe8a102db3de50dd71ff860d91be3df":
            # ref: https://huggingface.co/aari1995/German_Semantic_V3
            res = "jina-v2-de"
        if chkhsh == "0fe1cf6eda062318a1af7270f3331a85c539a01778ff948e24388e949c5282f4":
            # ref: https://huggingface.co/evilfreelancer/ruGPT3XL
            res = "gpt-2"
        if chkhsh == "0ef9807a4087ebef797fc749390439009c3b9eda9ad1a097abbe738f486c01e5":
            # ref: https://huggingface.co/meta-llama/Meta-Llama-3-8B
            res = "llama-bpe"
        if chkhsh == "049ecf7629871e3041641907f3de7c733e4dbfdc736f57d882ba0b0845599754":
            # ref: https://huggingface.co/deepseek-ai/deepseek-llm-7b-base
            res = "deepseek-llm"
        if chkhsh == "347715f544604f9118bb75ed199f68779f423cabb20db6de6f31b908d04d7821":
            # ref: https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base
            res = "deepseek-coder"
        if chkhsh == "8aeee3860c56296a157a1fe2fad249ec40aa59b1bb5709f4ade11c4e6fe652ed":
            # ref: https://huggingface.co/tiiuae/falcon-7b
            res = "falcon"
        if chkhsh == "0876d13b50744004aa9aeae05e7b0647eac9d801b5ba4668afc01e709c15e19f":
            # ref: https://huggingface.co/BAAI/bge-small-en-v1.5
            res = "bert-bge"
        if chkhsh == "9d032fcbd5501f4a38150912590928bfb36091efb5df11b8e2124b0390e3fb1e":
            # ref: https://huggingface.co/tiiuae/Falcon3-7B-Base
            res = "falcon3"
        if chkhsh == "8e62295832751ca1e8f92f2226f403dea30dc5165e448b5bfa05af5340c64ec7":
            # ref: https://huggingface.co/BAAI/bge-large-zh-v1.5
            res = "bert-bge-large"
        if chkhsh == "b6dc8df998e1cfbdc4eac8243701a65afe638679230920b50d6f17d81c098166":
            # ref: https://huggingface.co/mosaicml/mpt-7b
            res = "mpt"
        if chkhsh == "35d91631860c815f952d711435f48d356ebac988362536bed955d43bfa436e34":
            # ref: https://huggingface.co/bigcode/starcoder2-3b
            res = "starcoder"
        if chkhsh == "3ce83efda5659b07b1ad37ca97ca5797ea4285d9b9ab0dc679e4a720c9da7454":
            # ref: https://huggingface.co/openai-community/gpt2
            res = "gpt-2"
        if chkhsh == "32d85c31273f8019248f2559fed492d929ea28b17e51d81d3bb36fff23ca72b3":
            # ref: https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b
            res = "stablelm2"
        if chkhsh == "6221ad2852e85ce96f791f476e0b390cf9b474c9e3d1362f53a24a06dc8220ff":
            # ref: https://huggingface.co/smallcloudai/Refact-1_6-base
            res = "refact"
        if chkhsh == "9c2227e4dd922002fb81bde4fc02b0483ca4f12911410dee2255e4987644e3f8":
            # ref: https://huggingface.co/CohereForAI/c4ai-command-r-v01
            res = "command-r"
        if chkhsh == "d772b220ace2baec124bed8cfafce0ead7d6c38a4b65ef11261cf9d5d62246d1":
            # ref: https://huggingface.co/CohereLabs/tiny-aya-base
            res = "tiny_aya"
        if chkhsh == "e636dc30a262dcc0d8c323492e32ae2b70728f4df7dfe9737d9f920a282b8aea":
            # ref: https://huggingface.co/Qwen/Qwen1.5-7B
            res = "qwen2"
        if chkhsh == "b6dc8df998e1cfbdc4eac8243701a65afe638679230920b50d6f17d81c098166":
            # ref: https://huggingface.co/allenai/OLMo-1.7-7B-hf
            res = "olmo"
        if chkhsh == "a8594e3edff7c29c003940395316294b2c623e09894deebbc65f33f1515df79e":
            # ref: https://huggingface.co/databricks/dbrx-base
            res = "dbrx"
        if chkhsh == "c7699093ba4255a91e702aa38a596aa81669f3525dae06c2953267dde580f448":
            # ref: https://huggingface.co/jinaai/jina-reranker-v1-tiny-en
            res = "jina-v1-en"
        if chkhsh == "0876d13b50744004aa9aeae05e7b0647eac9d801b5ba4668afc01e709c15e19f":
            # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-en
            res = "jina-v2-en"
        if chkhsh == "171aeeedd6fb548d418a7461d053f11b6f1f1fc9b387bd66640d28a4b9f5c643":
            # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-es
            res = "jina-v2-es"
        if chkhsh == "27949a2493fc4a9f53f5b9b029c82689cfbe5d3a1929bb25e043089e28466de6":
            # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-de
            res = "jina-v2-de"
        if chkhsh == "a023e9fdc5a11f034d3ef515b92350e56fb2af1f66c6b6811a4444ea9bf8763d":
            # ref: https://huggingface.co/jinaai/jina-embeddings-v5-text-nano
            res = "jina-v5-nano"
        if chkhsh == "c136ed14d01c2745d4f60a9596ae66800e2b61fa45643e72436041855ad4089d":
            # ref: https://huggingface.co/abacusai/Smaug-Llama-3-70B-Instruct
            res = "smaug-bpe"
        if chkhsh == "c7ea5862a53e4272c035c8238367063e2b270d51faa48c0f09e9d5b54746c360":
            # ref: https://huggingface.co/LumiOpen/Poro-34B-chat
            res = "poro-chat"
        if chkhsh == "7967bfa498ade6b757b064f31e964dddbb80f8f9a4d68d4ba7998fcf281c531a":
            # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-code
            res = "jina-v2-code"
        if chkhsh == "7fc505bd3104ca1083b150b17d088b59534ede9bde81f0dd2090967d7fe52cee":
            # ref: https://huggingface.co/LumiOpen/Viking-7B
            res = "viking"
        if chkhsh == "b53802fb28e26d645c3a310b34bfe07da813026ec7c7716883404d5e0f8b1901":
            # ref: https://huggingface.co/core42/jais-13b
            res = "jais"
        if chkhsh == "bc5108ee1eb6a3d600cadd065f63190fbd0554dbc9e4bbd6a0d977970afc8d2a":
            # ref: https://huggingface.co/inceptionai/Jais-2-8B-Chat
            res = "jais-2"
        if chkhsh == "7b3e7548e4308f52a76e8229e4e6cc831195d0d1df43aed21ac6c93da05fec5f":
            # ref: https://huggingface.co/WisdomShell/CodeShell-7B
            res = "codeshell"
        if chkhsh == "63b97e4253352e6f357cc59ea5b583e3a680eaeaf2632188c2b952de2588485e":
            # ref: https://huggingface.co/mistralai/Mistral-Nemo-Base-2407
            res = "tekken"
        if chkhsh == "855059429035d75a914d1eda9f10a876752e281a054a7a3d421ef0533e5b6249":
            # ref: https://huggingface.co/HuggingFaceTB/SmolLM-135M
            res = "smollm"
        if chkhsh == "3c30d3ad1d6b64202cd222813e7736c2db6e1bd6d67197090fc1211fbc612ae7":
            # ref: https://huggingface.co/bigscience/bloom
            res = "bloom"
        if chkhsh == "bc01ce58980e1db43859146dc51b1758b3b88729b217a74792e9f8d43e479d21":
            # ref: https://huggingface.co/TurkuNLP/gpt3-finnish-small
            res = "gpt3-finnish"
        if chkhsh == "4e2b24cc4770243d65a2c9ec19770a72f08cffc161adbb73fcbb6b7dd45a0aae":
            # ref: https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct
            res = "exaone"
        if chkhsh == "fcace8b9cac38ce847670c970cd5892031a753a1ef381abd1d9af00f713da085":
            # ref: https://huggingface.co/microsoft/phi-2
            res = "phi-2"
        if chkhsh == "60824e3c0d9401f89943cbb2fff727f0e2d4c545ba4df2d6e4f09a6db0f5b450":
            # ref: https://huggingface.co/facebook/chameleon-7b
            res = "chameleon"
        if chkhsh == "8b5a93ed704057481f240da0be7e7dca721d7f8f4755263b6807227a2cbeae65":
            # ref: https://huggingface.co/sentence-transformers/stsb-roberta-base
            res = "roberta-bpe"
        if chkhsh == "ad851be1dba641f2e3711822f816db2c265f788b37c63b4e1aeacb9ee92de8eb":
            # ref: https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct
            res = "gigachat"
        if chkhsh == "d4c8f286ea6b520b3d495c4455483cfa2302c0cfcd4be05d781b6a8a0a7cdaf1":
            # ref: https://huggingface.co/Infinigence/Megrez-3B-Instruct
            res = "megrez"
        if chkhsh == "877081d19cf6996e2c4ff0e1236341e9b7bde288f5311a56a937f0afbbb3aeb5":
            # ref: https://huggingface.co/deepseek-ai/DeepSeek-V3
            res = "deepseek-v3"
        if chkhsh == "b3f499bb4255f8ca19fccd664443283318f2fd2414d5e0b040fbdd0cc195d6c5":
            # ref: https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
            res = "deepseek-r1-qwen"
        if chkhsh == "ccc2ef013c104be7bae2965776d611e1d7a8a2a9c547dd93a682c9a9fc80352e":
            # ref: https://huggingface.co/Xenova/gpt-4o
            res = "gpt-4o"
        if chkhsh == "7dec86086fcc38b66b7bc1575a160ae21cf705be7718b9d5598190d7c12db76f":
            # ref: https://huggingface.co/UW/OLMo2-8B-SuperBPE-t180k
            res = "superbpe"
        if chkhsh == "1994ffd01900cfb37395608534236ecd63f2bd5995d6cb1004dda1af50240f15":
            # ref: https://huggingface.co/trillionlabs/Trillion-7B-preview
            res = "trillion"
        if chkhsh == "96a5f08be6259352137b512d4157e333e21df7edd3fcd152990608735a65b224":
            # ref: https://huggingface.co/inclusionAI/Ling-lite
            res = "bailingmoe"
        if chkhsh == "d353350c764d8c3b39c763113960e4fb4919bea5fbf208a0e3b22e8469dc7406":
            # ref: https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct
            res = "llama4"
        if chkhsh == "0e9433cbbb161f89e264eb32e8e64bfe69e834973ffca5d41d3948a604a3e2a3":
            # ref: https://huggingface.co/mistral-community/pixtral-12b
            res = "pixtral"
        if chkhsh == "d5f1dd6f980fec569fb218a81a7658ac45fc56b38c5a0adeb1c232fbe04ef5ec":
            # ref: https://huggingface.co/ByteDance-Seed/Seed-Coder-8B-Base
            res = "seed-coder"
        if chkhsh == "b0a6b1c0bd5998ebd9df08611efde34a4ff03faed45ae09c43e6b31ebd4b94cf":
            # ref: https://huggingface.co/skt/A.X-4.0
            res = "a.x-4.0"
        if chkhsh == "f6791d196f87ce6b56a7d234be618e0d58f8cda3549416635b2bebcd22cd95c4":
            # ref: https://huggingface.co/K-intelligence/Midm-2.0-Base-Instruct
            res = "midm-2.0"
        if chkhsh == "169bf0296a13c4d9b7672313f749eb36501d931022de052aad6e36f2bf34dd51":
            # ref: https://huggingface.co/LiquidAI/LFM2-Tokenizer
            res = "lfm2"
        if chkhsh == "2085e1638f6c377a0aa4ead21b27bb4cb941bf800df86ed391011769c1758dfb":
            # ref: https://huggingface.co/LGAI-EXAONE/EXAONE-4.0-32B
            res = "exaone4"
        if chkhsh == "a1e163ecab2e718a4c829d1148b6e86824ec36163bb71941c3dca9cd5ac25756":
            # ref: https://huggingface.co/JetBrains/Mellum-4b-base
            res = "mellum"
        if chkhsh == "a0b64b4385f123663873756336c085744376d015ff328bb1d901598f63c44152":
            # ref: https://huggingface.co/answerdotai/ModernBERT-base
            res = "modern-bert"
        if chkhsh == "49fc0303c9e0d2c2c565c510f64b2d9b271276acdcdadff733249eda9f7d59df":
            # ref: https://huggingface.co/arcee-ai/Trinity-Tokenizer
            res = "afmoe"
        if chkhsh == "9b1be57e70d20d9501b2b3186e792d81181ae36ada3903c26f9fea418cf87206":
            # ref: https://huggingface.co/inclusionAI/Ling-mini-base-2.0
            res = "bailingmoe2"
        if chkhsh == "53e325976a6e142379c19b09afcae354f2f496f147afa8f9e189a33fe4e3024e":
            # ref: https://huggingface.co/ibm-granite/granite-docling-258M
            res = "granite-docling"
        if chkhsh == "f4f37b6c8eb9ea29b3eac6bb8c8487c5ab7885f8d8022e67edc1c68ce8403e95":
            # ref: https://huggingface.co/MiniMaxAI/MiniMax-M2
            res = "minimax-m2"
        if chkhsh == "a77756c3cc91392f442c5b99e414be8020d53ae31460de90754b4fcf5cc84a2d":
            # ref: https://huggingface.co/MiniMaxAI/MiniMax-M2.5
            res = "minimax-m2"
        if chkhsh == "4a2e2abae11ca2b86d570fc5b44be4d5eb5e72cc8f22dd136a94b37da83ab665":
            # ref: https://huggingface.co/KORMo-Team/KORMo-tokenizer
            res = "kormo"
        if chkhsh == "9d70134b369a70e5735009b6de918f7581b5211f7c074d1f89f753aea8248af1":
            # ref: https://huggingface.co/tencent/Youtu-LLM-2B
            res = "youtu"
        if chkhsh == "16389f0a1f51ee53e562ffd51c371dc508639ab0e4261502071836e50e223e91":
            # ref: https://huggingface.co/upstage/Solar-Open-100B
            res = "solar-open"
        if chkhsh == "6c81ce329e0802883b22eabab0d3fa48357337ef1ecb45443828bf1f6254833f":
            # ref: https://huggingface.co/LGAI-EXAONE/K-EXAONE-236B-A23B
            res = "exaone-moe"
        if chkhsh == "d30d75d9059f1aa2c19359de71047b3ae408c70875e8a3ccf8c5fba56c9d8af4":
            # ref: https://huggingface.co/Qwen/Qwen3.5-9B-Instruct
            res = "qwen35"
        if chkhsh == "b4b8ca1f9769494fbd956ebc4c249de6131fb277a4a3345a7a92c7dd7a55808d":
            # ref: https://huggingface.co/jdopensource/JoyAI-LLM-Flash
            res = "joyai-llm"
        if chkhsh == "e4d54df1ebc1f2b91acd986c5b51aa50837d5faf7c7398e73c1f9e9ee5d19869":
            # ref: https://huggingface.co/kakaocorp/kanana-2-30b-a3b-instruct-2601
            res = "kanana2"
        if chkhsh == "862f827721df956049dff5ca81a57f29e575280bc622e290d3bf4e35eca29015":
            # ref: https://huggingface.co/codefuse-ai/F2LLM-v2-4B
            res = "f2llmv2"
        if chkhsh == "62f6fb0a6fd5098caeabb19b07a5c1099cafc8b9c40eab6ea89ece4ec02fbc57":
            # ref: https://huggingface.co/sarvamai/sarvam-30b
            res = "sarvam-moe"

        if res is None:
            logger.warning("\n")
            logger.warning("**************************************************************************************")
            logger.warning("** WARNING: The BPE pre-tokenizer was not recognized!")
            logger.warning("**          There are 2 possible reasons for this:")
            logger.warning("**          - the model has not been added to convert_hf_to_gguf_update.py yet")
            logger.warning("**          - the pre-tokenization config has changed upstream")
            logger.warning("**          Check your model files and convert_hf_to_gguf_update.py and update them accordingly.")
            logger.warning("** ref:     https://github.com/ggml-org/llama.cpp/pull/6920")
            logger.warning("**")
            logger.warning(f"** chkhsh:  {chkhsh}")
            logger.warning("**************************************************************************************")
            logger.warning("\n")
            raise NotImplementedError("BPE pre-tokenizer was not recognized - update get_vocab_base_pre()")

        logger.debug(f"tokenizer.ggml.pre: {repr(res)}")
        logger.debug(f"chkhsh: {chkhsh}")

        return res
        # Marker: End get_vocab_base_pre

    def _set_vocab_none(self) -> None:
        self.gguf_writer.add_tokenizer_model("none")

    def _set_vocab_gpt2(self) -> None:
        tokens, toktypes, tokpre = self.get_vocab_base()
        self.gguf_writer.add_tokenizer_model("gpt2")
        self.gguf_writer.add_tokenizer_pre(tokpre)
        self.gguf_writer.add_token_list(tokens)
        self.gguf_writer.add_token_types(toktypes)

        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
        special_vocab.add_to_gguf(self.gguf_writer)

    def _set_vocab_qwen(self):
        dir_model = self.dir_model
        hparams = self.hparams
        tokens: list[str] = []
        toktypes: list[int] = []

        from transformers import AutoTokenizer
        tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
        vocab_size = hparams["vocab_size"]
        assert max(tokenizer.get_vocab().values()) < vocab_size  # ty: ignore[unresolved-attribute]

        tokpre = self.get_vocab_base_pre(tokenizer)

        merges = []
        vocab = {}
        mergeable_ranks = tokenizer.mergeable_ranks  # ty: ignore[unresolved-attribute]
        for token, rank in mergeable_ranks.items():
            vocab[QwenModel.token_bytes_to_string(token)] = rank
            if len(token) == 1:
                continue
            merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank)
            assert len(merged) == 2
            merges.append(' '.join(map(QwenModel.token_bytes_to_string, merged)))

        # for this kind of tokenizer, added_vocab is not a subset of vocab, so they need to be combined
        added_vocab = tokenizer.special_tokens  # ty: ignore[unresolved-attribute]
        reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **added_vocab}.items()}

        for i in range(vocab_size):
            if i not in reverse_vocab:
                tokens.append(f"[PAD{i}]")
                toktypes.append(gguf.TokenType.UNUSED)
            elif reverse_vocab[i] in added_vocab:
                tokens.append(reverse_vocab[i])
                toktypes.append(gguf.TokenType.CONTROL)
            else:
                tokens.append(reverse_vocab[i])
                toktypes.append(gguf.TokenType.NORMAL)

        self.gguf_writer.add_tokenizer_model("gpt2")
        self.gguf_writer.add_tokenizer_pre(tokpre)
        self.gguf_writer.add_token_list(tokens)
        self.gguf_writer.add_token_types(toktypes)

        special_vocab = gguf.SpecialVocab(dir_model, load_merges=False)
        special_vocab.merges = merges
        # only add special tokens when they were not already loaded from config.json
        if len(special_vocab.special_token_ids) == 0:
            special_vocab._set_special_token("bos", tokenizer.special_tokens["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
            special_vocab._set_special_token("eos", tokenizer.special_tokens["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
        # this one is usually not in config.json anyway
        special_vocab._set_special_token("unk", tokenizer.special_tokens["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
        special_vocab.add_to_gguf(self.gguf_writer)

    def _set_vocab_sentencepiece(self, add_to_gguf=True):
        tokens, scores, toktypes = self._create_vocab_sentencepiece()

        self.gguf_writer.add_tokenizer_model("llama")
        self.gguf_writer.add_tokenizer_pre("default")
        self.gguf_writer.add_token_list(tokens)
        self.gguf_writer.add_token_scores(scores)
        self.gguf_writer.add_token_types(toktypes)

        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
        special_vocab.add_to_gguf(self.gguf_writer)

    def _create_vocab_sentencepiece(self):
        from sentencepiece import SentencePieceProcessor

        tokenizer_path = self.dir_model / 'tokenizer.model'

        if not tokenizer_path.is_file():
            raise FileNotFoundError(f"File not found: {tokenizer_path}")

        tokenizer = SentencePieceProcessor()
        tokenizer.LoadFromFile(str(tokenizer_path))

        vocab_size = self.find_hparam([
            "vocab_size_per_layer_input", # gemma3n
            "vocab_size",
        ], optional=True) or tokenizer.vocab_size()

        tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
        scores: list[float] = [-10000.0] * vocab_size
        toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size

        for token_id in range(tokenizer.vocab_size()):
            if token_id >= vocab_size:
                logger.warning(f'ignore tokens from {token_id}: id is out of range, max={vocab_size - 1}')
                break

            piece = tokenizer.IdToPiece(token_id)
            text = piece.encode("utf-8")
            score = tokenizer.GetScore(token_id)

            toktype = SentencePieceTokenTypes.NORMAL
            if tokenizer.IsUnknown(token_id):
                toktype = SentencePieceTokenTypes.UNKNOWN
            elif tokenizer.IsControl(token_id):
                toktype = SentencePieceTokenTypes.CONTROL
            elif tokenizer.IsUnused(token_id):
                toktype = SentencePieceTokenTypes.UNUSED
            elif tokenizer.IsByte(token_id):
                toktype = SentencePieceTokenTypes.BYTE

            tokens[token_id] = text
            scores[token_id] = score
            toktypes[token_id] = toktype

        added_tokens_file = self.dir_model / 'added_tokens.json'
        if added_tokens_file.is_file():
            with open(added_tokens_file, "r", encoding="utf-8") as f:
                added_tokens_json = json.load(f)
                for key in added_tokens_json:
                    token_id = added_tokens_json[key]
                    if token_id >= vocab_size:
                        logger.warning(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}')
                        continue

                    tokens[token_id] = key.encode("utf-8")
                    scores[token_id] = -1000.0
                    toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED

        tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
        if tokenizer_config_file.is_file():
            with open(tokenizer_config_file, "r", encoding="utf-8") as f:
                tokenizer_config_json = json.load(f)
                added_tokens_decoder = tokenizer_config_json.get("added_tokens_decoder", {})
                for token_id, token_data in added_tokens_decoder.items():
                    token_id = int(token_id)
                    token: str = token_data["content"]
                    if token_id >= vocab_size:
                        logger.warning(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}')
                        continue
                    if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
                        if tokens[token_id] != token.encode("utf-8"):
                            logger.warning(f'replacing token {token_id}: {tokens[token_id].decode("utf-8")!r} -> {token!r}')
                    if token_data.get("special") or self.does_token_look_special(token):
                        toktypes[token_id] = SentencePieceTokenTypes.CONTROL
                    else:
                        token = token.replace(b"\xe2\x96\x81".decode("utf-8"), " ")  # pre-normalize user-defined spaces
                        toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED

                    scores[token_id] = -1000.0
                    tokens[token_id] = token.encode("utf-8")

        if vocab_size > len(tokens):
            pad_count = vocab_size - len(tokens)
            logger.debug(f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]")
            for i in range(1, pad_count + 1):
                tokens.append(bytes(f"[PAD{i}]", encoding="utf-8"))
                scores.append(-1000.0)
                toktypes.append(SentencePieceTokenTypes.UNUSED)

        return tokens, scores, toktypes

    def _set_vocab_llama_hf(self):
        vocab = gguf.LlamaHfVocab(self.dir_model)
        tokens = []
        scores = []
        toktypes = []

        for text, score, toktype in vocab.all_tokens():
            tokens.append(text)
            scores.append(score)
            toktypes.append(toktype)

        assert len(tokens) == vocab.vocab_size

        self.gguf_writer.add_tokenizer_model("llama")
        self.gguf_writer.add_tokenizer_pre("default")
        self.gguf_writer.add_token_list(tokens)
        self.gguf_writer.add_token_scores(scores)
        self.gguf_writer.add_token_types(toktypes)

        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
        special_vocab.add_to_gguf(self.gguf_writer)

    def _set_vocab_rwkv_world(self):
        assert (self.dir_model / "rwkv_vocab_v20230424.txt").is_file()
        vocab_size = self.hparams.get("vocab_size", 65536)

        tokens: list[bytes] = ['<s>'.encode("utf-8")]
        toktypes: list[int] = [gguf.TokenType.CONTROL]

        with open(self.dir_model / "rwkv_vocab_v20230424.txt", "r", encoding="utf-8") as f:
            lines = f.readlines()
            for line in lines:
                parts = line.split(' ')
                assert len(parts) >= 3
                token, token_len = ast.literal_eval(' '.join(parts[1:-1])), int(parts[-1])
                token = token.encode("utf-8") if isinstance(token, str) else token
                assert isinstance(token, bytes)
                assert len(token) == token_len
                token_text: str = repr(token)[2:-1]  # "b'\xff'" -> "\xff"
                tokens.append(token_text.encode("utf-8"))
                toktypes.append(gguf.TokenType.NORMAL)
        remainder = vocab_size - len(tokens)
        assert remainder >= 0
        for i in range(len(tokens), vocab_size):
            tokens.append(f"[PAD{i}]".encode("utf-8"))
            toktypes.append(gguf.TokenType.UNUSED)

        self.gguf_writer.add_tokenizer_model("rwkv")
        self.gguf_writer.add_token_list(tokens)
        self.gguf_writer.add_token_types(toktypes)
        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False)
        if special_vocab.chat_template is None:
            template_path = Path(__file__).parent / "models" / "templates" / "llama-cpp-rwkv-world.jinja"
            if template_path.is_file():
                with open(template_path, "r", encoding="utf-8") as f:
                    template = f.read()
            else:
                template = "rwkv-world"
            special_vocab.chat_template = template
        # hack: Add '\n\n' as the EOT token to make it chat normally
        special_vocab._set_special_token("eot", 261)
        # hack: Override these as they have already been set (incorrectly)
        special_vocab.special_token_ids["bos"] = 0
        special_vocab.special_token_ids["eos"] = 0

        special_vocab.add_to_gguf(self.gguf_writer)

    def _set_vocab_builtin(self, model_name: Literal["gpt-neox", "llama-spm"], vocab_size: int):
        tokenizer_path = Path(sys.path[0]) / "models" / f"ggml-vocab-{model_name}.gguf"
        logger.warning(f"Using tokenizer from '{os.path.relpath(tokenizer_path, os.getcwd())}'")
        vocab_reader = gguf.GGUFReader(tokenizer_path, "r")

        default_pre = "mpt" if model_name == "gpt-neox" else "default"

        field = vocab_reader.get_field(gguf.Keys.Tokenizer.MODEL)
        assert field  # tokenizer model
        self.gguf_writer.add_tokenizer_model(bytes(field.parts[-1]).decode("utf-8"))

        field = vocab_reader.get_field(gguf.Keys.Tokenizer.PRE)
        self.gguf_writer.add_tokenizer_pre(bytes(field.parts[-1]).decode("utf-8") if field else default_pre)

        field = vocab_reader.get_field(gguf.Keys.Tokenizer.LIST)
        assert field  # token list
        self.gguf_writer.add_token_list([bytes(field.parts[i]) for i in field.data][:vocab_size])

        if model_name == "llama-spm":
            field = vocab_reader.get_field(gguf.Keys.Tokenizer.SCORES)
            assert field  # token scores
            self.gguf_writer.add_token_scores([field.parts[i].tolist()[0] for i in field.data][:vocab_size])

        field = vocab_reader.get_field(gguf.Keys.Tokenizer.TOKEN_TYPE)
        assert field  # token types
        self.gguf_writer.add_token_types([field.parts[i].tolist()[0] for i in field.data][:vocab_size])

        if model_name != "llama-spm":
            field = vocab_reader.get_field(gguf.Keys.Tokenizer.MERGES)
            assert field  # token merges
            self.gguf_writer.add_token_merges([bytes(field.parts[i]) for i in field.data])

        if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.BOS_ID)) is not None:
            self.gguf_writer.add_bos_token_id(field.parts[-1].tolist()[0])
        if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.EOS_ID)) is not None:
            self.gguf_writer.add_eos_token_id(field.parts[-1].tolist()[0])
        if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.UNK_ID)) is not None:
            self.gguf_writer.add_unk_token_id(field.parts[-1].tolist()[0])
        if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.PAD_ID)) is not None:
            self.gguf_writer.add_pad_token_id(field.parts[-1].tolist()[0])
        if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.ADD_BOS)) is not None:
            self.gguf_writer.add_add_bos_token(field.parts[-1].tolist()[0])
        if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.ADD_EOS)) is not None:
            self.gguf_writer.add_add_eos_token(field.parts[-1].tolist()[0])

    def _try_set_pooling_type(self) -> None:
        # get pooling path
        pooling_path = None
        module_path = self.dir_model / "modules.json"
        if module_path.is_file():
            with open(module_path, encoding="utf-8") as f:
                modules = json.load(f)
            for mod in modules:
                if mod["type"].endswith("Pooling"):
                    pooling_path = mod["path"]
                    break

        mode_mapping = {
            "mean": gguf.PoolingType.MEAN,
            "cls": gguf.PoolingType.CLS,
            "lasttoken": gguf.PoolingType.LAST,
        }

        # get pooling type
        if pooling_path is not None:
            with open(self.dir_model / pooling_path / "config.json", encoding="utf-8") as f:
                pooling = json.load(f)
            if pooling.get("pooling_mode_mean_tokens"):
                pooling_type = gguf.PoolingType.MEAN
            elif pooling.get("pooling_mode_cls_token"):
                pooling_type = gguf.PoolingType.CLS
            elif pooling.get("pooling_mode_lasttoken"):
                pooling_type = gguf.PoolingType.LAST
            elif (pooling_mode := pooling.get("pooling_mode")) in mode_mapping:
                pooling_type = mode_mapping[pooling_mode]
            else:
                raise NotImplementedError("Only MEAN, CLS, and LAST pooling types supported")
            self.gguf_writer.add_pooling_type(pooling_type)

    def _set_vocab_glmedge(self):
        from transformers import AutoTokenizer
        tokenizer = AutoTokenizer.from_pretrained(self.dir_model)
        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
        tokens, toktypes, tokpre = self.get_vocab_base()
        self.gguf_writer.add_tokenizer_model("gpt2")
        self.gguf_writer.add_tokenizer_pre(tokpre)
        self.gguf_writer.add_token_list(tokens)
        self.gguf_writer.add_token_types(toktypes)
        special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
        special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"])  # ty: ignore[unresolved-attribute]
        special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
        special_vocab._set_special_token("bos", tokenizer.get_added_vocab()["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
        special_vocab.add_to_gguf(self.gguf_writer)

    def _set_vocab_glm(self):
        from transformers import AutoTokenizer
        tokenizer = AutoTokenizer.from_pretrained(self.dir_model)
        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
        tokens, toktypes, tokpre = self.get_vocab_base()
        self.gguf_writer.add_tokenizer_model("gpt2")
        self.gguf_writer.add_tokenizer_pre(tokpre)
        self.gguf_writer.add_token_list(tokens)
        self.gguf_writer.add_token_types(toktypes)
        # Special tokens
        # Note: Using <|endoftext|> (151329) for eot causes endless generation
        special_vocab._set_special_token("bos", tokenizer.get_added_vocab()["[gMASK]"])  # ty: ignore[unresolved-attribute]  # 151331
        special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"])  # ty: ignore[unresolved-attribute]  # 151336
        special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<|endoftext|>"])  # ty: ignore[unresolved-attribute]  # 151329
        special_vocab._set_special_token("eom", tokenizer.get_added_vocab()["<|observation|>"])  # ty: ignore[unresolved-attribute]  # 151338
        special_vocab.add_to_gguf(self.gguf_writer)

    def _set_vocab_interns1(self):
        tokens: list[str] = []
        toktypes: list[int] = []

        from transformers import AutoTokenizer
        tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
        vocab = getattr(tokenizer, 'vocab', tokenizer.get_vocab())  # ty: ignore[unresolved-attribute]
        vocab_size = self.hparams.get("vocab_size", len(vocab))
        assert max(vocab.values()) < vocab_size

        tokpre = self.get_vocab_base_pre(tokenizer)

        reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in vocab.items()}
        added_vocab = tokenizer.get_added_vocab()  # ty: ignore[unresolved-attribute]

        added_tokens_decoder = tokenizer.added_tokens_decoder  # ty: ignore[unresolved-attribute]

        for i in range(vocab_size):
            if i not in reverse_vocab:
                tokens.append(f"[PAD{i}]")
                toktypes.append(gguf.TokenType.UNUSED)
            else:
                token: str = reverse_vocab[i]
                if token in added_vocab:
                    # The tokenizer in llama.cpp assumes the CONTROL and USER_DEFINED tokens are pre-normalized.
                    # To avoid unexpected issues - we make sure to normalize non-normalized tokens
                    if not added_tokens_decoder[i].normalized:
                        previous_token = token
                        token = tokenizer.decode(tokenizer.encode(token, add_special_tokens=False))  # ty: ignore[unresolved-attribute, invalid-assignment]
                        if previous_token != token:
                            logger.info(f"{repr(previous_token)} is encoded and decoded back to {repr(token)} using AutoTokenizer")

                    if added_tokens_decoder[i].special or self.does_token_look_special(token):
                        toktypes.append(gguf.TokenType.CONTROL)
                    else:
                        toktypes.append(gguf.TokenType.USER_DEFINED)
                else:
                    toktypes.append(gguf.TokenType.NORMAL)
                tokens.append(token)

        self.gguf_writer.add_tokenizer_model("gpt2")
        self.gguf_writer.add_tokenizer_pre(tokpre)
        self.gguf_writer.add_token_list(tokens)
        self.gguf_writer.add_token_types(toktypes)

        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
        special_vocab._set_special_token("bos", 151643)
        special_vocab.add_to_gguf(self.gguf_writer)

    def _set_vocab_mistral(self):
        if not _mistral_common_installed:
            raise ImportError(_mistral_import_error_msg)

        vocab = MistralVocab(self.dir_model)
        logger.info(
            f"Converting tokenizer {vocab.tokenizer_type} of size {vocab.vocab_size}."
        )

        self.gguf_writer.add_tokenizer_model(vocab.gguf_tokenizer_model)

        tokens = []
        scores = []
        toktypes = []

        for text, score, toktype in vocab.all_tokens():
            tokens.append(text)
            scores.append(score)
            toktypes.append(toktype)

        assert len(tokens) == vocab.vocab_size, (
            f"token count ({len(tokens)}) != vocab size ({vocab.vocab_size})"
        )

        if vocab.tokenizer_type == MistralTokenizerType.tekken:
            self.gguf_writer.add_tokenizer_pre("tekken")
            self.gguf_writer.add_token_merges(
                vocab.extract_vocab_merges_from_model()
            )

        logger.info(
            f"Setting bos, eos, unk and pad token IDs to {vocab.bos_id}, {vocab.eos_id}, {vocab.unk_id}, {vocab.pad_id}."
        )

        self.gguf_writer.add_bos_token_id(vocab.bos_id)
        self.gguf_writer.add_eos_token_id(vocab.eos_id)
        self.gguf_writer.add_unk_token_id(vocab.unk_id)
        self.gguf_writer.add_pad_token_id(vocab.pad_id)

        self.gguf_writer.add_token_list(tokens)
        self.gguf_writer.add_token_scores(scores)
        self.gguf_writer.add_token_types(toktypes)
        self.gguf_writer.add_vocab_size(vocab.vocab_size)

        self.gguf_writer.add_add_bos_token(True)
        self.gguf_writer.add_add_eos_token(False)

        local_template_file_path = self.dir_model / "chat_template.jinja"

        if self.is_mistral_format and local_template_file_path.is_file():
            # Ministral-3 and other new Mistral models come with chat templates.
            # ref: https://huggingface.co/mistralai/Ministral-3-14B-Instruct-2512/tree/main
            logger.info("Using an existing Mistral local chat template.")

            with open(local_template_file_path, "r", encoding="utf-8") as f:
                template = f.read()
        elif not self.is_mistral_format or not self.disable_mistral_community_chat_template:
            template_dir = Path(__file__).parent / "models/templates/"

            # Log only for Mistral format that the official tokenization and detokenization is via `mistral-common`.
            if self.is_mistral_format:
                logger.info(
                    "Using a Mistral community chat template. These templates can be subject to errors in early days or weeks after a release. "
                    "Mistral recommends to use `mistral-common` to perform tokenization and detokenization."
                )
            template = MistralModel.get_community_chat_template(vocab, template_dir, self.is_mistral_format)
        else:
            logger.info("Not using a Mistral local or community chat template. Ensure to perform the tokenization and detokenization via `mistral-common`.")
            template = None

        if template is not None:
            self.gguf_writer.add_chat_template(template)

    def _set_vocab_plamo(self):
        # PLaMo models use a custom tokenizer with a .jsonl file
        tokenizer_jsonl_path = self.dir_model / "tokenizer.jsonl"
        tokenizer_config_path = self.dir_model / "tokenizer_config.json"

        if not tokenizer_jsonl_path.is_file():
            raise FileNotFoundError(f"PLaMo tokenizer file not found: {tokenizer_jsonl_path}")

        # Load tokenizer config
        with open(tokenizer_config_path, "r", encoding="utf-8") as f:
            tokenizer_config = json.load(f)

        # Load tokens from JSONL file (actually a list format)
        tokens = []
        scores = []
        toktypes = []

        with open(tokenizer_jsonl_path, "r", encoding="utf-8") as f:
            for line_num, line in enumerate(f):
                if line.strip():
                    token_data = json.loads(line)
                    # Format: [token, score, type, ?, ?, ?, ?]
                    token = token_data[0].encode("utf-8")
                    score = float(token_data[1])
                    token_type_str = token_data[2] if len(token_data) > 2 else "NORMAL"

                    tokens.append(token)
                    scores.append(score)

                    if token_type_str == "UNKNOWN":
                        toktypes.append(gguf.TokenType.UNKNOWN)
                    elif token_type_str == "CONTROL":
                        toktypes.append(gguf.TokenType.CONTROL)
                    elif token_type_str == "BYTE":
                        toktypes.append(gguf.TokenType.BYTE)
                    else:
                        token_str = token_data[0]
                        if token_str.startswith("<|plamo:") and token_str.endswith("|>"):
                            toktypes.append(gguf.TokenType.CONTROL)
                        else:
                            toktypes.append(gguf.TokenType.NORMAL)

        vocab_size = self.hparams["vocab_size"]
        if vocab_size > len(tokens):
            pad_count = vocab_size - len(tokens)
            logger.debug(f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]")
            for i in range(1, pad_count + 1):
                tokens.append(bytes(f"[PAD{i}]", encoding="utf-8"))
                scores.append(-1000.0)
                toktypes.append(gguf.TokenType.UNUSED)

        self.gguf_writer.add_tokenizer_model("plamo2")
        self.gguf_writer.add_tokenizer_pre("default")
        self.gguf_writer.add_token_list(tokens)
        self.gguf_writer.add_token_scores(scores)
        self.gguf_writer.add_token_types(toktypes)

        if "bos_token" in tokenizer_config and tokenizer_config["bos_token"] is not None:
            token_id = tokens.index(tokenizer_config["bos_token"].encode("utf-8"))
            self.gguf_writer.add_bos_token_id(token_id)
        if "eos_token" in tokenizer_config and tokenizer_config["eos_token"] is not None:
            token_id = tokens.index(tokenizer_config["eos_token"].encode("utf-8"))
            self.gguf_writer.add_eos_token_id(token_id)
        if "pad_token" in tokenizer_config and tokenizer_config["pad_token"] is not None:
            token_id = tokens.index(tokenizer_config["pad_token"].encode("utf-8"))
            self.gguf_writer.add_pad_token_id(token_id)
        if "sep_token" in tokenizer_config and tokenizer_config["sep_token"] is not None:
            token_id = tokens.index(tokenizer_config["sep_token"].encode("utf-8"))
            self.gguf_writer.add_sep_token_id(token_id)
        if "unk_token" in tokenizer_config and tokenizer_config["unk_token"] is not None:
            token_id = tokens.index(tokenizer_config["unk_token"].encode("utf-8"))
            self.gguf_writer.add_unk_token_id(token_id)

        # Add <|plamo:op|> as EOT to ensure appropriate end of generation
        self.gguf_writer.add_eot_token_id(4)

        self.gguf_writer.add_add_space_prefix(False)


class MmprojModel(ModelBase):
    model_type = ModelType.MMPROJ
    model_arch = gguf.MODEL_ARCH.MMPROJ
    preprocessor_config: dict[str, Any]
    global_config: dict[str, Any]

    n_block_keys = ["n_layers", "num_hidden_layers", "n_layer", "num_layers", "depth", "layers", "encoder_layers", "vt_num_hidden_layers"]

    has_vision_encoder: bool = True # by default
    has_audio_encoder: bool = False

    # for models having multiple encoders, we need to separate their hparams
    hparams_vision: dict[str, Any] | None = None
    hparams_audio: dict[str, Any] | None = None

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

        if self.model_arch != gguf.MODEL_ARCH.MMPROJ:
            raise TypeError("MmprojModel must be subclassed with model_arch = gguf.MODEL_ARCH.MMPROJ")

        # get n_embd of the text model
        if not self.is_mistral_format:
            if "text_config" not in self.hparams:
                self.hparams["text_config"] = {}
            if "audio_config" not in self.hparams:
                self.hparams["audio_config"] = {}
            text_config = {**self.hparams, **self.hparams["text_config"]}
            self.n_embd_text = text_config.get("hidden_size", text_config.get("n_embd", 0))
        else:
            text_config = {
                k: v for k, v in self.hparams.items() if k not in ["vision_encoder", "audio_encoder"]
            }
            # mistral native params.json: "dim" is the text hidden size ("hidden_dim" is the FFN intermediate size)
            self.n_embd_text = text_config.get("dim", 0)

        assert self.n_embd_text > 0, "n_embd not found in hparams"

        # move vision config to the top level, while preserving the original hparams in global_config
        import copy
        self.global_config = copy.deepcopy(self.hparams)
        self.hparams_vision = self.get_vision_config()
        self.hparams_audio = self.get_audio_config()

        if self.hparams_vision is None and self.hparams_audio is None:
            raise ValueError("vision_config / audio_config not found in hparams")

        # for compat with vision-only models
        self.hparams = self.hparams_vision or self.hparams_audio or self.hparams

        # TODO @ngxson : this is a hack to support both vision and audio encoders
        have_multiple_encoders = self.has_audio_encoder and self.has_vision_encoder
        self.block_count = 128 if have_multiple_encoders else self.find_hparam(self.n_block_keys, True)
        self.tensor_map = gguf.get_tensor_name_map(gguf.MODEL_ARCH.MMPROJ, self.block_count)

        # load preprocessor config
        self.preprocessor_config = {}

        # prefer preprocessor_config.json if possible
        preprocessor_config_path = self.dir_model / "preprocessor_config.json"
        if preprocessor_config_path.is_file():
            with open(preprocessor_config_path, "r", encoding="utf-8") as f:
                cfg = json.load(f)
                # move media_proc_cfg to root level for compat
                if "media_proc_cfg" in cfg:
                    cfg = {
                        **cfg,
                        **cfg["media_proc_cfg"],
                    }
                # merge configs
                self.preprocessor_config = {**self.preprocessor_config, **cfg}

        # prefer processor_config.json if possible
        processor_config_path = self.dir_model / "processor_config.json"
        if processor_config_path.is_file():
            with open(processor_config_path, "r", encoding="utf-8") as f:
                cfg = json.load(f)
                # move image_processor to root level for compat
                if "image_processor" in cfg:
                    cfg = {
                        **cfg,
                        **cfg["image_processor"],
                    }
                # merge configs
                self.preprocessor_config = {**self.preprocessor_config, **cfg}

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        # Skip non-multimodal tensors
        if "language_model." in name:
            return None

        return super().filter_tensors(item)

    def get_vision_config(self) -> dict[str, Any] | None:
        config_name = "vision_config" if not self.is_mistral_format else "vision_encoder"
        return self.global_config.get(config_name)

    def get_audio_config(self) -> dict[str, Any] | None:
        mm_config_key = "whisper_config" if "whisper_config" in self.hparams else "audio_config"
        return self.global_config.get(mm_config_key)

    def set_type(self):
        self.gguf_writer.add_type(gguf.GGUFType.MMPROJ)

    def prepare_metadata(self, vocab_only: bool):
        super().prepare_metadata(vocab_only=vocab_only)

        output_type: str = self.ftype.name.partition("_")[2]

        if self.fname_out.is_dir():
            fname_default: str = gguf.naming_convention(self.metadata.name, self.metadata.basename, self.metadata.finetune, self.metadata.version, size_label=None, output_type=output_type, model_type=None)
            self.fname_out = self.fname_out / f"mmproj-{fname_default}.gguf"
        else:
            self.fname_out = self.fname_out.parent / gguf.fill_templated_filename(self.fname_out.name, output_type)

    def set_gguf_parameters(self):
        self.gguf_writer.add_file_type(self.ftype)

        if self.has_vision_encoder:
            self.gguf_writer.add_clip_has_vision_encoder(True)
            self.gguf_writer.add_vision_projection_dim(self.n_embd_text)

            # vision config
            self.image_size = self.find_vparam(["image_size"])
            self.gguf_writer.add_vision_image_size(self.image_size)
            self.gguf_writer.add_vision_patch_size(self.find_vparam(["patch_size"]))
            self.gguf_writer.add_vision_embedding_length(self.find_vparam(["hidden_size", "width", "vt_hidden_size"]))
            self.gguf_writer.add_vision_feed_forward_length(self.find_vparam(["intermediate_size", "vt_intermediate_size"]))
            self.gguf_writer.add_vision_block_count(self.find_vparam(self.n_block_keys))
            self.gguf_writer.add_vision_head_count(self.find_vparam(["num_attention_heads", "num_heads", "heads", "vt_num_attention_heads"]))

            # preprocessor config
            image_mean = _MISTRAL_COMMON_DATASET_MEAN if self.is_mistral_format else self.preprocessor_config["image_mean"]
            image_std = _MISTRAL_COMMON_DATASET_STD if self.is_mistral_format else self.preprocessor_config["image_std"]

            self.gguf_writer.add_vision_image_mean(image_mean)
            self.gguf_writer.add_vision_image_std(image_std)

        if self.has_audio_encoder:
            self.gguf_writer.add_clip_has_audio_encoder(True)
            self.gguf_writer.add_audio_projection_dim(self.n_embd_text)

            # audio config
            self.gguf_writer.add_audio_embedding_length(self.find_aparam(["hidden_size"]))
            self.gguf_writer.add_audio_feed_forward_length(self.find_aparam(["intermediate_size"]))
            self.gguf_writer.add_audio_block_count(self.find_aparam(self.n_block_keys))
            self.gguf_writer.add_audio_head_count(self.find_aparam(["num_attention_heads"]))

        if not self.has_vision_encoder and not self.has_audio_encoder:
            raise ValueError("MmprojModel must have either vision or audio encoder")

    def write_vocab(self):
        raise ValueError("MmprojModel does not support vocab writing")

    def find_vparam(self, keys: Iterable[str], optional: bool = False) -> Any:
        assert self.hparams_vision is not None
        return self._find_param(self.hparams_vision, keys, optional)

    def find_aparam(self, keys: Iterable[str], optional: bool = False) -> Any:
        assert self.hparams_audio is not None
        return self._find_param(self.hparams_audio, keys, optional)

    def _find_param(self, obj: dict[str, Any], keys: Iterable[str], optional: bool = False) -> Any:
        key = next((k for k in keys if k in obj), None)
        if key is not None:
            return obj[key]
        if optional:
            return None
        raise KeyError(f"could not find any of: {keys}")

    def tensor_force_quant(self, name, new_name, bid, n_dims):
        del bid, name, n_dims  # unused
        if ".patch_embd.weight" in new_name or ".patch_merger.weight" in new_name:
            return gguf.GGMLQuantizationType.F16 if self.ftype == gguf.LlamaFileType.MOSTLY_F16 else gguf.GGMLQuantizationType.F32
        return False


@ModelBase.register("GPTNeoXForCausalLM")
class GPTNeoXModel(TextModel):
    model_arch = gguf.MODEL_ARCH.GPTNEOX

    def set_gguf_parameters(self):
        self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
        self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
        self.gguf_writer.add_block_count(self.block_count)
        self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
        self.gguf_writer.add_rope_dimension_count(
            int(self.hparams["rotary_pct"] * (self.hparams["hidden_size"] // self.hparams["num_attention_heads"])),
        )
        self.gguf_writer.add_head_count(self.hparams["num_attention_heads"])
        self.gguf_writer.add_parallel_residual(self.hparams.get("use_parallel_residual", True))
        self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"])

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
        n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
        assert n_head is not None
        assert n_embed is not None

        if re.match(r"gpt_neox\.layers\.\d+\.attention\.query_key_value\.weight", name):
            # Map bloom-style qkv_linear to gpt-style qkv_linear
            # bloom: https://github.com/huggingface/transformers/blob/main/src/transformers/models/bloom/modeling_bloom.py#L238-L252  # noqa
            # gpt-2: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py#L312  # noqa
            qkv_weights = data_torch.reshape((n_head, 3, n_embed // n_head, n_embed))
            data_torch = torch.cat(
                (
                    qkv_weights[:, 0, :, :].reshape((-1, n_embed)),
                    qkv_weights[:, 1, :, :].reshape((-1, n_embed)),
                    qkv_weights[:, 2, :, :].reshape((-1, n_embed)),
                ),
                dim=0,
            )
            logger.info("re-format attention.linear_qkv.weight")
        elif re.match(r"gpt_neox\.layers\.\d+\.attention\.query_key_value\.bias", name):
            qkv_bias = data_torch.reshape((n_head, 3, n_embed // n_head))
            data_torch = torch.cat(
                (
                    qkv_bias[:, 0, :].reshape((n_embed,)),
                    qkv_bias[:, 1, :].reshape((n_embed,)),
                    qkv_bias[:, 2, :].reshape((n_embed,)),
                ),
                dim=0,
            )
            logger.info("re-format attention.linear_qkv.bias")

        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("BloomForCausalLM", "BloomModel")
class BloomModel(TextModel):
    model_arch = gguf.MODEL_ARCH.BLOOM

    def set_gguf_parameters(self):
        n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
        n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
        assert n_head is not None
        assert n_embed is not None
        self.gguf_writer.add_context_length(self.hparams.get("seq_length", n_embed))
        self.gguf_writer.add_embedding_length(n_embed)
        self.gguf_writer.add_feed_forward_length(4 * n_embed)
        self.gguf_writer.add_block_count(self.block_count)
        self.gguf_writer.add_head_count(n_head)
        self.gguf_writer.add_head_count_kv(n_head)
        self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
        self.gguf_writer.add_file_type(self.ftype)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
        n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
        assert n_head is not None
        assert n_embed is not None

        name = re.sub(r'transformer\.', '', name)

        if re.match(r"h\.\d+\.self_attention\.query_key_value\.weight", name):
            # Map bloom-style qkv_linear to gpt-style qkv_linear
            # bloom: https://github.com/huggingface/transformers/blob/main/src/transformers/models/bloom/modeling_bloom.py#L238-L252  # noqa
            # gpt-2: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py#L312  # noqa
            qkv_weights = data_torch.reshape((n_head, 3, n_embed // n_head, n_embed))
            data_torch = torch.cat(
                (
                    qkv_weights[:, 0, :, :].reshape((-1, n_embed)),
                    qkv_weights[:, 1, :, :].reshape((-1, n_embed)),
                    qkv_weights[:, 2, :, :].reshape((-1, n_embed)),
                ),
                dim=0,
            )
            logger.info("re-format attention.linear_qkv.weight")
        elif re.match(r"h\.\d+\.self_attention\.query_key_value\.bias", name):
            qkv_bias = data_torch.reshape((n_head, 3, n_embed // n_head))
            data_torch = torch.cat(
                (
                    qkv_bias[:, 0, :].reshape((n_embed,)),
                    qkv_bias[:, 1, :].reshape((n_embed,)),
                    qkv_bias[:, 2, :].reshape((n_embed,)),
                ),
                dim=0,
            )
            logger.info("re-format attention.linear_qkv.bias")

        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("MPTForCausalLM")
class MPTModel(TextModel):
    model_arch = gguf.MODEL_ARCH.MPT

    def set_vocab(self):
        try:
            self._set_vocab_gpt2()
        except Exception:
            # Fallback for SEA-LION model
            self._set_vocab_sentencepiece()
            self.gguf_writer.add_add_bos_token(False)
            self.gguf_writer.add_pad_token_id(3)
            self.gguf_writer.add_eos_token_id(1)
            self.gguf_writer.add_unk_token_id(0)

    def set_gguf_parameters(self):
        self.gguf_writer.add_context_length(self.hparams["max_seq_len"])
        self.gguf_writer.add_embedding_length(self.hparams["d_model"])
        self.gguf_writer.add_block_count(self.block_count)
        self.gguf_writer.add_feed_forward_length(4 * self.hparams["d_model"])
        self.gguf_writer.add_head_count(self.hparams["n_heads"])
        if kv_n_heads := self.hparams["attn_config"].get("kv_n_heads"):
            self.gguf_writer.add_head_count_kv(kv_n_heads)
        self.gguf_writer.add_layer_norm_eps(1e-5)
        if self.hparams["attn_config"]["clip_qkv"] is not None:
            self.gguf_writer.add_clamp_kqv(self.hparams["attn_config"]["clip_qkv"])
        if self.hparams["attn_config"]["alibi"]:
            self.gguf_writer.add_max_alibi_bias(self.hparams["attn_config"]["alibi_bias_max"])
        else:
            self.gguf_writer.add_max_alibi_bias(0.0)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        if "scales" in name:
            new_name = self.map_tensor_name(name, try_suffixes=(".weight", ".bias", ".scales"))
            new_name = new_name.replace("scales", "act.scales")
        else:
            new_name = self.map_tensor_name(name, try_suffixes=(".weight", ".bias"))

        yield from super().modify_tensors(data_torch, new_name, bid)


@ModelBase.register("OrionForCausalLM")
class OrionModel(TextModel):
    model_arch = gguf.MODEL_ARCH.ORION

    def set_vocab(self):
        self._set_vocab_sentencepiece()

    def set_gguf_parameters(self):
        head_count = self.hparams["num_attention_heads"]
        head_count_kv = self.hparams.get("num_key_value_heads", head_count)

        ctx_length = 0
        if "max_sequence_length" in self.hparams:
            ctx_length = self.hparams["max_sequence_length"]
        elif "max_position_embeddings" in self.hparams:
            ctx_length = self.hparams["max_position_embeddings"]
        elif "model_max_length" in self.hparams:
            ctx_length = self.hparams["model_max_length"]
        else:
            raise ValueError("gguf: can not find ctx length parameter.")

        self.gguf_writer.add_file_type(self.ftype)
        self.gguf_writer.add_tensor_data_layout("Meta AI original pth")
        self.gguf_writer.add_context_length(ctx_length)
        self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
        self.gguf_writer.add_block_count(self.block_count)
        self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
        self.gguf_writer.add_head_count(head_count)
        self.gguf_writer.add_head_count_kv(head_count_kv)
        # note: config provides rms norm but it is actually layer norm
        # ref:  https://huggingface.co/OrionStarAI/Orion-14B-Chat/blob/276a17221ce42beb45f66fac657a41540e71f4f5/modeling_orion.py#L570-L571
        self.gguf_writer.add_layer_norm_eps(self.hparams["rms_norm_eps"])


@ModelBase.register("BaichuanForCausalLM", "BaiChuanForCausalLM")
class BaichuanModel(TextModel):
    model_arch = gguf.MODEL_ARCH.BAICHUAN

    def set_vocab(self):
        self._set_vocab_sentencepiece()

    def set_gguf_parameters(self):
        super().set_gguf_parameters()

        self.gguf_writer.add_tensor_data_layout("Meta AI original pth")
        self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        head_count = self.hparams["num_attention_heads"]
        head_count_kv = self.hparams.get("num_key_value_heads", head_count)

        if bid is not None and name == f"model.layers.{bid}.self_attn.W_pack.weight":
            logger.info(f"Unpacking and permuting layer {bid}")
            yield from [
                (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_Q, bid),
                    self._reverse_hf_permute_part(data_torch, 0, head_count, head_count)),
                (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_K, bid),
                    self._reverse_hf_permute_part(data_torch, 1, head_count, head_count_kv)),
                (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_V, bid),
                    self._reverse_hf_part(data_torch, 2)),
            ]
        else:
            yield from self.modify_tensors(data_torch, self.map_tensor_name(name), bid)

    def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor:
        if n_kv_head is not None and n_head != n_kv_head:
            n_head //= n_kv_head

        return (
            weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
            .swapaxes(1, 2)
            .reshape(weights.shape)
        )

    def _reverse_hf_permute_part(
        self, weights: Tensor, n_part: int, n_head: int, n_head_kv: int | None = None,
    ) -> Tensor:
        r = weights.shape[0] // 3
        return self._reverse_hf_permute(weights[r * n_part:r * n_part + r, ...], n_head, n_head_kv)

    def _reverse_hf_part(self, weights: Tensor, n_part: int) -> Tensor:
        r = weights.shape[0] // 3
        return weights[r * n_part:r * n_part + r, ...]


@ModelBase.register("XverseForCausalLM")
class XverseModel(TextModel):
    model_arch = gguf.MODEL_ARCH.XVERSE

    def set_vocab(self):
        assert (self.dir_model / "tokenizer.json").is_file()
        dir_model = self.dir_model
        hparams = self.hparams

        tokens: list[bytes] = []
        toktypes: list[int] = []

        from transformers import AutoTokenizer
        tokenizer = AutoTokenizer.from_pretrained(dir_model)
        vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))  # ty: ignore[unresolved-attribute]
        # Since we are checking the maximum index, we need to ensure it's strictly less than vocab_size,
        # because vocab_size is the count of items, and indexes start at 0.
        max_vocab_index = max(tokenizer.get_vocab().values())  # ty: ignore[unresolved-attribute]
        if max_vocab_index >= vocab_size:
            raise ValueError("Vocabulary size exceeds expected maximum size.")

        reverse_vocab: dict[int, str] = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()}  # ty: ignore[unresolved-attribute]
        added_vocab = tokenizer.get_added_vocab()  # ty: ignore[unresolved-attribute]

        for token_id in range(vocab_size):
            token_text = reverse_vocab[token_id].encode('utf-8')
            # replace "\x00" to string with length > 0
            if token_text == b"\x00":
                toktype = gguf.TokenType.BYTE  # special
                token_text = f"<{token_text}>".encode('utf-8')
            elif re.fullmatch(br"<0x[0-9A-Fa-f]{2}>", token_text):
                toktype = gguf.TokenType.BYTE  # special
            elif reverse_vocab[token_id] in added_vocab:
                if tokenizer.added_tokens_decoder[token_id].special:  # ty: ignore[unresolved-attribute]
                    toktype = gguf.TokenType.CONTROL
                else:
                    toktype = gguf.TokenType.USER_DEFINED
            else:
                toktype = gguf.TokenType.NORMAL

            tokens.append(token_text)
            toktypes.append(toktype)

        self.gguf_writer.add_tokenizer_model("llama")
        self.gguf_writer.add_tokenizer_pre("default")
        self.gguf_writer.add_token_list(tokens)
        self.gguf_writer.add_token_types(toktypes)

        special_vocab = gguf.SpecialVocab(dir_model, n_vocab=len(tokens))
        special_vocab.add_to_gguf(self.gguf_writer)

    def set_gguf_parameters(self):
        super().set_gguf_parameters()

        self.gguf_writer.add_tensor_data_layout("Meta AI original pth")
        self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        head_count = self.hparams["num_attention_heads"]
        head_count_kv = self.hparams.get("num_key_value_heads", head_count)

        # HF models permute some of the tensors, so we need to undo that
        if name.endswith("q_proj.weight"):
            data_torch = self._reverse_hf_permute(data_torch, head_count, head_count)
        if name.endswith("k_proj.weight"):
            data_torch = self._reverse_hf_permute(data_torch, head_count, head_count_kv)

        yield from super().modify_tensors(data_torch, name, bid)

    def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor:
        if n_kv_head is not None and n_head != n_kv_head:
            n_head //= n_kv_head

        return (
            weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
            .swapaxes(1, 2)
            .reshape(weights.shape)
        )


@ModelBase.register("FalconForCausalLM", "RWForCausalLM")
class FalconModel(TextModel):
    model_arch = gguf.MODEL_ARCH.FALCON

    def set_gguf_parameters(self):
        n_head = self.hparams.get("num_attention_heads")
        if n_head is None:
            n_head = self.hparams["n_head"]  # old name

        n_head_kv = self.hparams.get("num_kv_heads")
        if n_head_kv is None:
            n_head_kv = self.hparams.get("n_head_kv", 1)  # old name

        self.gguf_writer.add_context_length(2048)  # not in config.json
        self.gguf_writer.add_tensor_data_layout("jploski")  # qkv tensor transform
        self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
        self.gguf_writer.add_feed_forward_length(4 * self.hparams["hidden_size"])
        self.gguf_writer.add_block_count(self.block_count)
        self.gguf_writer.add_head_count(n_head)
        self.gguf_writer.add_head_count_kv(n_head_kv)
        self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
        self.gguf_writer.add_file_type(self.ftype)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        # QKV tensor transform
        # The original query_key_value tensor contains n_head_kv "kv groups",
        # each consisting of n_head/n_head_kv query weights followed by one key
        # and one value weight (shared by all query heads in the kv group).
        # This layout makes it a big pain to work with in GGML.
        # So we rearrange them here,, so that we have n_head query weights
        # followed by n_head_kv key weights followed by n_head_kv value weights,
        # in contiguous fashion.
        # ref: https://github.com/jploski/ggml/blob/falcon40b/examples/falcon/convert-hf-to-ggml.py

        if "query_key_value" in name:
            n_head = self.find_hparam(["num_attention_heads", "n_head"])
            n_head_kv = self.find_hparam(["num_kv_heads", "n_head_kv"], optional=True) or 1
            head_dim = self.hparams["hidden_size"] // n_head

            qkv = data_torch.view(n_head_kv, n_head // n_head_kv + 2, head_dim, head_dim * n_head)
            q = qkv[:, :-2].reshape(n_head * head_dim, head_dim * n_head)
            k = qkv[:, [-2]].reshape(n_head_kv * head_dim, head_dim * n_head)
            v = qkv[:, [-1]].reshape(n_head_kv * head_dim, head_dim * n_head)
            data_torch = torch.cat((q, k, v)).reshape_as(data_torch)

        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("GPTBigCodeForCausalLM")
class StarCoderModel(TextModel):
    model_arch = gguf.MODEL_ARCH.STARCODER

    def set_gguf_parameters(self):
        self.gguf_writer.add_context_length(self.hparams["n_positions"])
        self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
        self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"])
        self.gguf_writer.add_block_count(self.block_count)
        self.gguf_writer.add_head_count(self.hparams["n_head"])
        self.gguf_writer.add_head_count_kv(1)
        self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
        self.gguf_writer.add_file_type(self.ftype)


@ModelBase.register("GPTRefactForCausalLM")
class RefactModel(TextModel):
    model_arch = gguf.MODEL_ARCH.REFACT

    def set_vocab(self):
        super().set_vocab()

        # TODO: how to determine special FIM tokens automatically?
        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False,
                                          special_token_types = ['prefix', 'suffix', 'middle', 'eot'])
        special_vocab._set_special_token("prefix", 1)
        special_vocab._set_special_token("suffix", 3)
        special_vocab._set_special_token("middle", 2)
        special_vocab.chat_template = None  # do not add it twice
        special_vocab.add_to_gguf(self.gguf_writer)

    def set_gguf_parameters(self):
        hidden_dim = self.hparams["n_embd"]
        inner_dim = 4 * hidden_dim
        hidden_dim = int(2 * inner_dim / 3)
        multiple_of = 256
        ff_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)

        # refact uses Alibi. So this is from config.json which might be used by training.
        self.gguf_writer.add_context_length(self.hparams["n_positions"])
        self.gguf_writer.add_embedding_length(self.hparams["n_embd"])

        self.gguf_writer.add_feed_forward_length(ff_dim)
        self.gguf_writer.add_block_count(self.block_count)
        self.gguf_writer.add_head_count(self.hparams["n_head"])
        self.gguf_writer.add_head_count_kv(1)
        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"])
        self.gguf_writer.add_file_type(self.ftype)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        hidden_dim = self.hparams["n_embd"]
        inner_dim = 4 * hidden_dim
        hidden_dim = int(2 * inner_dim / 3)
        multiple_of = 256
        ff_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
        n_head = self.hparams["n_head"]
        n_head_kv = 1
        head_dim = self.hparams["n_embd"] // n_head

        if bid is not None:
            if name == f"transformer.h.{bid}.attn.kv.weight":
                yield from super().modify_tensors(data_torch[:n_head_kv * head_dim], self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_K, bid), bid)
                yield from super().modify_tensors(data_torch[n_head_kv * head_dim:], self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_V, bid), bid)
                return
            if name == f"transformer.h.{bid}.attn.q.weight":
                yield from super().modify_tensors(data_torch, self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_Q, bid), bid)
                return
            if name == f"transformer.h.{bid}.mlp.gate_up_proj.weight":
                yield from super().modify_tensors(data_torch[:ff_dim], self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE, bid), bid)
                yield from super().modify_tensors(data_torch[ff_dim:], self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP, bid), bid)
                return

        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("StableLmForCausalLM", "StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM")
class StableLMModel(TextModel):
    model_arch = gguf.MODEL_ARCH.STABLELM

    def set_vocab(self):
        if (self.dir_model / "tokenizer.json").is_file():
            self._set_vocab_gpt2()
        else:
            # StableLM 2 1.6B used to have a vocab in a similar format to Qwen's vocab
            self._set_vocab_qwen()

    def set_gguf_parameters(self):
        hparams = self.hparams

        self.gguf_writer.add_context_length(hparams["max_position_embeddings"])
        self.gguf_writer.add_embedding_length(hparams["hidden_size"])
        self.gguf_writer.add_block_count(self.block_count)
        self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
        rotary_factor = self.find_hparam(["partial_rotary_factor", "rope_pct"])
        self.gguf_writer.add_rope_dimension_count(int(rotary_factor * (hparams["hidden_size"] // hparams["num_attention_heads"])))
        self.gguf_writer.add_head_count(hparams["num_attention_heads"])
        self.gguf_writer.add_head_count_kv(hparams["num_key_value_heads"])
        self.gguf_writer.add_parallel_residual(hparams["use_parallel_residual"] if "use_parallel_residual" in hparams else True)
        self.gguf_writer.add_layer_norm_eps(self.find_hparam(["layer_norm_eps", "norm_eps"]))
        self.gguf_writer.add_file_type(self.ftype)

    _q_norms: list[dict[str, Tensor]] | None = None
    _k_norms: list[dict[str, Tensor]] | None = None

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        n_head = self.hparams["num_attention_heads"]
        n_kv_head = self.hparams["num_key_value_heads"]

        if name.find("q_layernorm.norms") != -1:
            assert bid is not None

            if self._q_norms is None:
                self._q_norms = [{} for _ in range(self.block_count)]

            self._q_norms[bid][name] = data_torch

            if len(self._q_norms[bid]) >= n_head:
                return self._stack_qk_norm(bid, n_head, self._q_norms[bid], "q_layernorm")
            else:
                return

        if name.find("k_layernorm.norms") != -1:
            assert bid is not None

            if self._k_norms is None:
                self._k_norms = [{} for _ in range(self.block_count)]

            self._k_norms[bid][name] = data_torch

            if len(self._k_norms[bid]) >= n_kv_head:
                return self._stack_qk_norm(bid, n_kv_head, self._k_norms[bid], "k_layernorm")
            else:
                return

        yield from super().modify_tensors(data_torch, name, bid)

    def _stack_qk_norm(self, bid: int, n_head: int, norms: dict[str, Tensor], layer_name: str = "q_layernorm"):
        datas: list[Tensor] = []
        # extract the norms in order
        for xid in range(n_head):
            ename = f"model.layers.{bid}.self_attn.{layer_name}.norms.{xid}.weight"
            datas.append(norms[ename])
            del norms[ename]
        data_torch = torch.stack(datas, dim=0)

        merged_name = f"model.layers.{bid}.self_attn.{layer_name}.weight"

        yield from super().modify_tensors(data_torch, merged_name, bid)

    def prepare_tensors(self):
        super().prepare_tensors()

        if self._q_norms is not None or self._k_norms is not None:
            # flatten two `list[dict[str, Tensor]]` into a single `list[str]`
            norms = (
                [k for d in self._q_norms for k in d.keys()] if self._q_norms is not None else []
            ) + (
                [k for d in self._k_norms for k in d.keys()] if self._k_norms is not None else []
            )
            if len(norms) > 0:
                raise ValueError(f"Unprocessed norms: {norms}")


@ModelBase.register(
    "LLaMAForCausalLM",
    "LlamaForCausalLM",
    "MistralForCausalLM",
    "MixtralForCausalLM",
    "VLlama3ForCausalLM",
    "LlavaForConditionalGeneration",
    "VoxtralForConditionalGeneration",
    "IQuestCoderForCausalLM",
    "LlamaModel")
class LlamaModel(TextModel):
    model_arch = gguf.MODEL_ARCH.LLAMA
    undo_permute = True

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        # fix for SmolVLM2, missing `num_attention_heads` in config.json
        if self.hf_arch == "VLlama3ForCausalLM":
            self.hparams["num_attention_heads"] = self.hparams.get("num_attention_heads", 32)
        # Mistral consolidated format has no config.json; origin_hf_arch is HF-only.
        if self.is_mistral_format:
            self.origin_hf_arch = None
        else:
            hparams = ModelBase.load_hparams(self.dir_model, is_mistral_format=False)
            self.origin_hf_arch = hparams.get('architectures', [None])[0]

    def set_vocab(self):
        if self.origin_hf_arch == "GlmasrModel":
            return self._set_vocab_glmedge()

        if self.is_mistral_format:
            return self._set_vocab_mistral()

        path_tekken_json = self.dir_model / "tekken.json"
        path_tokenizer_json = self.dir_model / "tokenizer.json"
        if path_tekken_json.is_file() and not path_tokenizer_json.is_file():
            self._set_vocab_mistral()

        try:
            self._set_vocab_sentencepiece()
        except FileNotFoundError:
            try:
                self._set_vocab_llama_hf()
            except (FileNotFoundError, TypeError):
                # Llama 3
                self._set_vocab_gpt2()

        # Apply to CodeLlama only (and ignore for Llama 3 with a vocab size of 128256)
        if self.hparams.get("vocab_size", 32000) == 32016:
            special_vocab = gguf.SpecialVocab(
                self.dir_model, load_merges=False,
                special_token_types = ['prefix', 'suffix', 'middle', 'eot']
            )
            special_vocab._set_special_token("prefix", 32007)
            special_vocab._set_special_token("suffix", 32008)
            special_vocab._set_special_token("middle", 32009)
            special_vocab._set_special_token("eot",    32010)
            special_vocab.add_to_gguf(self.gguf_writer)

        tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
        if tokenizer_config_file.is_file():
            with open(tokenizer_config_file, "r", encoding="utf-8") as f:
                tokenizer_config_json = json.load(f)
                if "add_prefix_space" in tokenizer_config_json:
                    self.gguf_writer.add_add_space_prefix(tokenizer_config_json["add_prefix_space"])

        # Apply to granite small models only
        if self.hparams.get("vocab_size", 32000) == 49152:
            self.gguf_writer.add_add_bos_token(False)

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        hparams = self.hparams

        if not self.is_mistral_format:
            self.gguf_writer.add_vocab_size(hparams["vocab_size"])

        if (rope_dim := hparams.get("head_dim")) is None:
            rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
        self.gguf_writer.add_rope_dimension_count(rope_dim)

    @staticmethod
    def permute(weights: Tensor, n_head: int, n_head_kv: int | None):
        if n_head_kv is not None and n_head != n_head_kv:
            n_head = n_head_kv
        return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
                .swapaxes(1, 2)
                .reshape(weights.shape))

    def _repack_nvfp4(self, name: str, weight: Tensor, scale: Tensor, scale2: Tensor, input_scale: Tensor):
        # Mirror the BF16 Q/K RoPE permutation site in modify_tensors; the NVFP4 path bypasses it.
        if self.undo_permute:
            n_head = self.find_hparam(["n_heads", "num_attention_heads"], optional=True)
            n_kv_head = self.find_hparam(["n_kv_heads", "num_key_value_heads"], optional=True)
            if n_head is not None:
                if name.endswith("q_proj.weight"):
                    weight = LlamaModel.permute(weight, n_head, n_head)
                    scale  = LlamaModel.permute(scale, n_head, n_head)
                elif name.endswith("k_proj.weight"):
                    weight = LlamaModel.permute(weight, n_head, n_kv_head)
                    scale  = LlamaModel.permute(scale, n_head, n_kv_head)
        super()._repack_nvfp4(name, weight, scale, scale2, input_scale)

    _experts: list[dict[str, Tensor]] | None = None

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        if "text_model." in name:
            name = name.replace("text_model.", "") # for SmolVLM

        return super().filter_tensors((name, gen))

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        n_head = self.find_hparam(["n_heads", "num_attention_heads"])
        n_kv_head = self.find_hparam(["n_kv_heads", "num_key_value_heads"])

        if self.hf_arch == "LlamaModel":
            name = "model." + name

        if self.undo_permute:
            if name.endswith(("q_proj.weight", "q_proj.bias")):
                data_torch = LlamaModel.permute(data_torch, n_head, n_head)
            if name.endswith(("k_proj.weight", "k_proj.bias")):
                data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)

        # process the experts separately
        if name.find("block_sparse_moe.experts") != -1:
            n_experts = self.hparams["num_local_experts"]

            assert bid is not None

            if self._experts is None:
                self._experts = [{} for _ in range(self.block_count)]

            self._experts[bid][name] = data_torch

            if len(self._experts[bid]) >= n_experts * 3:
                # merge the experts into a single 3d tensor
                for wid in ["w1", "w2", "w3"]:
                    datas: list[Tensor] = []

                    for xid in range(n_experts):
                        ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{wid}.weight"
                        datas.append(self._experts[bid][ename])
                        del self._experts[bid][ename]

                    data_torch = torch.stack(datas, dim=0)

                    merged_name = f"layers.{bid}.feed_forward.experts.{wid}.weight"

                    yield from super().modify_tensors(data_torch, merged_name, bid)
                return
            else:
                return

        yield from super().modify_tensors(data_torch, name, bid)

    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
        if rope_params := self.rope_parameters.get("full_attention", self.rope_parameters):
            if rope_params.get("rope_type", '').lower() == "llama3":
                base = rope_params.get("rope_theta", 10000.0)
                if (dim := self.hparams.get("head_dim")) is None:
                    dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
                freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))

                factor = rope_params.get("factor", 8.0)
                low_freq_factor = rope_params.get("low_freq_factor", 1.0)
                high_freq_factor = rope_params.get("high_freq_factor", 4.0)
                old_context_len = self.hparams.get("original_max_position_embeddings", 8192)

                low_freq_wavelen = old_context_len / low_freq_factor
                high_freq_wavelen = old_context_len / high_freq_factor
                # assert low_freq_wavelen != high_freq_wavelen # Errors for Llama4

                rope_factors = []
                for freq in freqs:
                    wavelen = 2 * math.pi / freq
                    if wavelen < high_freq_wavelen:
                        rope_factors.append(1)
                    elif wavelen > low_freq_wavelen:
                        rope_factors.append(factor)
                    else:
                        smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
                        rope_factors.append(1 / ((1 - smooth) / factor + smooth))

                yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32))

    def prepare_tensors(self):
        super().prepare_tensors()

        if self._experts is not None:
            # flatten `list[dict[str, Tensor]]` into `list[str]`
            experts = [k for d in self._experts for k in d.keys()]
            if len(experts) > 0:
                raise ValueError(f"Unprocessed experts: {experts}")


@ModelBase.register("ArceeForCausalLM")
class ArceeModel(LlamaModel):
    model_arch = gguf.MODEL_ARCH.ARCEE

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        self._try_set_pooling_type()


@ModelBase.register("AfmoeForCausalLM")
class AfmoeModel(LlamaModel):
    model_arch = gguf.MODEL_ARCH.AFMOE

    def set_gguf_parameters(self):
        super().set_gguf_parameters()

        # MoE parameters
        if (n_shared_experts := self.hparams.get("num_shared_experts")) is not None:
            self.gguf_writer.add_expert_shared_count(n_shared_experts)
        if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None:
            self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
        if (n_dense_layers := self.hparams.get("num_dense_layers")) is not None:
            self.gguf_writer.add_leading_dense_block_count(n_dense_layers)

        # Route normalization and scaling
        if (route_norm := self.hparams.get("route_norm")) is not None:
            self.gguf_writer.add_expert_weights_norm(route_norm)
        if (route_scale := self.hparams.get("route_scale")) is not None:
            self.gguf_writer.add_expert_weights_scale(route_scale)

        # Sliding window attention
        if (sliding_window := self.hparams.get("sliding_window")) is not None:
            self.gguf_writer.add_sliding_window(sliding_window)

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        if name.endswith(".expert_bias"):
            name = name.replace(".expert_bias", ".expert_bias.bias")

        return super().filter_tensors((name, gen))

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        # Handle expert weights - they're already merged in the HF format
        # process the experts separately
        if name.find("mlp.experts") != -1:
            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
            assert bid is not None

            if self._experts is None:
                self._experts = [{} for _ in range(self.block_count)]

            self._experts[bid][name] = data_torch

            if len(self._experts[bid]) >= n_experts * 3:
                # merge the experts into a single 3d tensor
                for w_name in ["gate_proj", "up_proj", "down_proj"]:
                    datas: list[Tensor] = []

                    for xid in range(n_experts):
                        ename_to_retrieve = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
                        datas.append(self._experts[bid][ename_to_retrieve])
                        del self._experts[bid][ename_to_retrieve]

                    data_torch = torch.stack(datas, dim=0)
                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
                    yield from ModelBase.modify_tensors(self, data_torch, merged_name, bid)

                return
            else:
                return

        yield from ModelBase.modify_tensors(self, data_torch, name, bid)


@ModelBase.register(
    "LlavaForConditionalGeneration", # pixtral
    "Mistral3ForConditionalGeneration", # mistral small 3.1
)
class LlavaVisionModel(MmprojModel):
    img_break_tok_id = -1
    use_break_tok = True

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        if self.hparams.get("model_type") == "pixtral":
            # layer_norm_eps is not in config.json, it is hard-coded in modeling_pixtral.py
            self.hparams["layer_norm_eps"] = self.hparams.get("layer_norm_eps", 1e-5)
            if self.use_break_tok:
                self.img_break_tok_id = self.get_token_id("[IMG_BREAK]")
        elif self.is_mistral_format:
            # hparams is already vision config here so norm_eps is only defined in global_config.
            self.hparams["norm_eps"] = self.global_config.get("norm_eps", None)
            assert self.hparams["norm_eps"] is not None, "norm_eps not found in params.json"
            if self.use_break_tok:
                self.img_break_tok_id = self.find_vparam(["image_break_token_id"])

                # params.json may ship -1 placeholders (Mistral Medium 3.5)
                # resolve the real id from the bundled tokenizer in that case
                if self.img_break_tok_id < 0:
                    self.img_break_tok_id = self.get_mistral_token_id("[IMG_BREAK]")
        else:
            raise ValueError(f"Unsupported model type: {self.hparams['model_type']}")
        logger.info(f"Image break token id: {self.img_break_tok_id}")

    def get_token_id(self, token: str) -> int:
        tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
        with open(tokenizer_config_file, "r", encoding="utf-8") as f:
            added_tokens_decoder = json.load(f).get('added_tokens_decoder') or {}
            for id_, token_data in added_tokens_decoder.items():
                if token_data.get("content") == token:
                    return int(id_)
            # fallthrough to tokenizer.json
        with open(self.dir_model / "tokenizer.json", "r", encoding="utf-8") as f:
            tokenizer_json = json.load(f)
            for token_data in tokenizer_json["added_tokens"]:
                if token_data["content"] == token:
                    return int(token_data["id"])
        raise ValueError(f"Token '{token}' not found in tokenizer config.")

    def get_mistral_token_id(self, token: str) -> int:
        # mistral native format ships tekken.json or a versioned spm tokenizer
        tekken_file = self.dir_model / "tekken.json"
        if tekken_file.is_file():
            with open(tekken_file, "r", encoding="utf-8") as f:
                data = json.load(f)
            for entry in data.get("special_tokens", []):
                if entry.get("token_str") == token:
                    return int(entry["rank"])
        tokenizer_json_file = self.dir_model / "tokenizer.json"
        if tokenizer_json_file.is_file():
            with open(tokenizer_json_file, "r", encoding="utf-8") as f:
                data = json.load(f)
            for entry in data.get("added_tokens", []):
                if entry.get("content") == token:
                    return int(entry["id"])
        raise ValueError(f"Token '{token}' not found in mistral tokenizer files.")

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        hparams = self.hparams
        if hparams.get("model_type") == "pixtral":
            self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.PIXTRAL)
            self.gguf_writer.add_vision_attention_layernorm_eps(hparams["layer_norm_eps"])

            # hidden_act
            if hparams["hidden_act"] == "silu":
                self.gguf_writer.add_vision_use_silu(True)
            elif hparams["hidden_act"] == "gelu":
                self.gguf_writer.add_vision_use_gelu(True)
            else:
                raise ValueError(f"Unsupported hidden_act: {hparams['hidden_act']}")

            # spatial_merge_size
            if "spatial_merge_size" in self.global_config:
                self.gguf_writer.add_vision_spatial_merge_size(self.global_config["spatial_merge_size"])

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        n_head = (
            self.hparams["num_attention_heads"] if not self.is_mistral_format else self.find_vparam(["num_attention_heads"])
        )
        n_kv_head = n_head

        valid_prefixes = (
            "multi_modal_projector.",
            "vision_tower.",
            "vision_encoder.",
            "vision_language_adapter.",
            "patch_merger.",
            "pre_mm_projector_norm",
        )

        if any(name.startswith(prefix) for prefix in valid_prefixes):
            # process vision tensors
            if name.endswith(("q_proj.weight", "q_proj.bias")) and not self.is_mistral_format:
                data_torch = LlamaModel.permute(data_torch, n_head, n_head)
            if name.endswith(("k_proj.weight", "k_proj.bias")) and not self.is_mistral_format:
                data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)
            yield from super().modify_tensors(data_torch, name, bid)
            return

        embed_key = "embed_tokens.weight" if not self.is_mistral_format else "tok_embeddings.weight"
        if self.img_break_tok_id > 0 and embed_key in name:
            logger.info(f"Extracting [IMG_BREAK] token embedding from {name}")
            # for pixtral model, we need to extract the [IMG_BREAK] token embedding
            img_break_embd = data_torch[self.img_break_tok_id]
            name = gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_TOK_EMBD_IMG_BREAK]
            yield from super().modify_tensors(img_break_embd, name, bid)

        return # skip other tensors


@ModelBase.register("Idefics3ForConditionalGeneration", "SmolVLMForConditionalGeneration")
class SmolVLMModel(MmprojModel):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        if self.hparams["model_type"] == "smolvlm_vision":
            # fix for SmolVLM2, missing some keys in config.json
            # default values are taken from transformers code
            self.hparams["hidden_size"] = self.hparams.get("hidden_size", 1152)
            self.hparams["num_attention_heads"] = self.hparams.get("num_attention_heads", 16)
            self.hparams["intermediate_size"] = self.hparams.get("intermediate_size", 3072)

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.IDEFICS3)
        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams.get("layer_norm_eps", 1e-5))
        self.gguf_writer.add_vision_projector_scale_factor(self.global_config.get("scale_factor", 2))
        self.gguf_writer.add_vision_use_gelu(True)

        # Add the preprocessor longest edge size
        preproc_image_size = self.preprocessor_config.get("size", {}).get("longest_edge", self.image_size)
        self.gguf_writer.add_vision_preproc_image_size(preproc_image_size)

    def tensor_force_quant(self, name, new_name, bid, n_dims):
        if ".embeddings." in name:
            return gguf.GGMLQuantizationType.F32
        return super().tensor_force_quant(name, new_name, bid, n_dims)

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        is_vision_tensor = "vision_tower" in name or "vision_model" in name or "model.connector" in name

        if not is_vision_tensor:
            return None

        return super().filter_tensors(item)


@ModelBase.register(
    "Llama4ForConditionalGeneration",
    "Llama4ForCausalLM",
)
class Llama4Model(LlamaModel):
    model_arch = gguf.MODEL_ARCH.LLAMA4
    undo_permute = False

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        # IMPORTANT: the normal "intermediate_size" is renamed to "intermediate_size_mlp", we need to undo this
        self.hparams["intermediate_size_moe"] = self.hparams["intermediate_size"]
        self.hparams["intermediate_size"] = self.hparams["intermediate_size_mlp"]

    def set_vocab(self):
        self._set_vocab_gpt2()

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        self.gguf_writer.add_interleave_moe_layer_step(self.hparams["interleave_moe_layer_step"])
        self.gguf_writer.add_expert_feed_forward_length(self.hparams["intermediate_size_moe"])
        if "layer_types" in self.hparams:
            if all(lt == "full_attention" for lt in self.hparams["layer_types"]):
                # all layers are full attention (for MobileLLM), disable swa
                self.gguf_writer.add_sliding_window(0)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
        # split the gate_up into gate and up
        if "gate_up_proj" in name:
            name_up = name.replace("gate_up_proj", "up_proj.weight")
            name_gate = name.replace("gate_up_proj", "gate_proj.weight")
            dim_half = data_torch.shape[-1] // 2
            gate_proj_weight, up_proj_weight = data_torch.transpose(-1, -2).split(dim_half, dim=-2)
            yield from super().modify_tensors(gate_proj_weight, name_gate, bid)
            yield from super().modify_tensors(up_proj_weight, name_up, bid)
            return

        if name.endswith("down_proj"):
            name += ".weight"
            data_torch = data_torch.transpose(-1, -2)

        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("Llama4ForConditionalGeneration")
class Llama4VisionModel(MmprojModel):
    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.LLAMA4)
        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams["norm_eps"])
        self.gguf_writer.add_vision_projector_scale_factor(int(1.0 / self.hparams["pixel_shuffle_ratio"]))
        assert self.hparams["hidden_act"] == "gelu"
        self.gguf_writer.add_vision_use_gelu(True)

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        if "multi_modal_projector" not in name and "vision_model" not in name:
            return None

        if "positional_embedding_vlm" in name and ".weight" not in name:
            name += ".weight"

        return super().filter_tensors((name, gen))

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        if "multi_modal_projector.linear_1" in name:
            # despite the name with number postfix, this is a single fully connected layer
            yield (gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_MMPROJ_FC] + '.weight', data_torch)
        else:
            yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("DeciLMForCausalLM")
class DeciModel(TextModel):
    model_arch = gguf.MODEL_ARCH.DECI

    @staticmethod
    def _ffn_mult_to_intermediate_size(ffn_mult: float, n_embd: int) -> int:
        # DeciLM-specific code
        intermediate_size = int(2 * ffn_mult * n_embd / 3)
        return DeciModel._find_multiple(intermediate_size, 256)

    @staticmethod
    def _find_multiple(n: int, k: int) -> int:
        # DeciLM-specific code
        if n % k == 0:
            return n
        return n + k - (n % k)

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

        if "block_configs" in self.hparams: # Llama-3_1-Nemotron-51B
            _block_configs: list[dict[str,Any]] = self.hparams["block_configs"]
            assert self.block_count == len(_block_configs)
            self._num_kv_heads = list()
            self._num_heads = list()
            _ffn_multipliers = list()
            # ***linear attention layer***
            # if n_heads_in_group is None and replace_with_linear is True
            # then _num_kv_heads[il] is 0 and _num_heads[il] is num_attention_heads
            # ***attention-free layer***
            # if n_heads_in_group is None and replace_with_linear is False
            # then _num_kv_heads[il] is 0 and _num_heads[il] is 0
            # ***normal attention-layer***
            # if n_heads_in_group is not None, then
            # _num_kv_heads[il] is num_attention_head // n_heads_in_group and
            # _num_heads[il] is num_attention_head
            # ***dummy layer*** for nemotron 253B
            # if n_heads_in_group is None and ffn_mult is None
            # then _num_kv_heads[il] is 0 and _num_heads[il] is 0 and _ffn_dims is 0
            for il in range(len(_block_configs)):
                if _block_configs[il]["attention"]["n_heads_in_group"] is None:
                    if _block_configs[il]["attention"]["replace_with_linear"] is True:
                        self._num_kv_heads.append(0)
                        self._num_heads.append(self.hparams["num_attention_heads"])
                    else:
                        self._num_kv_heads.append(0)
                        self._num_heads.append(0)
                else:
                    self._num_kv_heads.append(self.hparams["num_attention_heads"] // _block_configs[il]["attention"]["n_heads_in_group"])
                    self._num_heads.append(self.hparams["num_attention_heads"])
                if _block_configs[il]["ffn"]["ffn_mult"] is None: # dummy layer
                    _ffn_multipliers.append(0.0)
                else:
                    _ffn_multipliers.append(_block_configs[il]["ffn"]["ffn_mult"])
            assert self.block_count == len(self._num_kv_heads)
            assert self.block_count == len(self._num_heads)
            assert self.block_count == len(_ffn_multipliers)
            assert isinstance(self._num_kv_heads, list) and isinstance(self._num_kv_heads[0], int)
            assert isinstance(self._num_heads, list) and isinstance(self._num_heads[0], int)
            assert isinstance(_ffn_multipliers, list) and isinstance(_ffn_multipliers[0], float)
            self._ffn_dims: list[int] = [
                DeciModel._ffn_mult_to_intermediate_size(multiplier, self.hparams["hidden_size"])
                for multiplier in _ffn_multipliers
            ]

    def set_vocab(self):
        # Please change tokenizer_config.json of Llama-3_1-Nemotron-51B's
        # eos_token from '|eot_id|' to '|end_of_text|'
        if self.hparams.get("vocab_size", 128256) == 128256:
            tokens, toktypes, tokpre = self.get_vocab_base()
            self.gguf_writer.add_tokenizer_model("gpt2")
            self.gguf_writer.add_tokenizer_pre(tokpre)
            self.gguf_writer.add_token_list(tokens)
            self.gguf_writer.add_token_types(toktypes)

            special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
            special_vocab.add_to_gguf(self.gguf_writer)
        else:
            # DeciLM-7B
            self._set_vocab_llama_hf()

    def set_gguf_parameters(self):
        if "block_configs" in self.hparams: # Llama-3_1-Nemotron-51B
            assert self.block_count == len(self._num_kv_heads)
            assert self.block_count == len(self._num_heads)
            assert self.block_count == len(self._ffn_dims)
            if (rope_theta := self.rope_parameters.get("rope_theta")) is not None:
                self.gguf_writer.add_rope_freq_base(rope_theta)
            self.gguf_writer.add_head_count_kv(self._num_kv_heads)
            self.gguf_writer.add_head_count(self._num_heads)
            self.gguf_writer.add_feed_forward_length(self._ffn_dims)
            self.gguf_writer.add_block_count(self.block_count)
            self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
            self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
            self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
            self.gguf_writer.add_key_length(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
            self.gguf_writer.add_value_length(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
            self.gguf_writer.add_file_type(self.ftype)
        else: # DeciLM-7B
            super().set_gguf_parameters()
            if "num_key_value_heads_per_layer" in self.hparams: # DeciLM-7B
                self._num_kv_heads: list[int] = self.hparams["num_key_value_heads_per_layer"]
                assert self.block_count == len(self._num_kv_heads)
                self.gguf_writer.add_head_count_kv(self._num_kv_heads)
        hparams = self.hparams
        self.gguf_writer.add_vocab_size(hparams["vocab_size"])

        if (rope_dim := hparams.get("head_dim")) is None:
            rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
        self.gguf_writer.add_rope_dimension_count(rope_dim)

    @staticmethod
    def permute(weights: Tensor, n_head: int, n_head_kv: int | None):
        if n_head_kv is not None and n_head != n_head_kv:
            n_head = n_head_kv
        return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
                .swapaxes(1, 2)
                .reshape(weights.shape))

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        n_head = self.hparams["num_attention_heads"]
        if bid is not None:
            if "num_key_value_heads_per_layer" in self.hparams:
                n_kv_head = self.hparams["num_key_value_heads_per_layer"][bid]
            elif "block_configs" in self.hparams:
                n_kv_head = self._num_kv_heads[bid]
                n_head = self._num_heads[bid]
            else:
                n_kv_head = self.hparams.get("num_key_value_heads")
        else:
            n_kv_head = self.hparams.get("num_key_value_heads")

        if name.endswith(("q_proj.weight", "q_proj.bias")):
            data_torch = DeciModel.permute(data_torch, n_head, n_head)
        if name.endswith(("k_proj.weight", "k_proj.bias")):
            data_torch = DeciModel.permute(data_torch, n_head, n_kv_head)
        yield from super().modify_tensors(data_torch, name, bid)

    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
        if rope_params := self.rope_parameters.get("full_attention", self.rope_parameters):
            if rope_params.get("rope_type", '').lower() == "llama3":
                base = rope_params.get("rope_theta", 10000.0)
                if (dim := self.hparams.get("head_dim")) is None:
                    dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
                freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))

                factor = rope_params.get("factor", 8.0)
                low_freq_factor = rope_params.get("low_freq_factor", 1.0)
                high_freq_factor = rope_params.get("high_freq_factor", 4.0)
                old_context_len = self.hparams.get("original_max_position_embeddings", 8192)

                low_freq_wavelen = old_context_len / low_freq_factor
                high_freq_wavelen = old_context_len / high_freq_factor
                assert low_freq_wavelen != high_freq_wavelen

                rope_factors = []
                for freq in freqs:
                    wavelen = 2 * math.pi / freq
                    if wavelen < high_freq_wavelen:
                        rope_factors.append(1)
                    elif wavelen > low_freq_wavelen:
                        rope_factors.append(factor)
                    else:
                        smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
                        rope_factors.append(1 / ((1 - smooth) / factor + smooth))

                yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32))

    def prepare_tensors(self):
        super().prepare_tensors()


@ModelBase.register("BitnetForCausalLM")
class BitnetModel(TextModel):
    model_arch = gguf.MODEL_ARCH.BITNET

    def set_vocab(self):
        self._set_vocab_sentencepiece()

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
        self.gguf_writer.add_rope_scaling_factor(1.0)

    def weight_quant(self, weight: Tensor) -> Tensor:
        dtype = weight.dtype
        weight = weight.float()
        scale = weight.abs().mean().clamp(min=1e-5)
        iscale = 1 / scale
        # TODO: multiply by the scale directly instead of inverting it twice
        # (this is also unnecessarily doubly inverted upstream)
        # ref: https://huggingface.co/1bitLLM/bitnet_b1_58-3B/blob/af89e318d78a70802061246bf037199d2fb97020/utils_quant.py#L10
        result = (weight * iscale).round().clamp(-1, 1) / iscale
        return result.type(dtype)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        new_name = self.map_tensor_name(name)

        if any(self.match_model_tensor_name(new_name, key, bid) for key in [
            gguf.MODEL_TENSOR.ATTN_Q,
            gguf.MODEL_TENSOR.ATTN_K,
            gguf.MODEL_TENSOR.ATTN_V,
            gguf.MODEL_TENSOR.ATTN_OUT,
            gguf.MODEL_TENSOR.FFN_UP,
            gguf.MODEL_TENSOR.FFN_DOWN,
            gguf.MODEL_TENSOR.FFN_GATE,
        ]):
            # transform weight into 1/0/-1 (in fp32)
            data_torch = self.weight_quant(data_torch)

        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("GrokForCausalLM", "Grok1ForCausalLM")
class GrokModel(TextModel):
    model_arch = gguf.MODEL_ARCH.GROK

    def set_vocab(self):
        if (self.dir_model / 'tokenizer.model').is_file():
            self._set_vocab_sentencepiece()
            return

        if not (self.dir_model / 'tokenizer.json').is_file() or not (self.dir_model / 'chat_template.jinja').is_file():
            logger.error('Error: Missing vocab and chat template, download files from https://huggingface.co/alvarobartt/grok-2-tokenizer')
            sys.exit(1)

        self._set_vocab_gpt2()

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

    def set_gguf_parameters(self):
        super().set_gguf_parameters()

        self.gguf_writer.add_attn_logit_softcapping(self.hparams.get("attn_logit_softcapping", 30.0))
        self.gguf_writer.add_router_logit_softcapping(self.hparams.get("router_logit_softcapping", 30.0))
        if (final_logit_softcap := self.hparams.get("final_logit_softcapping")):
            self.gguf_writer.add_final_logit_softcapping(final_logit_softcap)

        if (rope_dim := self.hparams.get("head_dim")) is None:
            rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]

        if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None:
            self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)

        # Treat "original" as "yarn", seems to have been a mistake
        if self.hparams.get("rope_type") in ("yarn", "original"):
            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
            self.gguf_writer.add_rope_scaling_factor(self.hparams["scaling_factor"])
            self.gguf_writer.add_rope_scaling_orig_ctx_len(self.hparams["original_max_position_embeddings"])
            self.gguf_writer.add_rope_scaling_yarn_ext_factor(self.hparams["extrapolation_factor"])
            self.gguf_writer.add_rope_scaling_yarn_attn_factor(self.hparams["attn_factor"])
            self.gguf_writer.add_rope_scaling_yarn_beta_fast(self.hparams["beta_fast"])
            self.gguf_writer.add_rope_scaling_yarn_beta_slow(self.hparams["beta_slow"])

        if temp_len := self.hparams.get("attn_temperature_len"):
            self.gguf_writer.add_attn_temperature_length(temp_len)

        self.gguf_writer.add_attn_output_scale(self.hparams.get("attn_output_multiplier", rope_dim**-0.5))
        self.gguf_writer.add_embedding_scale(self.hparams["embedding_multiplier_scale"])
        self.gguf_writer.add_logit_scale(self.hparams["output_multiplier_scale"])

    _experts: list[dict[str, list[Tensor]]] | None = None
    _cur_expert = ""

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        deferred: list[tuple[Tensor, str, int | None]] = []
        is_expert = ".moe." in name or ".block_sparse_moe.experts." in name

        if not is_expert:
            deferred.append((data_torch, name, bid))

        # process the experts separately
        if is_expert or self._cur_expert:
            n_experts = self.hparams["num_local_experts"]

            assert bid is not None

            if self._experts is None:
                self._experts = [{} for _ in range(self.block_count)]

            # concatenate split tensors
            if name in self._experts[bid]:
                self._cur_expert = name
                self._experts[bid][name].append(data_torch)
                return
            elif is_expert:
                self._cur_expert = name
                self._experts[bid][name] = [data_torch]
                return
            else:
                self._cur_expert = ""

            for bid in range(self.block_count):
                if len(self._experts[bid]) >= n_experts * 3:
                    # merge the experts into a single 3d tensor
                    for wid in [("linear", "w1", 0), ("linear_1", "w2", 1), ("linear_v", "w3", 0)]:
                        datas: list[Tensor] = []

                        for xid in range(n_experts):
                            ename = f"transformer.decoder_layer.{bid}.moe.{xid}.{wid[0]}.weight"
                            if ename not in self._experts[bid]:
                                ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{wid[1]}.weight"
                            tensor_list = self._experts[bid][ename]
                            datas.append(torch.cat(tensor_list, dim=wid[2]) if len(tensor_list) > 1 else tensor_list[0])
                            del self._experts[bid][ename]

                        data_torch = torch.stack(datas, dim=0)

                        merged_name = f"transformer.decoder_layer.{bid}.moe.{wid[0]}.weight"

                        yield from super().modify_tensors(data_torch, merged_name, bid)

        for t in deferred:
            yield from super().modify_tensors(*t)


@ModelBase.register("DbrxForCausalLM")
class DbrxModel(TextModel):
    model_arch = gguf.MODEL_ARCH.DBRX

    def set_gguf_parameters(self):
        ffn_config = self.hparams["ffn_config"]
        attn_config = self.hparams["attn_config"]
        self.gguf_writer.add_block_count(self.block_count)

        self.gguf_writer.add_context_length(self.hparams["max_seq_len"])
        self.gguf_writer.add_embedding_length(self.hparams["d_model"])
        self.gguf_writer.add_feed_forward_length(ffn_config["ffn_hidden_size"])

        self.gguf_writer.add_head_count(self.hparams["n_heads"])
        self.gguf_writer.add_head_count_kv(attn_config["kv_n_heads"])

        self.gguf_writer.add_rope_freq_base(attn_config["rope_theta"])

        self.gguf_writer.add_clamp_kqv(attn_config["clip_qkv"])

        self.gguf_writer.add_expert_count(ffn_config["moe_num_experts"])
        self.gguf_writer.add_expert_used_count(ffn_config["moe_top_k"])

        self.gguf_writer.add_layer_norm_eps(1e-5)

        self.gguf_writer.add_file_type(self.ftype)
        logger.info(f"gguf: file type = {self.ftype}")

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        n_expert = self.hparams["ffn_config"]["moe_num_experts"]
        n_ff = self.hparams["ffn_config"]["ffn_hidden_size"]
        n_embd = self.hparams["d_model"]

        # Specific behavior for experts tensors: suffix .weight, view as 3D and transpose
        # original implementation expects (n_expert, n_ff, n_embd) for all experts weights
        # But llama.cpp moe graph works differently
        # AND the dimensions in ggml are typically in the reverse order of the pytorch dimensions
        # so (n_expert, n_ff, n_embd) in pytorch is {n_embd, n_ff, n_expert} in ggml_tensor
        exp_tensor_names = {"ffn.experts.mlp.w1": None,       # LLM_TENSOR_FFN_GATE_EXPS ggml_tensor->ne{n_embd, n_ff,   n_expert}
                            "ffn.experts.mlp.w2": (0, 2, 1),  # LLM_TENSOR_FFN_DOWN_EXPS ggml_tensor->ne{n_ff,   n_embd, n_expert}
                            "ffn.experts.mlp.v1": None}       # LLM_TENSOR_FFN_UP_EXPS   ggml_tensor->ne{n_embd, n_ff,   n_expert}
        experts = False

        for exp_tensor_name in exp_tensor_names.keys():
            if name.find(exp_tensor_name) != -1 and name.find(".weight") == -1:
                experts = True
                data_torch = data_torch.view(n_expert, n_ff, n_embd)
                if (permute_tensor := exp_tensor_names[exp_tensor_name]) is not None:
                    data_torch = data_torch.permute(*permute_tensor)
                break

        # map tensor names
        # In MoE models the ffn tensors are typically most of the model weights,
        # and need to be quantizable. Quantize expects tensor names to be suffixed by .weight.
        # Every other model has the weight names ending in .weight,
        # let's assume that is the convention which is not the case for dbrx:
        # https://huggingface.co/databricks/dbrx-instruct/blob/main/model.safetensors.index.json#L15
        new_name = self.map_tensor_name(name if not experts else name + ".weight", try_suffixes=(".weight",))

        yield from super().modify_tensors(data_torch, new_name, bid)

    def tensor_force_quant(self, name: str, new_name: str, bid: int | None, n_dims: int) -> gguf.GGMLQuantizationType | bool:
        del name, new_name, bid  # unused

        return n_dims > 1


@ModelBase.register("MiniCPMForCausalLM")
class MiniCPMModel(TextModel):
    model_arch = gguf.MODEL_ARCH.MINICPM

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        embedding_scale = float(self.hparams["scale_emb"])
        self.gguf_writer.add_embedding_scale(embedding_scale)
        logger.info(f"gguf: (minicpm) embedding_scale = {embedding_scale}")
        residual_scale = self.hparams["scale_depth"] / self.hparams["num_hidden_layers"] ** 0.5
        self.gguf_writer.add_residual_scale(residual_scale)
        logger.info(f"gguf: (minicpm) residual_scale = {residual_scale}")
        logit_scale = self.hparams["hidden_size"] / self.hparams["dim_model_base"]
        self.gguf_writer.add_logit_scale(logit_scale)
        logger.info(f"gguf: (minicpm) logit_scale = {logit_scale}")

    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
        rope_dims = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]

        rope_scaling = self.find_hparam(['rope_scaling'], True)
        if rope_scaling is not None:
            long_factors = rope_scaling.get('long_factor', None)
            short_factors = rope_scaling.get('short_factor', None)

            if long_factors is None or short_factors is None:
                raise KeyError('Missing the required key rope_scaling.long_factor or rope_scaling_short_factor')

            if len(long_factors) != len(short_factors) or len(long_factors) != rope_dims / 2:
                raise ValueError(f'The length of rope long and short factors must be {rope_dims / 2}')

            yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_LONG), torch.tensor(long_factors, dtype=torch.float32))
            yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_SHORT), torch.tensor(short_factors, dtype=torch.float32))

    def set_vocab(self):
        self._set_vocab_sentencepiece()

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        n_head = self.hparams["num_attention_heads"]
        n_kv_head = self.hparams.get("num_key_value_heads")

        # HF models permute some of the tensors, so we need to undo that
        if name.endswith(("q_proj.weight")):
            data_torch = LlamaModel.permute(data_torch, n_head, n_head)
        if name.endswith(("k_proj.weight")):
            data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)

        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("MiniCPM3ForCausalLM")
class MiniCPM3Model(TextModel):
    model_arch = gguf.MODEL_ARCH.MINICPM3

    def set_gguf_parameters(self):
        hparams = self.hparams

        self.gguf_writer.add_file_type(self.ftype)
        self.gguf_writer.add_context_length(hparams["max_position_embeddings"])
        self.gguf_writer.add_embedding_length(hparams["hidden_size"])
        self.gguf_writer.add_block_count(self.block_count)
        self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
        self.gguf_writer.add_head_count(hparams["num_attention_heads"])
        self.gguf_writer.add_head_count_kv(hparams["num_key_value_heads"])
        self.gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"])
        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
        if "q_lora_rank" in hparams and hparams["q_lora_rank"] is not None:
            self.gguf_writer.add_q_lora_rank(hparams["q_lora_rank"])
        self.gguf_writer.add_kv_lora_rank(hparams["kv_lora_rank"])
        self.gguf_writer.add_key_length(hparams["qk_nope_head_dim"] + hparams["qk_rope_head_dim"])
        self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"])

    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
        rope_scaling = self.find_hparam(['rope_scaling'], True)
        if rope_scaling is not None:
            rope_dims = self.hparams["qk_rope_head_dim"]

            long_factors = rope_scaling.get('long_factor', None)
            short_factors = rope_scaling.get('short_factor', None)

            if long_factors is None or short_factors is None:
                raise KeyError('Missing the required key rope_scaling.long_factor or rope_scaling_short_factor')

            if len(long_factors) != len(short_factors) or len(long_factors) != rope_dims / 2:
                raise ValueError(f'The length of rope long and short factors must be {rope_dims / 2}')

            yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_LONG), torch.tensor(long_factors, dtype=torch.float32))
            yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_SHORT), torch.tensor(short_factors, dtype=torch.float32))

    def set_vocab(self):
        self._set_vocab_sentencepiece()

    def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor:
        if n_kv_head is not None and n_head != n_kv_head:
            n_head //= n_kv_head

        return (
            weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
            .swapaxes(1, 2)
            .reshape(weights.shape)
        )


@ModelBase.register("QWenLMHeadModel")
class QwenModel(TextModel):
    model_arch = gguf.MODEL_ARCH.QWEN

    @staticmethod
    def token_bytes_to_string(b):
        from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode  # ty: ignore[unresolved-import]
        byte_encoder = bytes_to_unicode()
        return ''.join([byte_encoder[ord(char)] for char in b.decode('latin-1')])

    @staticmethod
    def bpe(mergeable_ranks: dict[bytes, int], token: bytes, max_rank: int | None = None) -> list[bytes]:
        parts = [bytes([b]) for b in token]
        while True:
            min_idx = None
            min_rank = None
            for i, pair in enumerate(zip(parts[:-1], parts[1:])):
                rank = mergeable_ranks.get(pair[0] + pair[1])
                if rank is not None and (min_rank is None or rank < min_rank):
                    min_idx = i
                    min_rank = rank
            if min_rank is None or (max_rank is not None and min_rank >= max_rank):
                break
            assert min_idx is not None
            parts = parts[:min_idx] + [parts[min_idx] + parts[min_idx + 1]] + parts[min_idx + 2:]
        return parts

    def set_vocab(self):
        self._set_vocab_qwen()


@ModelBase.register(
    "Qwen2Model",
    "Qwen2ForCausalLM",
    "Qwen2AudioForConditionalGeneration",
    "KORMoForCausalLM",
    "AudioFlamingo3ForConditionalGeneration",
    "DotsOCRForCausalLM",
)
class Qwen2Model(TextModel):
    model_arch = gguf.MODEL_ARCH.QWEN2

    def set_vocab(self):
        try:
            self._set_vocab_sentencepiece()
        except FileNotFoundError:
            self._set_vocab_gpt2()

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        self._try_set_pooling_type()

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        if self.hf_arch == "Qwen2Model":
            name = f"model.{name}"  # map to Qwen2ForCausalLM tensors
        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("DreamModel")
class DreamModel(TextModel):
    model_arch = gguf.MODEL_ARCH.DREAM

    def get_vocab_base(self) -> tuple[list[str], list[int], str]:
        tokens: list[str] = []
        toktypes: list[int] = []

        from transformers import AutoTokenizer
        tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)

        vocab_dict = tokenizer.get_vocab()  # ty: ignore[unresolved-attribute]
        vocab_size = self.hparams.get("vocab_size", len(vocab_dict))
        assert max(vocab_dict.values()) < vocab_size

        tokpre = self.get_vocab_base_pre(tokenizer)

        reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in vocab_dict.items()}
        added_vocab = tokenizer.get_added_vocab()  # ty: ignore[unresolved-attribute]

        for i in range(vocab_size):
            if i not in reverse_vocab:
                tokens.append(f"[PAD{i}]")
                toktypes.append(gguf.TokenType.UNUSED)
            elif reverse_vocab[i] in added_vocab:
                tokens.append(reverse_vocab[i])
                # Check if it's a special token - treat special tokens as CONTROL tokens
                if hasattr(tokenizer, 'added_tokens_decoder') and i in tokenizer.added_tokens_decoder:
                    if tokenizer.added_tokens_decoder[i].special:
                        toktypes.append(gguf.TokenType.CONTROL)
                    else:
                        toktypes.append(gguf.TokenType.USER_DEFINED)
                else:
                    # Fallback: treat all added vocab as control tokens for special tokens like <|im_start|>
                    toktypes.append(gguf.TokenType.CONTROL)
            else:
                tokens.append(reverse_vocab[i])
                toktypes.append(gguf.TokenType.NORMAL)

        return tokens, toktypes, tokpre

    def set_vocab(self):
        try:
            self._set_vocab_sentencepiece()
        except FileNotFoundError:
            self._set_vocab_gpt2()

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        self._try_set_pooling_type()

        # Dream models use non-causal attention for diffusion
        self.gguf_writer.add_causal_attention(False)

        # Add Dream-specific parameters
        mask_token_id = self.hparams.get("mask_token_id")
        if mask_token_id is not None:
            self.gguf_writer.add_mask_token_id(mask_token_id)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        # Dream model tensors should be mapped directly since it's the base model
        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("LLaDAModelLM")
class LLaDAModel(TextModel):
    model_arch = gguf.MODEL_ARCH.LLADA
    undo_permute = True

    def get_vocab_base(self) -> tuple[list[str], list[int], str]:
        tokens: list[str] = []
        toktypes: list[int] = []

        from transformers import AutoTokenizer
        tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)

        vocab_dict = tokenizer.get_vocab()  # ty: ignore[unresolved-attribute]
        vocab_size = self.hparams.get("vocab_size", len(vocab_dict))
        assert max(vocab_dict.values()) < vocab_size

        tokpre = self.get_vocab_base_pre(tokenizer)

        reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in vocab_dict.items()}
        added_vocab = tokenizer.get_added_vocab()  # ty: ignore[unresolved-attribute]

        for i in range(vocab_size):
            if i not in reverse_vocab:
                tokens.append(f"[PAD{i}]")
                toktypes.append(gguf.TokenType.UNUSED)
            elif reverse_vocab[i] in added_vocab:
                tokens.append(reverse_vocab[i])
                # Check if it's a special token - treat special tokens as CONTROL tokens
                if hasattr(tokenizer, 'added_tokens_decoder') and i in tokenizer.added_tokens_decoder:
                    if tokenizer.added_tokens_decoder[i].special:
                        toktypes.append(gguf.TokenType.CONTROL)
                    else:
                        toktypes.append(gguf.TokenType.USER_DEFINED)
                else:
                    # Fallback: treat all added vocab as control tokens for special tokens like <|im_start|>
                    toktypes.append(gguf.TokenType.CONTROL)
            else:
                tokens.append(reverse_vocab[i])
                toktypes.append(gguf.TokenType.NORMAL)

        return tokens, toktypes, tokpre

    def set_vocab(self):
        self._set_vocab_gpt2()

        # LLaDA specific parameters
        self.gguf_writer.add_add_bos_token(True)

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        self._try_set_pooling_type()

        # Add parameters similar to LlamaModel
        hparams = self.hparams
        self.gguf_writer.add_vocab_size(hparams["vocab_size"])

        if (rope_dim := hparams.get("head_dim")) is None:
            n_heads = hparams.get("num_attention_heads", hparams.get("n_heads"))
            assert n_heads is not None
            rope_dim = hparams.get("hidden_size", hparams.get("d_model")) // n_heads
        self.gguf_writer.add_rope_dimension_count(rope_dim)

        # Set context length for LLaDA
        context_length = self.hparams.get("max_sequence_length", 4096)
        self.gguf_writer.add_context_length(context_length)

        # Set embedding length (dimension size)
        embedding_length = self.hparams.get("d_model", 4096)
        self.gguf_writer.add_embedding_length(embedding_length)

        # Set feed forward length (MLP hidden size)
        feed_forward_length = self.hparams.get("mlp_hidden_size", 12288)
        self.gguf_writer.add_feed_forward_length(feed_forward_length)

        # LLaDA models use non-causal attention for diffusion, similar to Dream
        self.gguf_writer.add_causal_attention(False)

        # LLaDA models don't shift their logits
        self.gguf_writer.add_diffusion_shift_logits(False)

    @staticmethod
    def permute(weights: Tensor, n_head: int, n_head_kv: int | None):
        if n_head_kv is not None and n_head != n_head_kv:
            n_head = n_head_kv
        return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
                .swapaxes(1, 2)
                .reshape(weights.shape))

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        n_head = self.hparams.get("num_attention_heads", self.hparams.get("n_heads"))
        assert n_head is not None
        n_kv_head = self.hparams.get("num_key_value_heads", self.hparams.get("n_kv_heads"))

        if self.undo_permute:
            if name.endswith(("q_proj.weight", "q_proj.bias")):
                data_torch = LLaDAModel.permute(data_torch, n_head, n_head)
            if name.endswith(("k_proj.weight", "k_proj.bias")):
                data_torch = LLaDAModel.permute(data_torch, n_head, n_kv_head)

        # LLaDA model tensors should be mapped directly since it's the base model
        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("Ernie4_5_ForCausalLM", "Ernie4_5ForCausalLM")
class Ernie4_5Model(TextModel):
    model_arch = gguf.MODEL_ARCH.ERNIE4_5

    def set_vocab(self):
        self._set_vocab_sentencepiece()

        tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
        if tokenizer_config_file.is_file():
            with open(tokenizer_config_file, "r", encoding="utf-8") as f:
                tokenizer_config_json = json.load(f)
                if "add_prefix_space" in tokenizer_config_json:
                    self.gguf_writer.add_add_space_prefix(tokenizer_config_json["add_prefix_space"])

    def set_gguf_parameters(self):
        super().set_gguf_parameters()

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        if "ernie." in name:
            name = name.replace("ernie.", "model.")

        return super().filter_tensors((name, gen))

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        num_heads = self.hparams["num_attention_heads"]
        num_kv_heads = self.hparams["num_key_value_heads"]
        if (head_dim := self.hparams.get("head_dim")) is None:
            head_dim = self.hparams["hidden_size"] // num_heads

        # split the qkv weights
        # qkv_proj shape: [(num_heads + 2 * num_kv_heads) * head_dim, hidden_size]
        if "qkv_proj" in name:
            name_q = name.replace("qkv_proj.weight", "q_proj.weight")
            name_k = name.replace("qkv_proj.weight", "k_proj.weight")
            name_v = name.replace("qkv_proj.weight", "v_proj.weight")
            total_q_dim = num_heads * head_dim
            total_k_dim = num_kv_heads * head_dim
            total_v_dim = num_kv_heads * head_dim
            q_proj_weight, k_proj_weight, v_proj_weight = data_torch.split([total_q_dim, total_k_dim, total_v_dim], dim=0)
            yield from super().modify_tensors(q_proj_weight, name_q, bid)
            yield from super().modify_tensors(k_proj_weight, name_k, bid)
            yield from super().modify_tensors(v_proj_weight, name_v, bid)
        # split the up_gate_proj into gate and up
        # up_gate_proj shape: [2 * intermediate_size, hidden_size]
        elif "up_gate_proj" in name:
            name_up = name.replace("up_gate_proj.weight", "up_proj.weight")
            name_gate = name.replace("up_gate_proj.weight", "gate_proj.weight")
            dim_half = data_torch.shape[0] // 2
            gate_proj_weight, up_proj_weight = data_torch.split(dim_half, dim=0)
            yield from super().modify_tensors(gate_proj_weight, name_gate, bid)
            yield from super().modify_tensors(up_proj_weight, name_up, bid)
        else:
            yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("Ernie4_5_MoeForCausalLM")
class Ernie4_5MoeModel(Ernie4_5Model):
    model_arch = gguf.MODEL_ARCH.ERNIE4_5_MOE
    _experts: list[dict[str, Tensor]] | None = None

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self._experts = [{} for _ in range(self.block_count)]

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        self.gguf_writer.add_expert_count(self.hparams["moe_num_experts"])
        self.gguf_writer.add_expert_used_count(self.hparams["moe_k"])
        self.gguf_writer.add_interleave_moe_layer_step(self.hparams["moe_layer_interval"])
        self.gguf_writer.add_leading_dense_block_count(self.hparams["moe_layer_start_index"])
        if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None:
            self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
        if (shared_expert_count := self.hparams.get('moe_num_shared_experts')) is not None:
            self.gguf_writer.add_expert_shared_count(shared_expert_count)
            if shared_expert_count > 0 and (shared_expert_intermediate_size := self.hparams.get('intermediate_size')) is not None and (num_key_value_heads := self.hparams.get('num_key_value_heads')) is not None:
                self.gguf_writer.add_expert_shared_feed_forward_length(shared_expert_intermediate_size // num_key_value_heads)

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        # skip Multi-Token Prediction (MTP) layers (again, same as DeepseekV2)
        match = re.match(r"model.mtp_block.(\d+)", name)
        if match:
            return None

        # skip all other MTP tensors for now
        match = re.match(r"model.mtp_emb_norm.(\d+)", name)
        if match:
            return None

        match = re.match(r"model.mtp_hidden_norm.(\d+)", name)
        if match:
            return None

        match = re.match(r"model.mtp_linear_proj.(\d+)", name)
        if match:
            return None

        return super().filter_tensors(item)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        # process the experts separately
        if name.find("mlp.experts") != -1:
            n_experts = self.hparams["moe_num_experts"]
            assert bid is not None

            if self._experts is None:
                self._experts = [{} for _ in range(self.block_count)]

            self._experts[bid][name] = data_torch

            if len(self._experts[bid]) >= n_experts * 3:
                # merge the experts into a single 3d tensor
                for w_name in ["gate_proj", "up_proj", "down_proj"]:
                    datas: list[Tensor] = []

                    for xid in range(n_experts):
                        ename_to_retrieve = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
                        datas.append(self._experts[bid][ename_to_retrieve])
                        del self._experts[bid][ename_to_retrieve]

                    data_torch = torch.stack(datas, dim=0)
                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
                    yield from super().modify_tensors(data_torch, merged_name, bid)
        else:
            yield from ModelBase.modify_tensors(self, data_torch, name, bid)

    def prepare_tensors(self):
        super().prepare_tensors()

        if self._experts is not None:
            # flatten `list[dict[str, Tensor]]` into `list[str]`
            experts = [k for d in self._experts for k in d.keys()]
            if len(experts) > 0:
                raise ValueError(f"Unprocessed experts: {experts}")


@ModelBase.register("PaddleOCRVLForConditionalGeneration")
class PaddleOCRModel(Ernie4_5Model):
    model_arch = gguf.MODEL_ARCH.PADDLEOCR


@ModelBase.register("PaddleOCRVisionModel")
class PaddleOCRVisionModel(MmprojModel):
    # PaddleOCR-VL uses a modified version of Siglip
    min_pixels: int = 0
    max_pixels: int = 0

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        assert self.hparams_vision is not None
        self.min_pixels = self.preprocessor_config["min_pixels"]
        self.max_pixels = self.preprocessor_config["max_pixels"]
        self.hparams_vision["image_size"] = int(math.sqrt(self.max_pixels))

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        assert self.hparams_vision is not None
        hparams = self.hparams_vision
        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.PADDLEOCR)
        self.gguf_writer.add_vision_max_pixels(self.max_pixels)
        self.gguf_writer.add_vision_min_pixels(self.min_pixels)
        self.gguf_writer.add_vision_use_gelu(True)
        self.gguf_writer.add_vision_attention_layernorm_eps(hparams.get("rms_norm_eps", 1e-6))

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        if "vision_model" not in name and "mlp_AR" not in name:
            return None
        name = name.replace("visual.", "model.")
        if "packing_position_embedding" in name:
            # unused
            return None
        if "vision_model.head" in name:
            # we don't yet support image embeddings for this model
            return None

        return super().filter_tensors((name, gen))


@ModelBase.register(
    "Qwen2VLModel",
    "Qwen2VLForConditionalGeneration",
    "Qwen2_5_VLForConditionalGeneration",
    "Qwen2_5OmniModel",
)
class Qwen2VLModel(TextModel):
    model_arch = gguf.MODEL_ARCH.QWEN2VL

    def set_gguf_parameters(self):
        super().set_gguf_parameters()

    def set_vocab(self):
        try:
            self._set_vocab_sentencepiece()
        except FileNotFoundError:
            self._set_vocab_gpt2()

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        if name.startswith("thinker."):
            name = name.replace("thinker.", "")

        return super().filter_tensors((name, gen))


@ModelBase.register("Qwen2VLModel", "Qwen2VLForConditionalGeneration", "Qwen2_5_VLForConditionalGeneration")
class Qwen2VLVisionModel(MmprojModel):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        assert self.hparams_vision is not None
        self.hparams_vision["image_size"] = self.hparams_vision.get("image_size", 560)
        # rename config.json values
        self.hparams_vision["num_attention_heads"] = self.hparams_vision.get("num_heads")
        self.hparams_vision["num_hidden_layers"] = self.hparams_vision.get("depth")
        if "embed_dim" in self.hparams_vision: # qwen2vl
            self.hparams_vision["intermediate_size"] = self.hparams_vision.get("hidden_size")
            self.hparams_vision["hidden_size"] = self.hparams_vision.get("embed_dim")

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        assert self.hparams_vision is not None
        hparams = self.hparams_vision
        model_type = self.global_config['model_type']
        if model_type == 'qwen2_vl':
            self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.QWEN2VL)
        elif model_type == 'qwen2_5_vl' or model_type == 'qwen2_5_omni':
            if model_type == 'qwen2_5_omni':
                self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.QWEN25O)
            else:
                self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.QWEN25VL)
            self.gguf_writer.add_vision_use_silu(True)
            # find n_wa_pattern (window attention pattern)
            fullatt_block_indexes = hparams.get("fullatt_block_indexes")
            assert fullatt_block_indexes is not None, "fullatt_block_indexes is required for qwen2_5_vl"
            n_wa_pattern = fullatt_block_indexes[0] + 1
            # validate n_wa_pattern
            for i in range(1, len(fullatt_block_indexes)):
                if fullatt_block_indexes[i] - fullatt_block_indexes[i - 1] != n_wa_pattern:
                    raise ValueError(f"Invalid fullatt_block_indexes: {fullatt_block_indexes}")
            self.gguf_writer.add_vision_n_wa_pattern(n_wa_pattern)
        else:
            raise ValueError(f"Unknown QwenVL model type: {self.global_config['model_type']}")
        # default values below are taken from HF tranformers code
        self.gguf_writer.add_vision_attention_layernorm_eps(self.global_config.get("rms_norm_eps", 1e-6))

    def tensor_force_quant(self, name, new_name, bid, n_dims):
        if ".position_embd." in new_name:
            return gguf.GGMLQuantizationType.F32
        return super().tensor_force_quant(name, new_name, bid, n_dims)

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        if not name.startswith("visual."):
            return None

        return super().filter_tensors(item)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        # split QKV tensors if needed
        if ".qkv." in name:
            if data_torch.ndim == 2: # weight
                c3, _ = data_torch.shape
            else: # bias
                c3 = data_torch.shape[0]
            assert c3 % 3 == 0
            c = c3 // 3
            wq = data_torch[:c]
            wk = data_torch[c: c * 2]
            wv = data_torch[c * 2:]
            yield from super().modify_tensors(wq, name.replace("qkv", "q"), bid)
            yield from super().modify_tensors(wk, name.replace("qkv", "k"), bid)
            yield from super().modify_tensors(wv, name.replace("qkv", "v"), bid)
        elif 'patch_embed.proj.weight' in name:
            # split Conv3D into Conv2Ds
            c1, c2, kt, kh, kw = data_torch.shape
            del c1, c2, kh, kw  # unused
            assert kt == 2, "Current implementation only support temporal_patch_size of 2"
            yield (gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_ENC_EMBD_PATCH] + ".weight"  , data_torch[:, :, 0, ...])
            yield (gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_ENC_EMBD_PATCH] + ".weight.1", data_torch[:, :, 1, ...])
        else:
            yield from super().modify_tensors(data_torch, name, bid)


class Qwen25AudioModel(MmprojModel):
    has_audio_encoder = True

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        assert self.hparams_audio is not None
        self.hparams_audio["hidden_size"] = self.hparams_audio["d_model"]
        self.hparams_audio["intermediate_size"] = self.hparams_audio["encoder_ffn_dim"]
        self.hparams_audio["num_attention_heads"] = self.hparams_audio["encoder_attention_heads"]

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        assert self.hparams_audio is not None
        self.gguf_writer.add_audio_num_mel_bins(self.hparams_audio["num_mel_bins"])
        self.gguf_writer.add_audio_attention_layernorm_eps(self.hparams_audio.get("layer_norm_eps", 1e-5))

    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
        # SinusoidsPositionEmbedding
        assert self.hparams_audio is not None
        max_timescale = 10000
        length = 1500
        channels = self.hparams_audio["hidden_size"]
        log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1)
        inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2).float())
        scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :]
        pos_embd = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1).to(dtype=torch.float32)
        yield ("audio_tower.embed_positions.weight", pos_embd)

    def tensor_force_quant(self, name, new_name, bid, n_dims):
        if ".conv" in name and ".weight" in name:
            return gguf.GGMLQuantizationType.F16
        return super().tensor_force_quant(name, new_name, bid, n_dims)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        if "conv1.bias" in name or "conv2.bias" in name:
            # transpose conv1 and conv2 bias
            data_torch = data_torch.unsqueeze(-1)

        yield from MmprojModel.modify_tensors(self, data_torch, name, bid)


@ModelBase.register("Qwen2_5OmniModel")
class Qwen25OmniModel(Qwen2VLVisionModel, Qwen25AudioModel):
    has_audio_encoder = True
    has_vision_encoder = True

    def get_vision_config(self) -> dict[str, Any] | None:
        return self.global_config["thinker_config"].get("vision_config")

    def get_audio_config(self) -> dict[str, Any] | None:
        return self.global_config["thinker_config"].get("audio_config")

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.QWEN25O)

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        if not name.startswith("visual.") and not name.startswith("audio_tower."):
            return None

        if name.startswith("thinker."):
            name = name.replace("thinker.", "")

        if "audio_bos_eos_token" in name:
            # this tensor is left unused in transformers code
            # https://github.com/huggingface/transformers/blob/6e3063422c4b1c014aa60c32b9254fd2902f0f28/src/transformers/models/qwen2_5_omni/modular_qwen2_5_omni.py#L1809
            return None

        return MmprojModel.filter_tensors((name, gen))

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        if "visual." in name:
            yield from Qwen2VLVisionModel.modify_tensors(self, data_torch, name, bid)
        elif "audio_tower." in name:
            yield from Qwen25AudioModel.modify_tensors(self, data_torch, name, bid)
        return  # skip other tensors


@ModelBase.register("InternVisionModel")
class InternVisionModel(MmprojModel):

    min_dynamic_tiles: int = 0
    max_dynamic_tiles: int = 0

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        assert self.hparams_vision is not None
        self.min_dynamic_tiles = self.global_config.get("min_dynamic_patch", 0)
        self.max_dynamic_tiles = self.global_config.get("max_dynamic_patch", 0)

    def set_gguf_parameters(self):
        assert self.hparams_vision is not None
        if isinstance(self.hparams_vision['image_size'], list):
            self.hparams_vision['image_size'] = self.hparams_vision['image_size'][0]
        if isinstance(self.hparams_vision['patch_size'], list):
            self.hparams_vision['patch_size'] = self.hparams_vision['patch_size'][0]
        super().set_gguf_parameters()

        hparams = self.hparams
        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.INTERNVL)
        self.gguf_writer.add_vision_attention_layernorm_eps(hparams["layer_norm_eps"])
        # hidden_act
        if hparams["hidden_act"] == "silu":
            self.gguf_writer.add_vision_use_silu(True)
        elif hparams["hidden_act"] == "gelu":
            self.gguf_writer.add_vision_use_gelu(True)
        else:
            raise ValueError(f"Unsupported hidden_act: {hparams['hidden_act']}")
        # downsample_ratio
        downsample_ratio = self.global_config.get("downsample_ratio")
        assert downsample_ratio is not None
        self.gguf_writer.add_vision_projector_scale_factor(int(1.0 / downsample_ratio))
        # older models may not have min/max_dynamic_patch in config
        if self.min_dynamic_tiles > 0:
            self.gguf_writer.add_vision_preproc_min_tiles(self.min_dynamic_tiles)
        if self.max_dynamic_tiles > 0:
            self.gguf_writer.add_vision_preproc_max_tiles(self.max_dynamic_tiles)

    def tensor_force_quant(self, name, new_name, bid, n_dims):
        if ".position_embd." in new_name:
            return gguf.GGMLQuantizationType.F32
        return super().tensor_force_quant(name, new_name, bid, n_dims)

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        vision_prefix = ['vision_model', 'mlp', 'model.vision_tower', 'model.multi_modal_projector']
        if not any([name.startswith(prefix) for prefix in vision_prefix]):
            return None
        # deal with intern-s1 special case
        names_map = {
            "model.multi_modal_projector.layer_norm.bias": "mlp1.0.bias",
            "model.multi_modal_projector.layer_norm.weight": "mlp1.0.weight",
            "model.multi_modal_projector.linear_1.bias": "mlp1.1.bias",
            "model.multi_modal_projector.linear_1.weight": "mlp1.1.weight",
            "model.multi_modal_projector.linear_2.bias": "mlp1.3.bias",
            "model.multi_modal_projector.linear_2.weight": "mlp1.3.weight",
        }
        if name in names_map:
            name = names_map[name]
        # correct name
        if name.startswith("vision_model"):
            name = "vision_tower." + name
        if (".ls" in name or ".lambda_" in name or "position_embedding" in name) and not name.endswith(".weight"):
            name += ".weight"

        return super().filter_tensors((name, gen))

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        # split QKV tensors if needed
        if ".qkv." in name:
            if data_torch.ndim == 2: # weight
                c3, _ = data_torch.shape
            else: # bias
                c3 = data_torch.shape[0]
            assert c3 % 3 == 0
            c = c3 // 3
            wq = data_torch[:c]
            wk = data_torch[c: c * 2]
            wv = data_torch[c * 2:]
            yield from super().modify_tensors(wq, name.replace("attn.qkv", "self_attn.q_proj"), bid)
            yield from super().modify_tensors(wk, name.replace("attn.qkv", "self_attn.k_proj"), bid)
            yield from super().modify_tensors(wv, name.replace("attn.qkv", "self_attn.v_proj"), bid)
        else:
            yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register(
    "NemotronH_Nano_VL_V2",
    "RADIOModel",
)
class NemotronNanoV2VLModel(MmprojModel):
    # ViT-Huge architecture parameters for RADIO v2.5-h
    _vit_hidden_size = 1280
    _vit_intermediate_size = 5120
    _vit_num_layers = 32
    _vit_num_heads = 16

    def get_vision_config(self) -> dict[str, Any] | None:
        # RADIO config doesn't have standard ViT parameters, so they need to be constructed manually
        vision_config = self.global_config.get("vision_config")
        if vision_config is None:
            return None
        # Add ViT-H parameters
        vision_config = {
            **vision_config,
            "hidden_size": self._vit_hidden_size,
            "intermediate_size": self._vit_intermediate_size,
            "num_hidden_layers": self._vit_num_layers,
            "num_attention_heads": self._vit_num_heads,
            "image_size": self.global_config.get("force_image_size", 512),
        }
        return vision_config

    def set_gguf_parameters(self):
        if "image_mean" not in self.preprocessor_config:
            self.preprocessor_config["image_mean"] = [0.485, 0.456, 0.406]
        if "image_std" not in self.preprocessor_config:
            self.preprocessor_config["image_std"] = [0.229, 0.224, 0.225]

        super().set_gguf_parameters()
        hparams = self.global_config
        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.NEMOTRON_V2_VL)
        self.gguf_writer.add_vision_attention_layernorm_eps(1e-6)
        self.gguf_writer.add_vision_use_gelu(True)
        downsample_ratio = hparams.get("downsample_ratio", 0.5)
        self.gguf_writer.add_vision_projector_scale_factor(int(1.0 / downsample_ratio))

    def tensor_force_quant(self, name, new_name, bid, n_dims):
        if ".position_embd." in new_name or "pos_embed" in new_name:
            return gguf.GGMLQuantizationType.F32
        return super().tensor_force_quant(name, new_name, bid, n_dims)

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        if "input_conditioner" in name:
            return None

        # mtmd does not support video yet so skip tensors related to video.
        if "radio_model.model.patch_generator.video_embedder" in name:
            return None

        if not name.startswith("vision_model.radio_model.model.") and not name.startswith("mlp1."):
            return None

        if "patch_generator.pos_embed" in name:
            if not name.endswith(".weight"):
                name += ".weight"

        return super().filter_tensors((name, gen))

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        # RADIO's pos_embed doesn't have .weight suffix, but clip.cpp expects it
        if "patch_generator.pos_embed" in name:
            # Downsample position embeddings for fixed 512x512 image size
            import torch.nn.functional as F
            n_embd = self.hparams["hidden_size"]
            image_size = self.global_config.get("force_image_size", 512)
            patch_size = self.hparams["patch_size"]
            target_patches_per_side = image_size // patch_size  # 32
            max_patches_per_side = int((data_torch.shape[1]) ** 0.5)  # 128
            if target_patches_per_side != max_patches_per_side:
                # Reshape to grid, interpolate, flatten back
                data_torch = data_torch.reshape(1, max_patches_per_side, max_patches_per_side, n_embd)
                data_torch = data_torch.permute(0, 3, 1, 2).float()  # [1, n_embd, 128, 128]
                data_torch = F.interpolate(data_torch, size=(target_patches_per_side, target_patches_per_side),
                                           mode='bilinear', align_corners=True)
                data_torch = data_torch.permute(0, 2, 3, 1)  # [1, 32, 32, n_embd]
                data_torch = data_torch.reshape(1, target_patches_per_side * target_patches_per_side, n_embd)

        # Reshape linear patch embedding to conv2d format for ggml_conv_2d
        # From [n_embd, patch_size*patch_size*3] to [n_embd, 3, patch_size, patch_size]
        if "patch_generator.embedder" in name:
            patch_size = self.hparams["patch_size"]
            n_embd = self.hparams["hidden_size"]
            data_torch = data_torch.reshape(n_embd, 3, patch_size, patch_size)

        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("WavTokenizerDec")
class WavTokenizerDecModel(TextModel):
    model_arch = gguf.MODEL_ARCH.WAVTOKENIZER_DEC

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        if \
                name.endswith("codebook.cluster_size") or \
                name.endswith("codebook.embed_avg") or \
                name.endswith("codebook.inited"):
            logger.debug(f"Skipping {name!r}")
            return None

        return super().filter_tensors(item)

    def set_vocab(self):
        self._set_vocab_none()

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        self.gguf_writer.add_vocab_size         (self.hparams["vocab_size"])
        self.gguf_writer.add_features_length    (self.hparams["n_embd_features"])
        self.gguf_writer.add_feed_forward_length(self.hparams["n_ff"])
        self.gguf_writer.add_group_norm_eps     (self.hparams["group_norm_epsilon"])
        self.gguf_writer.add_group_norm_groups  (self.hparams["group_norm_groups"])

        self.gguf_writer.add_posnet_embedding_length(self.hparams["posnet"]["n_embd"])
        self.gguf_writer.add_posnet_block_count     (self.hparams["posnet"]["n_layer"])

        self.gguf_writer.add_convnext_embedding_length(self.hparams["convnext"]["n_embd"])
        self.gguf_writer.add_convnext_block_count     (self.hparams["convnext"]["n_layer"])

        self.gguf_writer.add_causal_attention(False)


@ModelBase.register("Qwen2MoeForCausalLM")
class Qwen2MoeModel(TextModel):
    model_arch = gguf.MODEL_ARCH.QWEN2MOE

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None:
            self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
            logger.info(f"gguf: expert feed forward length = {moe_intermediate_size}")
        if (shared_expert_intermediate_size := self.hparams.get('shared_expert_intermediate_size')) is not None:
            self.gguf_writer.add_expert_shared_feed_forward_length(shared_expert_intermediate_size)
            logger.info(f"gguf: expert shared feed forward length = {shared_expert_intermediate_size}")

    _experts: list[dict[str, Tensor]] | None = None

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        # handle aggregated expert tensors
        # GGUF stores dimensions reversed from PyTorch, so:
        # PyTorch (A,B,C) -> GGUF writes [C,B,A] -> GGML reads ne={C,B,A}
        # Input shapes from HF: (n_expert, n_ff_exp, n_embd) or (n_expert, n_embd, n_ff_exp)
        # Expected GGML ne: {n_embd, n_ff_exp, n_expert} for gate/up, {n_ff_exp, n_embd, n_expert} for down
        if name.endswith("mlp.experts.down_proj") or name.endswith("mlp.experts.down_proj.weight"):
            mapped = f"{name}.weight" if not name.endswith(".weight") else name
            # HF: [n_expert, n_embd, n_ff] -> GGML: {n_ff, n_embd, n_expert}
            yield from super().modify_tensors(data_torch, mapped, bid)
            return

        if name.endswith("mlp.experts.gate_up_proj") or name.endswith("mlp.experts.gate_up_proj.weight"):
            if data_torch.ndim < 3 or data_torch.shape[-2] % 2 != 0:
                raise ValueError(f"Unexpected gate_up_proj shape for {name}: {tuple(data_torch.shape)}")
            # HF: [n_expert, 2*n_ff, n_embd] -> split on dim=-2
            n_ff = data_torch.shape[-2] // 2
            gate = data_torch[..., :n_ff, :].contiguous()
            up = data_torch[..., n_ff:, :].contiguous()
            # gate/up: [n_expert, n_ff, n_embd] -> GGML: {n_embd, n_ff, n_expert}
            base_name = name.removesuffix(".weight").removesuffix(".gate_up_proj")
            mapped_gate = f"{base_name}.gate_proj.weight"
            mapped_up = f"{base_name}.up_proj.weight"
            yield from super().modify_tensors(gate, mapped_gate, bid)
            yield from super().modify_tensors(up, mapped_up, bid)
            return

        if name.find("experts") != -1:
            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
            assert bid is not None

            if self._experts is None:
                self._experts = [{} for _ in range(self.block_count)]

            self._experts[bid][name] = data_torch

            if len(self._experts[bid]) >= n_experts * 3:
                # merge the experts into a single 3d tensor
                for w_name in ["down_proj", "gate_proj", "up_proj"]:
                    datas: list[Tensor] = []

                    for xid in range(n_experts):
                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
                        datas.append(self._experts[bid][ename])
                        del self._experts[bid][ename]

                    data_torch = torch.stack(datas, dim=0)

                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"

                    yield from super().modify_tensors(data_torch, merged_name, bid)
                return
            else:
                return

        yield from super().modify_tensors(data_torch, name, bid)

    def prepare_tensors(self):
        super().prepare_tensors()

        if self._experts is not None:
            # flatten `list[dict[str, Tensor]]` into `list[str]`
            experts = [k for d in self._experts for k in d.keys()]
            if len(experts) > 0:
                raise ValueError(f"Unprocessed experts: {experts}")


@ModelBase.register("Qwen3ForCausalLM", "Qwen3Model")
class Qwen3Model(Qwen2Model):
    model_arch = gguf.MODEL_ARCH.QWEN3

    # extra logic for rerank models
    is_rerank: bool = False
    is_tied_embeddings: bool = False
    token_false_id: int | None = None
    token_true_id: int | None = None

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

        # track for intern-s1-mini
        hparams = ModelBase.load_hparams(self.dir_model, is_mistral_format=False)
        self.origin_hf_arch = hparams.get('architectures', [None])[0]

        if self._is_qwen3_reranker():
            self._find_rerank_config()

    def _is_qwen3_reranker(self) -> bool:
        readme_path = self.dir_model / "README.md"
        readme_text = ""
        if readme_path.exists():
            with readme_path.open("r", encoding="utf-8") as f:
                readme_text = f.read()

        name_hints = [
            str(self.dir_model.name),
            str(self.hparams.get("_name_or_path", "")),
            str(self.hparams.get("model_type", "")),
            str(self.origin_hf_arch or ""),
        ]
        name_hints = [hint.lower() for hint in name_hints if hint]

        if "# qwen3-reranker" in readme_text.lower() or "# qwen3-vl-reranker" in readme_text.lower():
            return True

        if any("qwen3-reranker" in hint or "qwen3-vl-reranker" in hint for hint in name_hints):
            return True

        return "sequenceclassification" in (self.origin_hf_arch or "").lower()

    def set_vocab(self):
        # deal with intern-s1-mini
        if self.origin_hf_arch == 'InternS1ForConditionalGeneration':
            self._set_vocab_interns1()
            return

        super().set_vocab()

    def _find_rerank_config(self):
        from transformers import AutoTokenizer
        tokenizer = AutoTokenizer.from_pretrained(self.dir_model)

        self.is_rerank = True
        self.is_tied_embeddings = self.hparams.get("tie_word_embeddings", False)
        self.token_false_id = tokenizer.convert_tokens_to_ids("no")  # ty: ignore[unresolved-attribute, invalid-assignment]
        self.token_true_id = tokenizer.convert_tokens_to_ids("yes")  # ty: ignore[unresolved-attribute, invalid-assignment]
        self.sep_token_id = tokenizer.convert_tokens_to_ids("|")  # ty: ignore[unresolved-attribute]

        assert self.token_false_id is not None and self.token_true_id is not None

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        if self.is_rerank:
            self.gguf_writer.add_pooling_type(gguf.PoolingType.RANK)
            self.gguf_writer.add_classifier_output_labels(["yes", "no"])
            self.gguf_writer.add_chat_template([{
                "name": "rerank",
                "template": "<|im_start|>system\nJudge whether the Document meets the requirements based on the Query and the Instruct provided. Note that the answer can only be \"yes\" or \"no\".<|im_end|>\n"
                            "<|im_start|>user\n<Instruct>: Given a web search query, retrieve relevant passages that answer the query\n<Query>: {query}\n<Document>: {document}<|im_end|>\n"
                            "<|im_start|>assistant\n<think>\n\n</think>\n\n"
            }])

    def _get_cls_out_tensor(self, data_torch: Tensor) -> Tensor:
        # extract "yes" and "no" tokens from the output lm_head tensor
        false_row = data_torch[self.token_false_id]
        true_row = data_torch[self.token_true_id]
        return torch.stack([true_row, false_row], dim=0)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        if self.is_rerank:
            is_tied_head = self.is_tied_embeddings and "embed_tokens" in name
            is_real_head = not self.is_tied_embeddings and "lm_head" in name
            if is_tied_head or is_real_head:
                cls_out_head = (
                    gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.CLS_OUT] + ".weight",
                    self._get_cls_out_tensor(data_torch),
                )
                yield cls_out_head
                if is_tied_head:
                    yield from super().modify_tensors(data_torch, name, bid)
                return

        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("Qwen3MoeForCausalLM")
class Qwen3MoeModel(Qwen2MoeModel):
    model_arch = gguf.MODEL_ARCH.QWEN3MOE

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        hparams = ModelBase.load_hparams(self.dir_model, False)
        self.origin_hf_arch = hparams.get('architectures', [None])[0]

    def set_vocab(self):
        # deal with intern-s1
        if self.origin_hf_arch == 'InternS1ForConditionalGeneration':
            self._set_vocab_interns1()
            return

        super().set_vocab()


@ModelBase.register("Qwen3NextForCausalLM")
class Qwen3NextModel(Qwen2MoeModel):
    model_arch = gguf.MODEL_ARCH.QWEN3NEXT

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        self.gguf_writer.add_ssm_conv_kernel(self.hparams["linear_conv_kernel_dim"])
        self.gguf_writer.add_ssm_state_size(self.hparams["linear_key_head_dim"])
        self.gguf_writer.add_ssm_group_count(self.hparams["linear_num_key_heads"])
        self.gguf_writer.add_ssm_time_step_rank(self.hparams["linear_num_value_heads"])
        self.gguf_writer.add_ssm_inner_size(self.hparams["linear_value_head_dim"] * self.hparams["linear_num_value_heads"])
        self.gguf_writer.add_full_attention_interval(self.hparams.get("full_attention_interval", 4))
        if (rope_dim := self.hparams.get("head_dim")) is None:
            rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
        self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.hparams.get("partial_rotary_factor", 0.25)))

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        if name.startswith("mtp"):
            # ignore MTP layers for now
            return None

        return super().filter_tensors(item)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        if name.endswith(".A_log"):
            data_torch = -torch.exp(data_torch)
        elif name.endswith(".dt_bias"):
            name = name.rpartition(".dt_bias")[0] + ".dt_proj.bias"
        elif "conv1d" in name:
            data_torch = data_torch.squeeze()
        elif name.endswith("norm.weight") and not name.endswith("linear_attn.norm.weight"):
            data_torch = data_torch + 1

        if "in_proj_qkvz.weight" in name:
            # original order:  [q, k, v, z] * head_count
            # corrected order: [q * head_count, k * head_count, v * head_count, z * head_count]
            head_k_dim = self.hparams["linear_key_head_dim"]
            head_v_dim = self.hparams["linear_value_head_dim"]
            num_v_heads = self.hparams["linear_num_value_heads"]
            num_k_heads = self.hparams["linear_num_key_heads"]
            hidden_size = self.hparams["hidden_size"]
            split_arg_list_qkvz = [
                head_k_dim, # q partition
                head_k_dim, # k partition
                (num_v_heads // num_k_heads * head_v_dim), # v partition
                (num_v_heads // num_k_heads * head_v_dim), # z partition
            ]
            # view as (n_embd, head_count, [q+k+v+z])
            data_torch = data_torch.permute(1, 0).contiguous()
            data_torch = data_torch.view(-1, num_k_heads, sum(split_arg_list_qkvz))
            # split into q, k, v, z
            q, k, v, z = torch.split(data_torch, split_arg_list_qkvz, dim=-1)
            # flatten dim + head_count
            q = q.contiguous().view(hidden_size, -1)
            k = k.contiguous().view(hidden_size, -1)
            v = v.contiguous().view(hidden_size, -1)
            z = z.contiguous().view(hidden_size, -1)
            # stack back
            qkv = torch.cat([q, k, v], dim=-1).permute(1, 0).contiguous()
            z = z.permute(1, 0).contiguous()
            yield (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_QKV,  bid, ".weight"), qkv)
            yield (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_GATE, bid, ".weight"), z)
        else:
            yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("RND1")
class RND1Model(Qwen2MoeModel):
    model_arch = gguf.MODEL_ARCH.RND1

    def set_gguf_parameters(self):
        super().set_gguf_parameters()

        # RND1 specific parameters
        # RND1 uses bidirectional attention
        self.gguf_writer.add_causal_attention(False)

        if (mask_token_id := self.hparams.get("mask_token_id")) is not None:
            self.gguf_writer.add_mask_token_id(mask_token_id)


@ModelBase.register("Qwen3VLForConditionalGeneration", "Qwen3VLMoeForConditionalGeneration", "Qwen3_5ForConditionalGeneration", "Qwen3_5MoeForConditionalGeneration")
class Qwen3VLVisionModel(MmprojModel):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        if self.hparams_vision is None:
            logger.info("No vision config found, skipping vision tensor processing")
            return

        # Compute image_size if not present
        if "image_size" not in self.hparams_vision:
            # For Qwen3VL/Qwen3VLMoe, compute from num_position_embeddings
            num_pos = self.hparams_vision.get("num_position_embeddings", 2304)
            patch_size = self.hparams_vision.get("patch_size", 16)
            # num_position_embeddings = (image_size / patch_size) ** 2
            # So image_size = sqrt(num_position_embeddings) * patch_size
            image_size = int(num_pos**0.5 * patch_size)
            self.hparams_vision["image_size"] = image_size

        # Rename config values for compatibility
        self.hparams_vision["num_attention_heads"] = self.hparams_vision.get("num_heads")
        self.hparams_vision["num_hidden_layers"] = self.hparams_vision.get("depth")

        self.is_deepstack_layers = [False] * int(self.hparams_vision["num_hidden_layers"] or 0)
        for idx in self.hparams_vision.get("deepstack_visual_indexes", []):
            self.is_deepstack_layers[idx] = True

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        # in case mixed modalities, the arch will be handled by subclass
        if not self.has_audio_encoder:
            self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.QWEN3VL)
        self.gguf_writer.add_vision_use_gelu(True)

        if self.hparams_vision is not None:
            merge_size = self.hparams_vision.get("spatial_merge_size")
            if merge_size is not None:
                self.gguf_writer.add_vision_spatial_merge_size(int(merge_size))

        # Use text config's rms_norm_eps for vision attention layernorm eps
        rms_norm_eps = self.global_config.get("text_config", {}).get("rms_norm_eps", 1e-6)
        self.gguf_writer.add_vision_attention_layernorm_eps(rms_norm_eps)

        if self.is_deepstack_layers:
            self.gguf_writer.add_vision_is_deepstack_layers(self.is_deepstack_layers)

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        # Skip text model tensors
        if name.startswith("lm_head."):
            return None

        # Skip MTP tensors
        if name.startswith("mtp."):
            return None

        if name.startswith("model.visual."):
            name = name.replace("model.visual.", "visual.", 1)

        if not name.startswith("visual."):
            return None

        return super().filter_tensors((name, gen))

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        assert self.hparams_vision is not None

        if name.startswith("visual.deepstack_merger_list."):
            prefix, rest = name.split(".", maxsplit=3)[2:]
            # prefix is the layer index, convert to absolute clip layer index!
            idx = self.hparams_vision.get("deepstack_visual_indexes", [])[int(prefix)]
            target = rest

            tensor_type: gguf.MODEL_TENSOR
            if target.startswith("norm."):
                tensor_type = gguf.MODEL_TENSOR.V_DS_NORM
                suffix = target.split(".", 1)[1]
            elif target.startswith("linear_fc1."):
                tensor_type = gguf.MODEL_TENSOR.V_DS_FC1
                suffix = target.split(".", 1)[1]
            elif target.startswith("linear_fc2."):
                tensor_type = gguf.MODEL_TENSOR.V_DS_FC2
                suffix = target.split(".", 1)[1]
            else:
                raise ValueError(f"Unexpected deepstack tensor: {name}")

            new_name = self.format_tensor_name(tensor_type, idx, suffix=f".{suffix}")
            yield from super().modify_tensors(data_torch, new_name, bid)
            return

        if name.startswith("visual.merger."):
            suffix = name.split(".", 2)[2]
            if suffix.startswith("linear_fc"):
                fc_idx_str, tail = suffix.split(".", 1)
                fc_num = int(fc_idx_str.replace("linear_fc", ""))
                # Qwen3VL has linear_fc1 and linear_fc2
                # Map to indices 0 and 2 (matching Qwen2VL which uses indices 0 and 2)
                if fc_num == 1:
                    fc_idx = 0
                elif fc_num == 2:
                    fc_idx = 2
                else:
                    raise ValueError(f"unexpected fc index {fc_num} in {name}")
                new_name = self.format_tensor_name(gguf.MODEL_TENSOR.V_MMPROJ, fc_idx, suffix=f".{tail}")
            elif suffix.startswith("norm."):
                new_name = self.format_tensor_name(gguf.MODEL_TENSOR.V_POST_NORM, suffix=f".{suffix.split('.', 1)[1]}")
            else:
                raise ValueError(f"Unexpected merger tensor: {name}")
            yield (new_name, data_torch)
            return

        if name == "visual.patch_embed.proj.weight":
            # split Conv3D into Conv2Ds along temporal dimension
            c1, c2, kt, _, _ = data_torch.shape
            del c1, c2
            if kt != 2:
                raise ValueError("Current implementation only supports temporal_patch_size of 2")
            yield (gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_ENC_EMBD_PATCH] + ".weight", data_torch[:, :, 0, ...])
            yield (gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_ENC_EMBD_PATCH] + ".weight.1", data_torch[:, :, 1, ...])
            return

        if name == "visual.patch_embed.proj.bias":
            # Include the bias - it's used by the C++ code
            yield (gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_ENC_EMBD_PATCH] + ".bias", data_torch)
            return

        yield from MmprojModel.modify_tensors(self, data_torch, name, bid)


@ModelBase.register("Qwen3OmniMoeForConditionalGeneration")
class Qwen3OmniMmprojModel(Qwen3VLVisionModel, Qwen25AudioModel):
    has_audio_encoder = True
    has_vision_encoder = True

    def get_vision_config(self) -> dict[str, Any] | None:
        if self.has_vision_encoder:
            return self.global_config["thinker_config"].get("vision_config")
        else:
            return None

    def get_audio_config(self) -> dict[str, Any] | None:
        if self.has_audio_encoder:
            return self.global_config["thinker_config"].get("audio_config")
        else:
            return None

    def set_gguf_parameters(self):
        if self.has_vision_encoder:
            Qwen3VLVisionModel.set_gguf_parameters(self)
            self.gguf_writer.add_clip_vision_projector_type(gguf.VisionProjectorType.QWEN3VL)
        if self.has_audio_encoder:
            Qwen25AudioModel.set_gguf_parameters(self)
            self.gguf_writer.add_clip_audio_projector_type(gguf.VisionProjectorType.QWEN3A)

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        # Skip text model tensors
        if name.startswith("lm_head."):
            return None

        # Skip MTP tensors
        if name.startswith("mtp."):
            return None

        if name.startswith("model.visual."):
            name = name.replace("model.visual.", "visual.", 1)

        if "visual." not in name and "audio_tower." not in name:
            return None

        return MmprojModel.filter_tensors((name, gen))

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        if "visual." in name:
            if not self.has_vision_encoder:
                raise ValueError(f"Model does not have vision encoder, but found tensor {name}")
            # need to transform vision tensor naming, so that modify_tensors() logic can be used correctly
            name = name.replace("thinker.visual.", "model.visual.")
            if ".merger_list." in name:
                name = name.replace(".merger_list.", ".deepstack_merger_list.")
                name = name.replace(".ln_q", ".norm")
                name = name.replace(".mlp.0", ".linear_fc1")
                name = name.replace(".mlp.2", ".linear_fc2")
            elif ".merger." in name:
                name = name.replace(".ln_q", ".norm")
                name = name.replace(".mlp.0", ".linear_fc1")
                name = name.replace(".mlp.2", ".linear_fc2")
            yield from Qwen3VLVisionModel.modify_tensors(self, data_torch, name, bid)
        elif "audio_tower." in name:
            if not self.has_audio_encoder:
                raise ValueError(f"Model does not have audio encoder, but found tensor {name}")
            if "conv2d" in name and name.endswith(".bias"):
                # transform conv2d bias [n_embd] --> [1, 1, n_embd]
                data_torch = data_torch.unsqueeze(-1).unsqueeze(-1)
            yield from Qwen25AudioModel.modify_tensors(self, data_torch, name, bid)


@ModelBase.register("Qwen3ASRForConditionalGeneration")
class Qwen3ASRMmprojModel(Qwen3OmniMmprojModel):
    has_audio_encoder = True
    has_vision_encoder = False


@ModelBase.register("Glm4vForConditionalGeneration", "Glm4vMoeForConditionalGeneration", "GlmOcrForConditionalGeneration")
class Glm4VVisionModel(Qwen3VLVisionModel):
    def set_gguf_parameters(self):
        MmprojModel.set_gguf_parameters(self) # skip Qwen3VLVisionModel parameters
        assert self.hparams_vision is not None
        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.GLM4V)

        hidden_act = str(self.hparams_vision.get("hidden_act", "")).lower()
        if hidden_act == "gelu":
            self.gguf_writer.add_vision_use_gelu(True)
        elif hidden_act == "silu":
            self.gguf_writer.add_vision_use_silu(True)

        rms_norm_eps = self.hparams_vision.get("rms_norm_eps", 1e-5)
        self.gguf_writer.add_vision_attention_layernorm_eps(rms_norm_eps)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        if name.startswith("visual.merger."):
            yield from ModelBase.modify_tensors(self, data_torch, name, bid)
            return
        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("StepVLForConditionalGeneration")
class Step3VLVisionModel(MmprojModel):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        assert self.hparams_vision is not None

        if not self.hparams_vision.get("intermediate_size"):
            hidden_size = self.hparams_vision.get("hidden_size") or self.hparams_vision.get("width") or 0
            assert hidden_size > 0
            mlp_ratio = float(self.hparams_vision.get("mlp_ratio", 8960 / 1536))
            self.hparams_vision["intermediate_size"] = int(round(hidden_size * mlp_ratio))

        self.preprocessor_config.setdefault("image_mean", list(_MISTRAL_COMMON_DATASET_MEAN))
        self.preprocessor_config.setdefault("image_std", list(_MISTRAL_COMMON_DATASET_STD))

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        assert self.hparams_vision is not None

        projector_stride = int(self.global_config.get("understand_projector_stride", -1))
        hidden_size = int(self.hparams_vision.get("hidden_size", self.hparams_vision.get("width", -1)))
        num_layers = int(self.hparams_vision.get("num_hidden_layers", self.hparams_vision.get("layers", -1)))
        assert (projector_stride, int(self.hparams_vision.get("image_size", -1)), hidden_size, num_layers) == (2, 728, 1536, 47), (
            "current Step3-VL conversion path is only validated for Step3-VL-10B"
        )

        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.STEP3VL)
        self.gguf_writer.add_vision_attention_layernorm_eps(float(self.hparams_vision.get("layer_norm_eps", 1e-5)))
        self.gguf_writer.add_vision_projector_scale_factor(projector_stride ** 2)
        # 3024 max resize comes from step3-vl-10b processing_step3.py.
        self.gguf_writer.add_vision_preproc_image_size(3024)

    def tensor_force_quant(self, name, new_name, bid, n_dims):
        if ".position_embd." in new_name:
            return gguf.GGMLQuantizationType.F32
        if ("mm.0." in new_name or "mm.1." in new_name) and new_name.endswith(".weight"):
            return gguf.GGMLQuantizationType.F16 if self.ftype == gguf.LlamaFileType.MOSTLY_F16 else gguf.GGMLQuantizationType.F32
        return super().tensor_force_quant(name, new_name, bid, n_dims)

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        if name.startswith(("model.", "lm_head.")):
            return None

        return super().filter_tensors(item)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        if name.startswith("vision_model.vit_downsampler"):
            match = re.match(r"vision_model\.vit_downsampler(\d+)\.(weight|bias)", name)
            if match is None:
                raise ValueError(f"Unexpected Step3-VL projector tensor {name!r}")

            proj_id = int(match.group(1)) - 1
            suffix = f".{match.group(2)}"
            yield (self.format_tensor_name(gguf.MODEL_TENSOR.V_MMPROJ, proj_id, suffix=suffix), data_torch)
            return

        if name == "vit_large_projector.weight":
            yield (self.format_tensor_name(gguf.MODEL_TENSOR.V_MMPROJ_FC), data_torch)
            return

        if name.startswith("vision_model."):
            if name == "vision_model.positional_embedding":
                name += ".weight"
            elif name.endswith(".gamma") and ".ls_" in name:
                name = name.removesuffix(".gamma") + ".weight"

            name = name.replace("attn.in_proj_weight", "attn.in_proj.weight")
            name = name.replace("attn.in_proj_bias", "attn.in_proj.bias")

            yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("Qwen3VLForConditionalGeneration")
class Qwen3VLTextModel(Qwen3Model):
    model_arch = gguf.MODEL_ARCH.QWEN3VL

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        if "thinker_config" in self.hparams:
            vision_config = self.hparams["thinker_config"].get("vision_config", {})
        else:
            vision_config = self.hparams.get("vision_config", {})
        deepstack_layer_num = len(vision_config.get("deepstack_visual_indexes", []))
        self.gguf_writer.add_num_deepstack_layers(deepstack_layer_num)

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        name = name.replace("thinker.", "")

        return super().filter_tensors((name, gen))


@ModelBase.register("StepVLForConditionalGeneration")
class Step3VLTextModel(Qwen3Model):
    model_arch = gguf.MODEL_ARCH.QWEN3


@ModelBase.register("Qwen3VLMoeForConditionalGeneration")
class Qwen3VLMoeTextModel(Qwen3MoeModel):
    model_arch = gguf.MODEL_ARCH.QWEN3VLMOE

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        vision_config = self.hparams.get("vision_config", {})
        deepstack_layer_num = len(vision_config.get("deepstack_visual_indexes", []))
        self.gguf_writer.add_num_deepstack_layers(deepstack_layer_num)

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        name = name.replace("thinker.", "")

        return super().filter_tensors((name, gen))

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        # Qwen3VL has transposed packed tensors, so we treat it differently from general Qwen2MoE packed tensors
        if name.endswith("mlp.experts.down_proj") or name.endswith("mlp.experts.down_proj.weight"):
            mapped = f"{name}.weight" if not name.endswith(".weight") else name
            permuted = data_torch.permute(0, 2, 1).contiguous()
            yield from ModelBase.modify_tensors(self, permuted, mapped, bid)
            return

        if name.endswith("mlp.experts.gate_up_proj") or name.endswith("mlp.experts.gate_up_proj.weight"):
            if data_torch.ndim < 3 or data_torch.shape[-1] % 2 != 0:
                raise ValueError(f"Unexpected gate_up_proj shape for {name}: {tuple(data_torch.shape)}")
            split_dim = data_torch.shape[-1] // 2
            gate = data_torch[..., :split_dim].contiguous()
            up = data_torch[..., split_dim:].contiguous()
            # Input gate/up: (n_expert=128, n_embd=2048, n_ff_exp=768)
            # Want GGML ne: {n_embd, n_ff_exp, n_expert} = {2048, 768, 128}
            # Need PyTorch: (128, 768, 2048) [reversed of GGML]
            # So: permute(0, 2, 1): (128, 2048, 768) -> (128, 768, 2048)
            base_name = name.removesuffix(".weight")
            base = base_name.rsplit('.', 1)[0]
            mapped_gate = f"{base}.gate_proj.weight"
            mapped_up = f"{base}.up_proj.weight"
            perm_gate = gate.permute(0, 2, 1).contiguous()
            perm_up = up.permute(0, 2, 1).contiguous()
            yield from ModelBase.modify_tensors(self, perm_gate, mapped_gate, bid)
            yield from ModelBase.modify_tensors(self, perm_up, mapped_up, bid)
            return

        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("Qwen3OmniMoeForConditionalGeneration")
class Qwen3OmniMoeTextModel(Qwen3VLMoeTextModel):
    model_arch = gguf.MODEL_ARCH.QWEN3VLMOE

    def set_vocab(self):
        super().set_vocab()
        # correct BOS/EOS tokens
        with open(self.dir_model / "tokenizer_config.json", "r", encoding="utf-8") as f:
            tokenizer_config = json.load(f)
            added_tokens = tokenizer_config.get("added_tokens_decoder", {})
            for token_id, data in added_tokens.items():
                if data.get("content") == "<|im_end|>":
                    self.gguf_writer.add_bos_token_id(int(token_id))
                    self.gguf_writer.add_eos_token_id(int(token_id))
                    break

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        self.gguf_writer.add_num_deepstack_layers(0)


@ModelBase.register("Qwen3ASRForConditionalGeneration")
class Qwen3ASRTextModel(Qwen3VLTextModel):
    model_arch = gguf.MODEL_ARCH.QWEN3VL

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        self.gguf_writer.add_num_deepstack_layers(0)

    def set_vocab(self):
        super().set_vocab()
        # fix chat template, use correct chatml format
        self.gguf_writer.add_chat_template("{% for message in messages %}{{'<|im_start|>' + message['role'] + '\\n' + message['content'] + '<|im_end|>' + '\\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\\n' }}{% endif %}")
        # correct BOS/EOS tokens
        with open(self.dir_model / "tokenizer_config.json", "r", encoding="utf-8") as f:
            tokenizer_config = json.load(f)
            added_tokens = tokenizer_config.get("added_tokens_decoder", {})
            for token_id, data in added_tokens.items():
                if data.get("content") == "<|im_end|>":
                    self.gguf_writer.add_bos_token_id(int(token_id))
                    self.gguf_writer.add_eos_token_id(int(token_id))
                    break


class _LinearAttentionVReorderBase(Qwen3NextModel):
    model_arch = gguf.MODEL_ARCH.QWEN3NEXT  # overridden by subclasses
    """reorders V heads from grouped to tiled order for ggml broadcast

    see https://github.com/ggml-org/llama.cpp/pull/19468#discussion_r2786394306

    Linear attention may has num_k_heads < num_v_heads. The HF weights store
    V heads grouped by K head: [G0_v0..v{r-1}, G1_v0..v{r-1}, ...].
    ggml binary ops use tiled broadcast: [K0, K1, ..., K0, K1, ...].
    We reorder V heads to tiled order so ggml_repeat can replace the expensive
    interleaved repeat: [G0_v0, G1_v0, ..., G0_v1, G1_v1, ...].
    """

    @staticmethod
    def _reorder_v_heads(tensor: Tensor, dim: int, num_k_heads: int, num_v_per_k: int, head_dim: int) -> Tensor:
        """Reorder V heads from grouped (by K head) to tiled order along the given dimension."""
        shape = list(tensor.shape)
        if dim < 0:
            dim += len(shape)
        new_shape = shape[:dim] + [num_k_heads, num_v_per_k, head_dim] + shape[dim + 1:]
        tensor = tensor.reshape(*new_shape)
        perm = list(range(len(new_shape)))
        perm[dim], perm[dim + 1] = perm[dim + 1], perm[dim]
        return tensor.permute(*perm).contiguous().reshape(*shape)

    def _transform_nvfp4_weight(self, name: str, weight: Tensor, scale: Tensor) -> tuple[Tensor, Tensor]:
        if not name.endswith((
            ".linear_attn.in_proj_qkv.weight",
            ".linear_attn.in_proj_z.weight",
            ".linear_attn.in_proj_a.weight",
            ".linear_attn.in_proj_b.weight",
            ".linear_attn.out_proj.weight",
        )):
            return weight, scale

        num_k_heads = self.hparams["linear_num_key_heads"]
        num_v_heads = self.hparams["linear_num_value_heads"]
        head_k_dim = self.hparams["linear_key_head_dim"]
        head_v_dim = self.hparams["linear_value_head_dim"]
        num_v_per_k = num_v_heads // num_k_heads

        def unpack_nibbles(qs: Tensor) -> Tensor:
            lo = torch.bitwise_and(qs, 0x0F)
            hi = torch.bitwise_right_shift(qs, 4)
            return torch.stack((lo, hi), dim=-1).reshape(*qs.shape[:-1], qs.shape[-1] * 2)

        def pack_nibbles(codes: Tensor) -> Tensor:
            codes = codes.reshape(*codes.shape[:-1], codes.shape[-1] // 2, 2)
            lo = torch.bitwise_and(codes[..., 0], 0x0F)
            hi = torch.bitwise_left_shift(torch.bitwise_and(codes[..., 1], 0x0F), 4)
            return torch.bitwise_or(lo, hi).contiguous()

        def apply_col_perm(qs: Tensor, scales: Tensor, col_perm: Tensor) -> tuple[Tensor, Tensor]:
            assert qs.ndim >= 2
            assert scales.ndim >= 2

            k = qs.shape[-1] * 2
            assert col_perm.numel() == k
            assert k % 16 == 0

            group_cols = col_perm.reshape(-1, 16)
            group_starts = group_cols[:, 0]
            expected = group_starts.unsqueeze(1) + torch.arange(16, dtype=col_perm.dtype)
            assert torch.equal(group_cols, expected)
            assert torch.all(group_starts % 16 == 0)

            group_perm = (group_starts // 16).to(dtype=torch.long)
            expected_groups = torch.arange(scales.shape[-1], dtype=torch.long)
            assert group_perm.numel() == scales.shape[-1]
            assert torch.equal(torch.sort(group_perm).values, expected_groups)

            codes = unpack_nibbles(qs)
            codes = codes.index_select(-1, col_perm.to(device=qs.device, dtype=torch.long))
            qs = pack_nibbles(codes)
            scales = scales.index_select(-1, group_perm.to(device=scales.device))
            return qs, scales

        def reorder_rows(qs: Tensor, scales: Tensor, head_dim: int) -> tuple[Tensor, Tensor]:
            row_perm = self._reorder_v_heads(
                torch.arange(num_v_heads * head_dim, dtype=torch.long).unsqueeze(-1),
                0, num_k_heads, num_v_per_k, head_dim,
            ).squeeze(-1)
            return (
                qs.index_select(0, row_perm.to(device=qs.device)),
                scales.index_select(0, row_perm.to(device=scales.device)),
            )

        if name.endswith(".linear_attn.in_proj_qkv.weight"):
            q_dim = head_k_dim * num_k_heads
            k_dim = head_k_dim * num_k_heads
            q = weight[:q_dim]
            k = weight[q_dim:q_dim + k_dim]
            v = weight[q_dim + k_dim:]
            q_scale = scale[:q_dim]
            k_scale = scale[q_dim:q_dim + k_dim]
            v_scale = scale[q_dim + k_dim:]
            v, v_scale = reorder_rows(v, v_scale, head_v_dim)
            return torch.cat([q, k, v], dim=0), torch.cat([q_scale, k_scale, v_scale], dim=0)

        if name.endswith(".linear_attn.in_proj_z.weight"):
            weight, scale = reorder_rows(weight, scale, head_v_dim)
        elif name.endswith((".linear_attn.in_proj_a.weight", ".linear_attn.in_proj_b.weight")):
            weight, scale = reorder_rows(weight, scale, 1)
        elif name.endswith(".linear_attn.out_proj.weight"):
            col_perm = self._reorder_v_heads(
                torch.arange(num_v_heads * head_v_dim, dtype=torch.long).unsqueeze(0),
                1, num_k_heads, num_v_per_k, head_v_dim,
            ).squeeze(0)
            weight, scale = apply_col_perm(weight, scale, col_perm)

        return weight, scale

    def _repack_nvfp4(self, name: str, weight: Tensor, scale: Tensor, scale2: Tensor, input_scale: Tensor):
        weight, scale = self._transform_nvfp4_weight(name, weight, scale)
        super()._repack_nvfp4(name, weight, scale, scale2, input_scale)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        num_k_heads = self.hparams.get("linear_num_key_heads", 0)
        num_v_heads = self.hparams.get("linear_num_value_heads", 0)

        if num_k_heads > 0 and num_v_heads > 0 and num_k_heads != num_v_heads and "linear_attn." in name:
            head_k_dim = self.hparams["linear_key_head_dim"]
            head_v_dim = self.hparams["linear_value_head_dim"]
            num_v_per_k = num_v_heads // num_k_heads

            if ".in_proj_qkv." in name:
                # QKV weight: reorder only the V rows
                q_dim = head_k_dim * num_k_heads
                k_dim = head_k_dim * num_k_heads
                q = data_torch[:q_dim]
                k = data_torch[q_dim:q_dim + k_dim]
                v = data_torch[q_dim + k_dim:]
                v = self._reorder_v_heads(v, 0, num_k_heads, num_v_per_k, head_v_dim)
                data_torch = torch.cat([q, k, v], dim=0)

            elif ".in_proj_z." in name:
                # Z gate weight: reorder rows (num_v_heads * head_v_dim)
                data_torch = self._reorder_v_heads(data_torch, 0, num_k_heads, num_v_per_k, head_v_dim)

            elif ".in_proj_b." in name or ".in_proj_a." in name:
                # Beta/Alpha weight: reorder rows (num_v_heads, head_dim=1)
                data_torch = self._reorder_v_heads(data_torch, 0, num_k_heads, num_v_per_k, 1)

            elif ".A_log" in name or ".dt_bias" in name or ".dt_proj" in name:
                # A_log / dt_bias: 1D parameters with num_v_heads elements
                if data_torch.ndim == 1:
                    data_torch = self._reorder_v_heads(
                        data_torch.unsqueeze(-1), 0, num_k_heads, num_v_per_k, 1
                    ).squeeze(-1)
                else:
                    data_torch = self._reorder_v_heads(data_torch, -1, num_k_heads, num_v_per_k, 1)

            elif ".conv1d" in name:
                # Conv1d kernel: reorder only the V channel portion
                data = data_torch.squeeze()
                qk_channels = head_k_dim * num_k_heads * 2
                qk_part = data[:qk_channels]
                v_part = data[qk_channels:]
                v_part = self._reorder_v_heads(v_part, 0, num_k_heads, num_v_per_k, head_v_dim)
                data_torch = torch.cat([qk_part, v_part], dim=0)

            elif ".out_proj." in name:
                # Out projection weight: reorder columns (input dimension)
                data_torch = self._reorder_v_heads(data_torch, 1, num_k_heads, num_v_per_k, head_v_dim)

        yield from super().modify_tensors(data_torch, name, bid)


class _Qwen35MRopeMixin:
    # Qwen3.5 always applies interleaved MRoPE (see Qwen3_5RotaryEmbedding in transformers);
    # the upstream default mrope_section is [11, 11, 10] and llama.cpp's QWEN35 / QWEN35MOE
    # loaders treat qwen35.rope.dimension_sections as required, so make sure it is always
    # written even when a particular checkpoint omits the field in `rope_parameters`.
    _QWEN35_DEFAULT_MROPE_SECTION = [11, 11, 10, 0]

    gguf_writer: gguf.GGUFWriter
    rope_parameters: dict

    def set_gguf_parameters(self):
        super().set_gguf_parameters()  # ty: ignore[unresolved-attribute]
        if "mrope_section" not in self.rope_parameters:
            self.gguf_writer.add_rope_dimension_sections(self._QWEN35_DEFAULT_MROPE_SECTION)


@ModelBase.register("Qwen3_5ForConditionalGeneration", "Qwen3_5ForCausalLM")
class Qwen3_5TextModel(_Qwen35MRopeMixin, _LinearAttentionVReorderBase):
    model_arch = gguf.MODEL_ARCH.QWEN35


@ModelBase.register("Qwen3_5MoeForConditionalGeneration", "Qwen3_5MoeForCausalLM")
class Qwen3_5MoeTextModel(_Qwen35MRopeMixin, _LinearAttentionVReorderBase):
    model_arch = gguf.MODEL_ARCH.QWEN35MOE


# MiniCPM-V 4.6: text tower is Qwen3.5 (linear+full hybrid attention) wrapped under
# `model.language_model.*`; vision tower is SigLIP + a window-attention ViT merger
# + a final DownsampleMLP merger. The same HF arch is registered twice below: once as
# the LM (text mode) and once as the mmproj (vision mode), mirroring the Qwen3-VL setup.

@ModelBase.register("MiniCPMV4_6ForConditionalGeneration")
class MiniCPMV4_6TextModel(Qwen3_5TextModel):
    model_arch = gguf.MODEL_ARCH.QWEN35

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        if name.startswith("model.merger."):
            return None
        # MTP tensors are not used at inference yet; align with Qwen3Next behaviour
        if name.startswith("mtp"):
            return None

        return super().filter_tensors(item)


@ModelBase.register("MiniCPMV4_6ForConditionalGeneration")
class MiniCPMV4_6VisionModel(MmprojModel):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        if self.hparams_vision is not None:
            # In MiniCPM-V 4.6 `vision_config.image_size` (980) describes the SigLIP
            # positional embedding bucket grid (70 x 70), while the per-slice processing
            # resolution is the preprocessor's `scale_resolution` (typically 448).
            # The CLIP loader in tools/mtmd/clip.cpp consumes `clip.vision.image_size`
            # as the slice size and warmup resolution, so report `scale_resolution` there
            # to match the upstream MiniCPMV4_6ImageProcessorPil slicing rules.
            scale_resolution = self.preprocessor_config.get("scale_resolution")
            if scale_resolution is not None:
                self.hparams_vision["image_size"] = int(scale_resolution)

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        assert self.hparams_vision is not None

        # projector type string is consumed by clip_projector_type_from_string() in clip.cpp
        # (mapped to PROJECTOR_TYPE_MINICPMV4_6).
        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.MINICPMV4_6)

        # ViT merger 2x2 + final merger 2x2 = 4x spatial merge per dimension; used for slice alignment
        self.gguf_writer.add_vision_projector_scale_factor(4)

        # borrow wa_layer_indexes for vit_merger insertion point
        insert_layer_id = int(self.global_config.get(
            "insert_layer_id", self.hparams_vision.get("insert_layer_id", 6)))
        self.gguf_writer.add_vision_wa_layer_indexes([insert_layer_id])

        # SigLIP vision body uses gelu_pytorch_tanh, which matches ggml_gelu (tanh approx).
        self.gguf_writer.add_vision_use_gelu(True)
        self.gguf_writer.add_vision_attention_layernorm_eps(
            self.hparams_vision.get("layer_norm_eps", 1e-6))

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        # lm_head / MTP -> belong to the LM file
        if name.startswith(("lm_head.", "mtp")):
            return None

        return super().filter_tensors(item)


@ModelBase.register("GPT2LMHeadModel")
class GPT2Model(TextModel):
    model_arch = gguf.MODEL_ARCH.GPT2

    def set_gguf_parameters(self):
        self.gguf_writer.add_block_count(self.block_count)
        self.gguf_writer.add_context_length(self.hparams["n_ctx"])
        self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
        self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"])
        self.gguf_writer.add_head_count(self.hparams["n_head"])
        self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
        self.gguf_writer.add_file_type(self.ftype)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        # we don't need these
        if name.endswith((".attn.bias", ".attn.masked_bias")):
            yield from super().modify_tensors(data_torch, name, bid)
            return

        if name.endswith((".c_attn.weight", ".c_proj.weight", ".c_fc.weight", ".c_proj.weight")):
            data_torch = data_torch.transpose(1, 0)

        new_name = self.map_tensor_name(name)

        yield from super().modify_tensors(data_torch, new_name, bid)


@ModelBase.register("RuGPT3XLForCausalLM")
class RuGPT3XLModel(TextModel):
    model_arch = gguf.MODEL_ARCH.GPT2

    _qkv_parts: list[dict[str, Tensor]] | None = None

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        # Fuse separate Q, K, V projections into a single QKV tensor
        if ".self_attn.q_proj." in name or ".self_attn.k_proj." in name or ".self_attn.v_proj." in name:
            suffix = "weight" if name.endswith(".weight") else "bias"
            part = "q" if ".q_proj." in name else ("k" if ".k_proj." in name else "v")
            key = f"{part}.{suffix}"

            assert bid is not None
            if self._qkv_parts is None:
                self._qkv_parts = [{} for _ in range(self.block_count)]
            self._qkv_parts[bid][key] = data_torch

            q_key, k_key, v_key = f"q.{suffix}", f"k.{suffix}", f"v.{suffix}"
            if all(k in self._qkv_parts[bid] for k in [q_key, k_key, v_key]):
                q = self._qkv_parts[bid].pop(q_key)
                k = self._qkv_parts[bid].pop(k_key)
                v = self._qkv_parts[bid].pop(v_key)
                data_torch = torch.cat([q, k, v], dim=0)
                name = self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_QKV, bid, f".{suffix}")
                logger.debug(f"Fused Q/K/V {suffix} for layer {bid} -> {name}")
            else:
                return

        yield from super().modify_tensors(data_torch, name, bid)

    def prepare_tensors(self):
        super().prepare_tensors()

        if self._qkv_parts is not None:
            # flatten `list[dict[str, Tensor]]` into `list[str]`
            parts = [f"({i}){k}" for i, d in enumerate(self._qkv_parts) for k in d.keys()]
            if len(parts) > 0:
                raise ValueError(f"Unprocessed Q/K/V parts: {parts}")


@ModelBase.register("PhiForCausalLM")
class Phi2Model(TextModel):
    model_arch = gguf.MODEL_ARCH.PHI2

    def set_gguf_parameters(self):
        rot_pct = self.find_hparam(["partial_rotary_factor"])
        n_embd = self.find_hparam(["hidden_size", "n_embd"])
        n_head = self.find_hparam(["num_attention_heads", "n_head"])

        self.gguf_writer.add_context_length(self.find_hparam(["n_positions", "max_position_embeddings"]))

        self.gguf_writer.add_embedding_length(n_embd)
        self.gguf_writer.add_feed_forward_length(4 * n_embd)
        self.gguf_writer.add_block_count(self.block_count)
        self.gguf_writer.add_head_count(n_head)
        self.gguf_writer.add_head_count_kv(n_head)
        self.gguf_writer.add_layer_norm_eps(self.find_hparam(["layer_norm_epsilon", "layer_norm_eps"]))
        self.gguf_writer.add_rope_dimension_count(int(rot_pct * n_embd) // n_head)
        self.gguf_writer.add_file_type(self.ftype)
        self.gguf_writer.add_add_bos_token(False)


@ModelBase.register("Phi3ForCausalLM", "Phi4ForCausalLMV")
class Phi3MiniModel(TextModel):
    model_arch = gguf.MODEL_ARCH.PHI3

    def set_vocab(self):
        # Phi-4 model uses GPT2Tokenizer
        tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
        if tokenizer_config_file.is_file():
            with open(tokenizer_config_file, "r", encoding="utf-8") as f:
                tokenizer_config_json = json.load(f)
                tokenizer_class = tokenizer_config_json['tokenizer_class']
                if tokenizer_class == 'GPT2Tokenizer':
                    return self._set_vocab_gpt2()

        from sentencepiece import SentencePieceProcessor

        tokenizer_path = self.dir_model / 'tokenizer.model'

        if not tokenizer_path.is_file():
            raise ValueError(f'Error: Missing {tokenizer_path}')

        tokenizer = SentencePieceProcessor()
        tokenizer.LoadFromFile(str(tokenizer_path))

        vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())

        tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
        scores: list[float] = [-10000.0] * vocab_size
        toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size

        for token_id in range(tokenizer.vocab_size()):

            piece = tokenizer.IdToPiece(token_id)
            text = piece.encode("utf-8")
            score = tokenizer.GetScore(token_id)

            toktype = SentencePieceTokenTypes.NORMAL
            if tokenizer.IsUnknown(token_id):
                toktype = SentencePieceTokenTypes.UNKNOWN
            elif tokenizer.IsControl(token_id):
                toktype = SentencePieceTokenTypes.CONTROL
            elif tokenizer.IsUnused(token_id):
                toktype = SentencePieceTokenTypes.UNUSED
            elif tokenizer.IsByte(token_id):
                toktype = SentencePieceTokenTypes.BYTE

            tokens[token_id] = text
            scores[token_id] = score
            toktypes[token_id] = toktype

        added_tokens_file = self.dir_model / 'added_tokens.json'
        if added_tokens_file.is_file():
            with open(added_tokens_file, "r", encoding="utf-8") as f:
                added_tokens_json = json.load(f)

                for key in added_tokens_json:
                    token_id = added_tokens_json[key]
                    if token_id >= vocab_size:
                        logger.debug(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}')
                        continue

                    tokens[token_id] = key.encode("utf-8")
                    scores[token_id] = -1000.0
                    toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED

        tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
        if tokenizer_config_file.is_file():
            with open(tokenizer_config_file, "r", encoding="utf-8") as f:
                tokenizer_config_json = json.load(f)
                added_tokens_decoder = tokenizer_config_json.get("added_tokens_decoder", {})
                for token_id, foken_data in added_tokens_decoder.items():
                    token_id = int(token_id)
                    token = foken_data["content"].encode("utf-8")
                    if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
                        if tokens[token_id] != token:
                            logger.warning(f'replacing token {token_id}: {tokens[token_id].decode("utf-8")!r} -> {token.decode("utf-8")!r}')
                    tokens[token_id] = token
                    scores[token_id] = -1000.0
                    toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
                    if foken_data.get("special"):
                        toktypes[token_id] = SentencePieceTokenTypes.CONTROL

        tokenizer_file = self.dir_model / 'tokenizer.json'
        if tokenizer_file.is_file():
            with open(tokenizer_file, "r", encoding="utf-8") as f:
                tokenizer_json = json.load(f)
                added_tokens = tokenizer_json.get("added_tokens", [])
                for foken_data in added_tokens:
                    token_id = int(foken_data["id"])
                    token = foken_data["content"].encode("utf-8")
                    if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
                        if tokens[token_id] != token:
                            logger.warning(f'replacing token {token_id}: {tokens[token_id].decode("utf-8")!r} -> {token.decode("utf-8")!r}')
                    tokens[token_id] = token
                    scores[token_id] = -1000.0
                    toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
                    if foken_data.get("special"):
                        toktypes[token_id] = SentencePieceTokenTypes.CONTROL

        self.gguf_writer.add_tokenizer_model("llama")
        self.gguf_writer.add_tokenizer_pre("default")
        self.gguf_writer.add_token_list(tokens)
        self.gguf_writer.add_token_scores(scores)
        self.gguf_writer.add_token_types(toktypes)

        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
        special_vocab.add_to_gguf(self.gguf_writer)

    def set_gguf_parameters(self):
        n_embd = self.find_hparam(["hidden_size", "n_embd"])
        n_head = self.find_hparam(["num_attention_heads", "n_head"])
        n_head_kv = self.find_hparam(["num_key_value_heads", "n_head_kv"])
        rms_eps = self.find_hparam(["rms_norm_eps"])
        max_pos_embds = self.find_hparam(["n_positions", "max_position_embeddings"])
        orig_max_pos_embds = self.find_hparam(["original_max_position_embeddings"])
        rot_pct = self.hparams.get("partial_rotary_factor", 1.0)
        rope_dims = int(rot_pct * n_embd) // n_head

        self.gguf_writer.add_context_length(max_pos_embds)
        self.gguf_writer.add_rope_scaling_orig_ctx_len(orig_max_pos_embds)
        self.gguf_writer.add_embedding_length(n_embd)
        self.gguf_writer.add_feed_forward_length(self.find_hparam(["intermediate_size"]))
        self.gguf_writer.add_block_count(self.block_count)
        self.gguf_writer.add_head_count(n_head)
        self.gguf_writer.add_head_count_kv(n_head_kv)
        self.gguf_writer.add_layer_norm_rms_eps(rms_eps)
        self.gguf_writer.add_rope_dimension_count(rope_dims)
        self.gguf_writer.add_rope_freq_base(self.rope_parameters.get("full_attention", self.rope_parameters)["rope_theta"])
        self.gguf_writer.add_file_type(self.ftype)
        sliding_window = self.hparams.get("sliding_window")
        # use zero value of sliding_window to distinguish Phi-4 from other PHI3 models
        if sliding_window is None:
            sliding_window = 0
        self.gguf_writer.add_sliding_window(sliding_window)

    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
        n_embd = self.find_hparam(["hidden_size", "n_embd"])
        n_head = self.find_hparam(["num_attention_heads", "n_head"])
        max_pos_embds = self.find_hparam(["n_positions", "max_position_embeddings"])
        orig_max_pos_embds = self.find_hparam(["original_max_position_embeddings"])
        rot_pct = self.hparams.get("partial_rotary_factor", 1.0)
        rope_dims = int(rot_pct * n_embd) // n_head

        # write rope scaling for long context (128k) model
        rope_scaling = self.find_hparam(['rope_scaling'], True)
        if rope_scaling is None:
            return

        scale = max_pos_embds / orig_max_pos_embds

        rope_scaling_type = rope_scaling.get('rope_type', rope_scaling.get('type', '')).lower()
        if len(rope_scaling_type) == 0:
            raise KeyError('Missing the required key rope_scaling.type')

        if rope_scaling_type == 'su' or rope_scaling_type == 'longrope':
            attn_factor = math.sqrt(1 + math.log(scale) / math.log(orig_max_pos_embds)) if scale > 1.0 else 1.0
        elif rope_scaling_type == 'yarn':
            attn_factor = 0.1 * math.log(scale) + 1.0 if scale > 1.0 else 1.0
        else:
            raise NotImplementedError(f'The rope scaling type {rope_scaling_type} is not supported yet')

        self.gguf_writer.add_rope_scaling_attn_factors(attn_factor)

        long_factors = rope_scaling.get('long_factor', None)
        short_factors = rope_scaling.get('short_factor', None)

        if long_factors is None or short_factors is None:
            raise KeyError('Missing the required key rope_scaling.long_factor or rope_scaling_short_factor')

        if len(long_factors) != len(short_factors) or len(long_factors) != rope_dims / 2:
            raise ValueError(f'The length of rope long and short factors must be {rope_dims / 2}. long_factors = {len(long_factors)}, short_factors = {len(short_factors)}.')

        yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_LONG), torch.tensor(long_factors, dtype=torch.float32))
        yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_SHORT), torch.tensor(short_factors, dtype=torch.float32))


@ModelBase.register("Phi4ForCausalLMV")
class Phi4VisionMmprojModel(MmprojModel):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        assert self.hparams_vision is not None

        self.vision_total_layers = int(self.find_vparam(self.n_block_keys))
        if self.vision_total_layers < 2:
            raise ValueError(
                f"Phi-4 vision mmproj conversion requires at least 2 vision layers, got {self.vision_total_layers}"
            )

        # Phi-4 uses SigLIP2 hidden_states[-2], so export one fewer encoder block and
        # drop post-layernorm/head weights. This makes the GGUF runtime output match
        # the feature map consumed by the patched siglip.cpp Phi-4 projector path.
        self.vision_export_layers = self.vision_total_layers - 1
        self.vision_last_layer_idx = self.vision_total_layers - 1

        for key in self.n_block_keys:
            if key in self.hparams_vision:
                self.hparams_vision[key] = self.vision_export_layers
                break

        self.block_count = self.vision_export_layers
        self.tensor_map = gguf.get_tensor_name_map(gguf.MODEL_ARCH.MMPROJ, self.block_count)

        patch_size = self.preprocessor_config.get("patch_size")
        if patch_size is None:
            raise KeyError("Phi-4 vision mmproj conversion requires patch_size in preprocessor_config.json")

        self.hparams_vision["patch_size"] = patch_size

        pos_emb_name = next(
            (
                name for name in self.model_tensors
                if name.endswith("vision_model.embeddings.position_embedding.weight")
            ),
            None,
        )
        if pos_emb_name is None:
            raise KeyError("Phi-4 vision mmproj conversion could not find position_embedding.weight")

        pos_emb_shape = self.model_tensors[pos_emb_name]().shape
        base_grid_tokens = int(pos_emb_shape[0])
        grid_side = math.isqrt(base_grid_tokens)
        if grid_side * grid_side != base_grid_tokens:
            raise ValueError(f"Unexpected Phi-4 position embedding shape: {tuple(pos_emb_shape)}")

        self.hparams_vision["image_size"] = grid_side * patch_size

        min_num_patches = self.preprocessor_config.get("min_num_patches", self.global_config.get("min_num_patches"))
        max_num_patches = self.preprocessor_config.get("max_num_patches", self.global_config.get("max_num_patches"))
        if min_num_patches is None or max_num_patches is None:
            raise KeyError("Phi-4 vision mmproj conversion requires min_num_patches and max_num_patches")

        self.min_pixels = int(min_num_patches) * patch_size * patch_size
        self.max_pixels = int(max_num_patches) * patch_size * patch_size

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        assert self.hparams_vision is not None

        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.PHI4)
        self.gguf_writer.add_vision_min_pixels(self.min_pixels)
        self.gguf_writer.add_vision_max_pixels(self.max_pixels)
        self.gguf_writer.add_vision_use_gelu(True)
        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams_vision.get("layer_norm_eps", 1e-6))

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        name = name.replace("model.vision_tower.vision_tower.", "vision_tower.")

        if not name.startswith(("vision_tower.", "model.mm_projector.", "mm_projector.")):
            return None

        if ".vision_model.head." in name:
            return None

        if ".vision_model.post_layernorm." in name:
            return None

        return super().filter_tensors((name, gen))

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        if name.startswith("vision_tower."):
            if bid is not None and bid == self.vision_last_layer_idx:
                return

            if name.endswith("vision_model.embeddings.patch_embedding.weight"):
                assert self.hparams_vision is not None
                if data_torch.ndim != 2:
                    raise ValueError(f"Unexpected Phi-4 patch embedding shape: {tuple(data_torch.shape)}")

                patch_area = self.hparams_vision["patch_size"] ** 2
                in_features = data_torch.shape[1]
                if in_features % patch_area != 0:
                    raise ValueError(
                        f"Phi-4 patch embedding input dim {in_features} is not divisible by patch area {patch_area}"
                    )

                num_channels = in_features // patch_area
                patch_size = self.hparams_vision["patch_size"]
                data_torch = data_torch.view(data_torch.shape[0], patch_size, patch_size, num_channels)
                data_torch = data_torch.permute(0, 3, 1, 2)

            yield from super().modify_tensors(data_torch, name, bid)
            return

        if name.startswith(("model.mm_projector.", "mm_projector.")):
            local_name = name
            local_name = local_name.replace("model.mm_projector.", "")
            local_name = local_name.replace("mm_projector.", "")

            if not (local_name.startswith("0.") or local_name.startswith("2.")):
                return

            suffix = ".bias" if local_name.endswith(".bias") else ".weight"
            mm_idx = int(local_name.split(".", maxsplit=1)[0])
            yield (self.format_tensor_name(gguf.MODEL_TENSOR.V_MMPROJ, mm_idx, suffix=suffix), data_torch)
            return

        return


@ModelBase.register("PhiMoEForCausalLM")
class PhiMoeModel(Phi3MiniModel):
    model_arch = gguf.MODEL_ARCH.PHIMOE

    _experts: list[dict[str, Tensor]] | None = None

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        self.gguf_writer.add_expert_used_count(self.find_hparam(["num_experts_per_tok", "num_experts_per_token"]))
        self.gguf_writer.add_expert_count(self.find_hparam(["num_local_experts", "num_experts"]))

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        # process the experts separately
        if name.find("block_sparse_moe.experts") != -1:
            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
            assert bid is not None

            if self._experts is None:
                self._experts = [{} for _ in range(self.block_count)]

            self._experts[bid][name] = data_torch

            if len(self._experts[bid]) >= n_experts * 3:
                # merge the experts into a single 3d tensor
                for w_name in ["w1", "w2", "w3"]:
                    datas: list[Tensor] = []

                    for xid in range(n_experts):
                        ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{w_name}.weight"
                        datas.append(self._experts[bid][ename])
                        del self._experts[bid][ename]

                    data_torch = torch.stack(datas, dim=0)

                    merged_name = f"model.layers.{bid}.block_sparse_moe.experts.{w_name}.weight"

                    yield from super().modify_tensors(data_torch, merged_name, bid)
                return
            else:
                return

        yield from super().modify_tensors(data_torch, name, bid)

    def prepare_tensors(self):
        super().prepare_tensors()

        if self._experts is not None:
            # flatten `list[dict[str, Tensor]]` into `list[str]`
            experts = [k for d in self._experts for k in d.keys()]
            if len(experts) > 0:
                raise ValueError(f"Unprocessed experts: {experts}")


@ModelBase.register("PlamoForCausalLM")
class PlamoModel(TextModel):
    model_arch = gguf.MODEL_ARCH.PLAMO

    def set_vocab(self):
        self._set_vocab_sentencepiece()

    def set_gguf_parameters(self):
        hparams = self.hparams

        self.gguf_writer.add_context_length(4096)  # not in config.json
        self.gguf_writer.add_embedding_length(hparams["hidden_size"])
        self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
        self.gguf_writer.add_block_count(self.block_count)
        self.gguf_writer.add_head_count(hparams["num_attention_heads"])
        self.gguf_writer.add_head_count_kv(5)  # hparams["num_key_value_heads"]) is wrong
        self.gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"])
        self.gguf_writer.add_file_type(self.ftype)

    def shuffle_attn_q_weight(self, data_torch):
        assert data_torch.size() == (5120, 5120)
        data_torch = data_torch.reshape(8, 5, 128, 5120)
        data_torch = torch.permute(data_torch, (1, 0, 2, 3))
        data_torch = torch.reshape(data_torch, (5120, 5120))
        return data_torch

    def shuffle_attn_output_weight(self, data_torch):
        assert data_torch.size() == (5120, 5120)
        data_torch = data_torch.reshape(5120, 8, 5, 128)
        data_torch = torch.permute(data_torch, (0, 2, 1, 3))
        data_torch = torch.reshape(data_torch, (5120, 5120))
        return data_torch

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        new_name = self.map_tensor_name(name)

        # shuffle for broadcasting of gqa in ggml_mul_mat
        if new_name.endswith("attn_q.weight"):
            data_torch = self.shuffle_attn_q_weight(data_torch)
        elif new_name.endswith("attn_output.weight"):
            data_torch = self.shuffle_attn_output_weight(data_torch)

        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("Plamo2ForCausalLM", "PLaMo2ForCausalLM")
class Plamo2Model(TextModel):
    model_arch = gguf.MODEL_ARCH.PLAMO2

    def set_vocab(self):
        self._set_vocab_plamo()

    def set_gguf_parameters(self):
        hparams = self.hparams
        self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])

        # Which layers are Mamba layers
        # PLaMo 2 uses mamba_step to indicate the pattern (e.g., 2 means every other layer)
        # This logic matches modeling_plamo.py's is_mamba function
        mamba_step = hparams.get("mamba_step", 2)
        mamba_enabled = hparams.get("mamba_enabled", True)
        num_key_value_heads = []
        num_attention_heads = []

        if mamba_enabled:
            for i in range(self.block_count):
                if self.block_count <= (mamba_step // 2):
                    # use attention in last layer
                    is_mamba = (i != self.block_count - 1)
                else:
                    is_mamba = (i % mamba_step) != (mamba_step // 2)
                if is_mamba:
                    num_key_value_heads.append(0)
                    num_attention_heads.append(0)
                else:
                    num_key_value_heads.append(hparams.get("num_key_value_heads", 4))
                    num_attention_heads.append(hparams.get("num_attention_heads", 32))

        if num_key_value_heads and num_attention_heads:
            self.gguf_writer.add_head_count_kv(num_key_value_heads)
            self.gguf_writer.add_head_count(num_attention_heads)

        self.gguf_writer.add_context_length(hparams.get("max_position_embeddings", 2048))
        self.gguf_writer.add_embedding_length(hparams.get("hidden_size", 4096))
        self.gguf_writer.add_key_length(hparams.get("hidden_size_per_head", 128))
        self.gguf_writer.add_value_length(hparams.get("hidden_size_per_head", 128))
        self.gguf_writer.add_block_count(self.block_count)
        self.gguf_writer.add_layer_norm_rms_eps(hparams.get("rms_norm_eps", 1e-06))
        self.gguf_writer.add_rope_freq_base(self.rope_parameters.get("rope_theta", 10000))

        # Mamba parameters
        self.gguf_writer.add_ssm_state_size(hparams.get("mamba_d_state", 64))
        self.gguf_writer.add_ssm_conv_kernel(hparams.get("mamba_d_conv", 4))
        self.gguf_writer.add_ssm_time_step_rank(hparams.get("mamba_num_heads", 64))
        intermediate_size = hparams.get("mamba_num_heads", 64) * hparams.get("hidden_size_per_head", 128)
        self.gguf_writer.add_ssm_inner_size(intermediate_size)
        self.gguf_writer.add_ssm_group_count(0)

        # MLP feed forward parameters (for attention layers)
        self.gguf_writer.add_feed_forward_length(hparams.get("intermediate_size", 13312))
        self.gguf_writer.add_file_type(self.ftype)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        if name.endswith(".A_log"):
            data_torch = -torch.exp(data_torch)
        elif name.endswith(".dt_bias"):
            name = name.rpartition(".dt_bias")[0] + ".dt_proj.bias"
        elif name.endswith(".dt_norm_weight"):
            name = name.rpartition(".dt_norm_weight")[0] + ".dt_norm.weight"
        elif name.endswith(".B_norm_weight"):
            name = name.rpartition(".B_norm_weight")[0] + ".B_norm.weight"
        elif name.endswith(".C_norm_weight"):
            name = name.rpartition(".C_norm_weight")[0] + ".C_norm.weight"
        elif name.endswith(".k_weight"):
            name = name.rpartition(".k_weight")[0] + ".k.weight"
        elif name.endswith(".q_weight"):
            name = name.rpartition(".q_weight")[0] + ".q.weight"
        elif name.endswith(".conv1d.weight"):
            data_torch = torch.squeeze(data_torch)  # remove (, 1, )
            assert data_torch.ndim == 2
        elif name.endswith(".pre_mixer_norm.weight"):
            data_torch += 1.0
        elif name.endswith(".post_mixer_norm.weight"):
            data_torch += 1.0 / 5
        elif name.endswith(".pre_mlp_norm.weight"):
            data_torch += 1.0
        elif name.endswith(".post_mlp_norm.weight"):
            data_torch += 1.0 / (5**1.5)
        elif name.endswith(".norm.weight"):
            data_torch += 1.0

        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("Plamo3ForCausalLM", "PLaMo3ForCausalLM")
class Plamo3Model(TextModel):
    model_arch = gguf.MODEL_ARCH.PLAMO3

    def set_vocab(self):
        self._set_vocab_plamo()

        tokenizer_config_path = self.dir_model / "tokenizer_config.json"
        tokenizer_config = {}

        if tokenizer_config_path.is_file():
            with open(tokenizer_config_path, encoding="utf-8") as f:
                tokenizer_config = json.load(f)

        chat_template = tokenizer_config.get("chat_template")
        chat_template_jinja = self.dir_model / "chat_template.jinja"

        if chat_template_jinja.is_file():
            with open(chat_template_jinja, encoding="utf-8") as f:
                chat_template = f.read()

        if chat_template:
            self.gguf_writer.add_chat_template(chat_template)

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])
        if (sliding_window := self.find_hparam(["window_size", "sliding_window"], optional=True)) is not None:
            self.gguf_writer.add_sliding_window(sliding_window)
            self.gguf_writer.add_sliding_window_pattern(self.hparams["sliding_window_pattern"])

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:

        if name.endswith(".pre_mixer_norm.weight"):
            data_torch = data_torch + 1.0
        elif name.endswith(".post_mixer_norm.weight"):
            data_torch = data_torch + 1.0 / 5
        elif name.endswith(".pre_mlp_norm.weight"):
            data_torch = data_torch + 1.0
        elif name.endswith(".post_mlp_norm.weight"):
            data_torch = data_torch + 1.0 / (5**1.5)
        elif name.endswith((".mixer.q_norm.weight", ".mixer.k_norm.weight")):
            data_torch = data_torch + 1.0
        elif name.endswith(".norm.weight"):
            data_torch = data_torch + 1.0

        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("CodeShellForCausalLM")
class CodeShellModel(TextModel):
    model_arch = gguf.MODEL_ARCH.CODESHELL

    def set_gguf_parameters(self):
        self.gguf_writer.add_context_length(self.hparams["n_positions"])
        self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
        self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"])
        self.gguf_writer.add_block_count(self.block_count)
        self.gguf_writer.add_head_count(self.hparams["n_head"])
        self.gguf_writer.add_head_count_kv(self.hparams["num_query_groups"])
        self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
        self.gguf_writer.add_file_type(self.ftype)
        self.gguf_writer.add_rope_freq_base(10000.0)
        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
        self.gguf_writer.add_rope_scaling_factor(1.0)


@ModelBase.register("KimiLinearModel", "KimiLinearForCausalLM")
class KimiLinearModel(TextModel):
    """Kimi-Linear model with hybrid MLA+KDA architecture"""
    model_arch = gguf.MODEL_ARCH.KIMI_LINEAR

    _experts: list[dict[str, Tensor]] | None = None

    def set_vocab(self):
        try:
            self._set_vocab_gpt2()
            return
        except Exception:
            pass

        from transformers import AutoTokenizer
        tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
        tokpre = self.get_vocab_base_pre(tokenizer)

        if tokpre == "kimi-k2":
            # Build merges list using the approach similar to HunYuanMoE
            merges = []
            vocab = {}
            mergeable_ranks = tokenizer.model._mergeable_ranks  # ty: ignore[unresolved-attribute]
            for token, rank in mergeable_ranks.items():
                vocab[QwenModel.token_bytes_to_string(token)] = rank
                if len(token) == 1:
                    continue
                merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank)
                if len(merged) == 2:
                    merges.append(' '.join(map(QwenModel.token_bytes_to_string, merged)))
            # Build token list
            vocab_size = self.hparams["vocab_size"]
            special_tokens = tokenizer.special_tokens  # ty: ignore[unresolved-attribute]
            reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **special_tokens}.items()}
            tokens: list[str] = []
            toktypes: list[int] = []

            for i in range(vocab_size):
                if i not in reverse_vocab:
                    tokens.append(f"[PAD{i}]")
                    toktypes.append(gguf.TokenType.UNUSED)
                else:
                    token = reverse_vocab[i]
                    tokens.append(token)
                    if i in special_tokens.values():
                        toktypes.append(gguf.TokenType.CONTROL)
                    else:
                        toktypes.append(gguf.TokenType.NORMAL)

            self.gguf_writer.add_tokenizer_model("gpt2")
            self.gguf_writer.add_tokenizer_pre(tokpre)
            self.gguf_writer.add_token_list(tokens)
            self.gguf_writer.add_token_types(toktypes)
            self.gguf_writer.add_token_merges(merges)

            special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False)
            special_vocab.add_to_gguf(self.gguf_writer)
            # override eos id in config.json with tiktoken eos id
            self.gguf_writer.add_eos_token_id(tokenizer.eos_id)  # ty: ignore[unresolved-attribute]
        else:
            raise NotImplementedError(f"Deepseek pre-tokenizer {tokpre!r} is not supported yet!")

    def set_gguf_parameters(self):
        # note: To enable MLA KV cache, attention needs to be converted into MQA (ie: GQA with 1 group)
        self.hparams["num_key_value_heads"] = 1

        super().set_gguf_parameters()
        self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])

        # KDA & MLA params
        # Get ssm_d_conv from linear_attn_config.short_conv_kernel_size or ssm_d_conv
        linear_attn_config = self.hparams["linear_attn_config"]
        # n_head == 0 for KDA layers, n_head > 0 for MLA layers
        # full_attention_layers list will be used to distinguish layer type
        _num_kv_heads = list()
        _full_attn_layers = linear_attn_config["full_attn_layers"]
        for il in range(self.hparams["num_hidden_layers"]):
            if il + 1 in _full_attn_layers:
                _num_kv_heads.append(self.hparams["num_key_value_heads"])
            else:
                _num_kv_heads.append(0)
        assert len(_num_kv_heads) == self.hparams["num_hidden_layers"]
        self.gguf_writer.add_head_count_kv(_num_kv_heads)

        if (ssm_d_conv := linear_attn_config.get("short_conv_kernel_size")) is not None:
            self.gguf_writer.add_ssm_conv_kernel(ssm_d_conv)
        if (kda_head_dim := linear_attn_config.get("head_dim")) is not None:
            self.gguf_writer.add_kda_head_dim(kda_head_dim)

        # MLA params - use add_* methods that handle arch substitution
        # Support both HuggingFace naming (q_lora_rank, kv_lora_rank) and internal naming (n_lora_q, n_lora_kv)
        if (q_lora_rank := self.find_hparam(["q_lora_rank", "n_lora_q"], optional=True)) is not None:
            self.gguf_writer.add_q_lora_rank(q_lora_rank)
        # To enable MLA KV cache, MLA needs to be converted into MQA with larger heads, then decompresses to MHA
        kv_lora_rank = self.find_hparam(["kv_lora_rank", "n_lora_kv"], optional=False)
        self.gguf_writer.add_kv_lora_rank(kv_lora_rank)

        # MLA head dimensions
        # Support HuggingFace naming: qk_nope_head_dim, qk_rope_head_dim, v_head_dim
        qk_nope_head_dim = self.hparams.get("qk_nope_head_dim")
        # Rotation - use qk_rope_head_dim for Kimi
        qk_rope_head_dim = self.find_hparam(["qk_rope_head_dim", "n_rot"], optional=False)
        self.gguf_writer.add_rope_dimension_count(qk_rope_head_dim)
        self.gguf_writer.add_key_length(kv_lora_rank + qk_rope_head_dim)
        v_head_dim = self.hparams.get("v_head_dim")

        # Calculate n_embd_head_k_mla = qk_nope_head_dim + qk_rope_head_dim
        if (n_embd_head_k_mla := self.find_hparam(["n_embd_head_k_mla"], optional=True)) is not None:
            self.gguf_writer.add_key_length_mla(n_embd_head_k_mla)
        elif qk_nope_head_dim is not None:
            n_embd_head_k_mla = qk_nope_head_dim + qk_rope_head_dim
            self.gguf_writer.add_key_length_mla(n_embd_head_k_mla)

        # n_embd_head_v_mla = v_head_dim
        if (n_embd_head_v_mla := self.hparams.get("n_embd_head_v_mla")) is not None:
            self.gguf_writer.add_value_length_mla(n_embd_head_v_mla)
        elif v_head_dim is not None:
            self.gguf_writer.add_value_length_mla(v_head_dim)

        # moe_intermediate_size (1024 for Kimi)
        self.gguf_writer.add_expert_feed_forward_length(self.hparams["moe_intermediate_size"])
        # num_shared_experts (1 for Kimi)
        self.gguf_writer.add_expert_shared_count(self.hparams["num_shared_experts"])
        # first_k_dense_replace (1 for Kimi - first layer uses dense MLP)
        self.gguf_writer.add_leading_dense_block_count(self.hparams["first_k_dense_replace"])
        # Routed scaling factor (expert_weights_scale = 2.446 for Kimi)
        self.gguf_writer.add_expert_weights_scale(self.hparams["routed_scaling_factor"])

    def prepare_tensors(self):
        super().prepare_tensors()
        if self._experts is not None:
            experts = [k for d in self._experts for k in d.keys()]
            if len(experts) > 0:
                raise ValueError(f"Unprocessed experts: {experts}")

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        logger.info(f"Processing {name}: shape before = {tuple(data_torch.shape)}")

        # Handle KDA conv1d weights
        # HuggingFace/vLLM stores as [d_inner, d_conv] (2D), memory layout: conv_step changes fastest
        # llama.cpp expects ggml ne = [d_conv, 1, d_inner, 1], memory layout: ne[0]=d_conv changes fastest
        # GGUF reverses numpy shape when writing, so numpy (1, d_inner, 1, d_conv) -> ggml ne = [d_conv, 1, d_inner, 1]
        # Memory layouts match: both have conv_step (d_conv) changing fastest
        if name.endswith((".q_conv1d.weight", ".k_conv1d.weight", ".v_conv1d.weight")):
            # HF shape: [d_inner, d_conv] e.g. [4096, 4]
            # Target numpy shape: (1, d_inner, 1, d_conv) -> ggml ne = [d_conv, 1, d_inner, 1]
            if data_torch.ndim == 2:
                d_inner, d_conv = data_torch.shape
                # Reshape to (1, d_inner, 1, d_conv) - memory layout preserved (d_conv fastest)
                data_torch = data_torch.reshape(1, d_inner, 1, d_conv)
                logger.info(f"Reshaped conv1d weight {name}: [d_inner={d_inner}, d_conv={d_conv}] -> numpy {tuple(data_torch.shape)} -> ggml ne=[{d_conv}, 1, {d_inner}, 1]")
            elif data_torch.ndim == 3:
                # Already 3D [d_inner, 1, d_conv] from unsqueeze
                d_inner, _, d_conv = data_torch.shape
                data_torch = data_torch.reshape(1, d_inner, 1, d_conv)
                logger.info(f"Reshaped conv1d weight {name}: [d_inner={d_inner}, 1, d_conv={d_conv}] -> numpy {tuple(data_torch.shape)} -> ggml ne=[{d_conv}, 1, {d_inner}, 1]")

        # Handle A_log: iHF stores as [1, 1, num_heads, 1]
        # llama.cpp expects ggml ne = [1, num_heads, 1, 1]
        # GGUF reverses numpy shape: numpy (1, 1, num_heads, 1) -> ggml ne = [1, num_heads, 1, 1]
        if name.endswith(".A_log"):
            data_torch = -torch.exp(data_torch)
        if name.endswith(".dt_bias"):
            name = name.rpartition(".dt_bias")[0] + ".dt_proj.bias"
            logger.info("Changed dt_bias to dt_proj.bias")

        # process the experts separately
        if name.find("block_sparse_moe.experts") != -1:
            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
            assert bid is not None

            if self._experts is None:
                self._experts = [{} for _ in range(self.block_count)]

            self._experts[bid][name] = data_torch

            if len(self._experts[bid]) >= n_experts * 3:
                # merge the experts into a single 3d tensor
                # w1: gate, w2: down, w3: up
                for wid, tname in [("w1", gguf.MODEL_TENSOR.FFN_GATE_EXP),
                                   ("w2", gguf.MODEL_TENSOR.FFN_DOWN_EXP),
                                   ("w3", gguf.MODEL_TENSOR.FFN_UP_EXP)]:
                    datas: list[Tensor] = []
                    for xid in range(n_experts):
                        ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{wid}.weight"
                        datas.append(self._experts[bid][ename])
                        del self._experts[bid][ename]
                    data_torch = torch.stack(datas, dim=0)
                    new_name = self.format_tensor_name(tname, bid)
                    yield from super().modify_tensors(data_torch, new_name, bid)
            return

        # note: MLA with the absorption optimization, needs these two split and k_b_proj transposed
        if name.endswith("kv_b_proj.weight"):
            name_kb = name.replace("kv_b_proj", "k_b_proj")
            name_vb = name.replace("kv_b_proj", "v_b_proj")
            n_head_kv = self.hparams["num_key_value_heads"]
            v_head_dim = self.find_hparam(["n_embd_head_v_mla", "v_head_dim"], optional=False)
            qk_nope_head_dim = self.hparams["qk_nope_head_dim"]
            logger.info("Split kv_b n_head_kv %d\n" % n_head_kv)
            assert data_torch.shape[0] == n_head_kv * (v_head_dim + qk_nope_head_dim)
            kv_b = data_torch.view(n_head_kv, v_head_dim + qk_nope_head_dim, data_torch.shape[-1])
            k_b, v_b = torch.split(kv_b, [qk_nope_head_dim, v_head_dim], dim=1)
            k_b = k_b.transpose(1, 2)
            yield from super().modify_tensors(k_b, name_kb, bid)
            yield from super().modify_tensors(v_b, name_vb, bid)
            return

        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("InternLM2ForCausalLM")
class InternLM2Model(TextModel):
    model_arch = gguf.MODEL_ARCH.INTERNLM2

    def set_vocab(self):
        # (TODO): Is there a better way?
        # Copy from _set_vocab_sentencepiece, The only difference is that we will treat the character
        # \x00 specially and convert it into an emoji character to prevent it from being mistakenly
        # recognized as an empty string in C++.
        from sentencepiece import SentencePieceProcessor
        from sentencepiece import sentencepiece_model_pb2 as model

        tokenizer_path = self.dir_model / 'tokenizer.model'

        tokens: list[bytes] = []
        scores: list[float] = []
        toktypes: list[int] = []

        if not tokenizer_path.is_file():
            logger.error(f'Error: Missing {tokenizer_path}')
            sys.exit(1)

        sentencepiece_model = model.ModelProto()  # pyright: ignore[reportAttributeAccessIssue] # ty: ignore[unresolved-attribute]
        sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read())
        add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix

        tokenizer = SentencePieceProcessor()
        tokenizer.LoadFromFile(str(tokenizer_path))

        vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())

        for token_id in range(vocab_size):
            piece = tokenizer.IdToPiece(token_id)
            text = piece.encode("utf-8")
            score = tokenizer.GetScore(token_id)
            if text == b"\x00":
                # (TODO): fixme
                # Hack here and replace the \x00 characters.
                logger.warning(f"InternLM2 convert token '{text}' to '🐉'!")
                text = "🐉".encode("utf-8")

            toktype = SentencePieceTokenTypes.NORMAL
            if tokenizer.IsUnknown(token_id):
                toktype = SentencePieceTokenTypes.UNKNOWN
            elif tokenizer.IsControl(token_id):
                toktype = SentencePieceTokenTypes.CONTROL
            elif tokenizer.IsUnused(token_id):
                toktype = SentencePieceTokenTypes.UNUSED
            elif tokenizer.IsByte(token_id):
                toktype = SentencePieceTokenTypes.BYTE
            # take care of ununsed raw token
            if piece.startswith('[UNUSED'):
                toktype = SentencePieceTokenTypes.UNUSED

            tokens.append(text)
            scores.append(score)
            toktypes.append(toktype)

        added_tokens_file = self.dir_model / 'added_tokens.json'
        if added_tokens_file.is_file():
            with open(added_tokens_file, "r", encoding="utf-8") as f:
                added_tokens_json = json.load(f)

                for key in added_tokens_json:
                    tokens.append(key.encode("utf-8"))
                    scores.append(-1000.0)
                    toktypes.append(SentencePieceTokenTypes.USER_DEFINED)

        chat_eos_token = '<|im_end|>'
        chat_eos_token_id = None

        tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
        if tokenizer_config_file.is_file():
            with open(tokenizer_config_file, "r", encoding="utf-8") as f:
                tokenizer_config_json = json.load(f)
                added_tokens_decoder = tokenizer_config_json.get("added_tokens_decoder", {})
                for token_id, foken_data in added_tokens_decoder.items():
                    token_id = int(token_id)
                    token = foken_data["content"]
                    if token == chat_eos_token:
                        chat_eos_token_id = token_id
                    token = token.encode("utf-8")
                    if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
                        if tokens[token_id] != token:
                            logger.warning(f'replacing token {token_id}: {tokens[token_id].decode("utf-8")!r} -> {token.decode("utf-8")!r}')
                    tokens[token_id] = token
                    scores[token_id] = -1000.0
                    toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
                    if foken_data.get("special"):
                        toktypes[token_id] = SentencePieceTokenTypes.CONTROL

        tokenizer_file = self.dir_model / 'tokenizer.json'
        if tokenizer_file.is_file():
            with open(tokenizer_file, "r", encoding="utf-8") as f:
                tokenizer_json = json.load(f)
                added_tokens = tokenizer_json.get("added_tokens", [])
                for foken_data in added_tokens:
                    token_id = int(foken_data["id"])
                    token = foken_data["content"]
                    if token == chat_eos_token:
                        chat_eos_token_id = token_id
                    token = token.encode("utf-8")
                    if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
                        if tokens[token_id] != token:
                            logger.warning(f'replacing token {token_id}: {tokens[token_id].decode("utf-8")!r} -> {token.decode("utf-8")!r}')
                    tokens[token_id] = token
                    scores[token_id] = -1000.0
                    toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
                    if foken_data.get("special"):
                        toktypes[token_id] = SentencePieceTokenTypes.CONTROL

        self.gguf_writer.add_tokenizer_model("llama")
        self.gguf_writer.add_tokenizer_pre("default")
        self.gguf_writer.add_token_list(tokens)
        self.gguf_writer.add_token_scores(scores)
        self.gguf_writer.add_token_types(toktypes)
        self.gguf_writer.add_add_space_prefix(add_prefix)

        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
        old_eos = special_vocab.special_token_ids["eos"]
        if chat_eos_token_id is not None:
            # For the chat model, we replace the eos with '<|im_end|>'.
            # TODO: this is a hack, should be fixed
            #       https://github.com/ggml-org/llama.cpp/pull/6745#issuecomment-2067687048
            special_vocab.special_token_ids["eos"] = chat_eos_token_id
            logger.warning(f"Replace eos:{old_eos} with a special token:{chat_eos_token_id}"
                           " in chat mode so that the conversation can end normally.")

        special_vocab.add_to_gguf(self.gguf_writer)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        num_heads = self.hparams["num_attention_heads"]
        num_kv_heads = self.hparams["num_key_value_heads"]
        n_embd = self.hparams["hidden_size"]
        q_per_kv = num_heads // num_kv_heads
        head_dim = n_embd // num_heads
        num_groups = num_heads // q_per_kv

        if bid is not None and f"model.layers.{bid}.attention.wqkv" in name:
            qkv = data_torch

            qkv = qkv.reshape((num_groups, q_per_kv + 2, head_dim, n_embd))
            q, k, v = qkv[:, : q_per_kv], qkv[:, -2], qkv[:, -1]

            # The model weights of q and k equire additional reshape.
            q = LlamaModel.permute(q.reshape((-1, q.shape[-1])), num_heads, num_heads)
            k = LlamaModel.permute(k.reshape((-1, k.shape[-1])), num_heads, num_kv_heads)
            v = v.reshape((-1, v.shape[-1]))

            yield from super().modify_tensors(q, self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_Q, bid), bid)
            yield from super().modify_tensors(k, self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_K, bid), bid)
            yield from super().modify_tensors(v, self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_V, bid), bid)
        else:
            yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("InternLM3ForCausalLM")
class InternLM3Model(TextModel):
    model_arch = gguf.MODEL_ARCH.LLAMA

    def set_vocab(self):
        tokens, scores, toktypes = self._create_vocab_sentencepiece()

        self.gguf_writer.add_tokenizer_model("llama")
        self.gguf_writer.add_tokenizer_pre("default")
        self.gguf_writer.add_token_list(tokens)
        self.gguf_writer.add_token_scores(scores)
        self.gguf_writer.add_token_types(toktypes)

        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))

        tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
        if tokenizer_config_file.is_file():
            with open(tokenizer_config_file, "r", encoding="utf-8") as f:
                tokenizer_config_json = json.load(f)
                if "add_prefix_space" in tokenizer_config_json:
                    self.gguf_writer.add_add_space_prefix(tokenizer_config_json["add_prefix_space"])

                if "added_tokens_decoder" in tokenizer_config_json:
                    for token_id, token_data in tokenizer_config_json["added_tokens_decoder"].items():
                        if token_data.get("special"):
                            token_id = int(token_id)
                            token = token_data["content"]
                            special_vocab._set_special_token(token, token_id)
                            # update eos token
                            if token == '<|im_end|>' and "eos" in special_vocab.special_token_ids:
                                special_vocab.special_token_ids["eos"] = token_id

        special_vocab.add_to_gguf(self.gguf_writer)

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        hparams = self.hparams
        self.gguf_writer.add_vocab_size(hparams["vocab_size"])

        if (rope_dim := hparams.get("head_dim")) is None:
            rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
        self.gguf_writer.add_rope_dimension_count(rope_dim)

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        if name.startswith(("mlp", "vision_model")):
            # skip visual tensors
            return None

        return super().filter_tensors(item)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        n_head = self.hparams["num_attention_heads"]
        n_kv_head = self.hparams.get("num_key_value_heads")
        if name.endswith(("q_proj.weight", "q_proj.bias")):
            data_torch = LlamaModel.permute(data_torch, n_head, n_head)
        if name.endswith(("k_proj.weight", "k_proj.bias")):
            data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)
        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("BertModel", "BertForMaskedLM", "CamembertModel", "BertForSequenceClassification")
class BertModel(TextModel):
    model_arch = gguf.MODEL_ARCH.BERT

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.vocab_size = None

        if cls_out_labels := self.hparams.get("id2label"):
            if len(cls_out_labels) == 2 and cls_out_labels[0] == "LABEL_0":
                # Remove dummy labels added by AutoConfig
                cls_out_labels = None
        self.cls_out_labels = cls_out_labels

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        self.gguf_writer.add_causal_attention(False)
        self._try_set_pooling_type()

        if self.cls_out_labels:
            self.gguf_writer.add_classifier_output_labels([v for k, v in sorted(self.cls_out_labels.items())])

    def set_vocab(self):
        tokens, toktypes, tokpre = self.get_vocab_base()
        self.vocab_size = len(tokens)

        # we need this to validate the size of the token_type embeddings
        # though currently we are passing all zeros to the token_type embeddings
        # "Sequence A" or "Sequence B"
        self.gguf_writer.add_token_type_count(self.hparams.get("type_vocab_size", 1))

        # convert to phantom space vocab
        def phantom(tok, toktype):
            if toktype == gguf.TokenType.CONTROL:
                return tok
            if tok.startswith("##"):
                return tok[2:]
            return "\u2581" + tok
        assert len(tokens) == len(toktypes)
        tokens = list(map(phantom, tokens, toktypes))

        # add vocab to gguf
        self.gguf_writer.add_tokenizer_model("bert")
        self.gguf_writer.add_tokenizer_pre(tokpre)
        self.gguf_writer.add_token_list(tokens)
        self.gguf_writer.add_token_types(toktypes)

        # handle special tokens
        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
        special_vocab.add_to_gguf(self.gguf_writer)

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        if name.startswith("bert."):
            name = name[5:]

        if name.endswith(".gamma"):
            name = name[:-6] + ".weight"

        if name.endswith(".beta"):
            name = name[:-5] + ".bias"

        # we are only using BERT for embeddings so we don't need the pooling layer
        if name in ("embeddings.position_ids", "pooler.dense.weight", "pooler.dense.bias"):
            return None

        if name.startswith("cls.predictions"):
            return None

        if name.startswith("cls.seq_relationship"):
            return None

        return super().filter_tensors((name, gen))

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        if self.cls_out_labels:
            # For BertForSequenceClassification (direct projection layer)
            if name == "classifier.weight":
                name = "classifier.out_proj.weight"

            if name == "classifier.bias":
                name = "classifier.out_proj.bias"

        yield from super().modify_tensors(data_torch, name, bid)

    def _xlmroberta_tokenizer_init(self) -> None:
        # we need the pad_token_id to know how to chop down position_embd matrix
        if (pad_token_id := self.hparams.get("pad_token_id")) is not None:
            self._position_offset = 1 + pad_token_id
            if "max_position_embeddings" in self.hparams:
                self.hparams["max_position_embeddings"] -= self._position_offset
        else:
            self._position_offset = None

    def _xlmroberta_set_vocab(self) -> None:
        # to avoid TypeError: Descriptors cannot be created directly
        # exception when importing sentencepiece_model_pb2
        os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python"
        from sentencepiece import SentencePieceProcessor
        from sentencepiece import sentencepiece_model_pb2 as model

        tokenizer_path = self.dir_model / 'sentencepiece.bpe.model'

        tokenizer_json = {}
        tokenizer_config_json = {}
        if not tokenizer_path.is_file():
            tokenizer_path = self.dir_model / 'tokenizer.json'
            tokenizer_config_path = self.dir_model / 'tokenizer_config.json'

            if not tokenizer_path.is_file():
                raise FileNotFoundError(f"File not found: {tokenizer_path}")

            from base64 import b64decode
            from transformers import AutoTokenizer
            tokenizer = AutoTokenizer.from_pretrained(self.dir_model)

            with open(tokenizer_path, "r", encoding="utf-8") as fp:
                tokenizer_json = json.load(fp)

            if tokenizer_config_path.is_file():
                with open(tokenizer_config_path, "r", encoding="utf-8") as fp:
                    tokenizer_config_json = json.load(fp)

            add_prefix = tokenizer.add_prefix_space  # ty: ignore[unresolved-attribute]
            remove_whitespaces = tokenizer.clean_up_tokenization_spaces  # ty: ignore[unresolved-attribute]
            precompiled_charsmap = b64decode(tokenizer_json["normalizer"]["precompiled_charsmap"])

            vocab_size = max(self.hparams.get("vocab_size", 0), tokenizer.vocab_size)  # ty: ignore[unresolved-attribute]
        else:
            sentencepiece_model = model.ModelProto()  # pyright: ignore[reportAttributeAccessIssue] # ty: ignore[unresolved-attribute]
            sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read())
            assert sentencepiece_model.trainer_spec.model_type == 1  # UNIGRAM

            add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix
            remove_whitespaces = sentencepiece_model.normalizer_spec.remove_extra_whitespaces
            precompiled_charsmap = sentencepiece_model.normalizer_spec.precompiled_charsmap

            tokenizer = SentencePieceProcessor()
            tokenizer.LoadFromFile(str(tokenizer_path))

            vocab_size = max(self.hparams.get("vocab_size", 0), tokenizer.vocab_size())

        tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
        scores: list[float] = [-10000.0] * vocab_size
        toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size

        if isinstance(tokenizer, SentencePieceProcessor):
            for token_id in range(tokenizer.vocab_size()):
                piece = tokenizer.IdToPiece(token_id)
                text = piece.encode("utf-8")
                score = tokenizer.GetScore(token_id)

                toktype = SentencePieceTokenTypes.NORMAL
                if tokenizer.IsUnknown(token_id):
                    toktype = SentencePieceTokenTypes.UNKNOWN
                elif tokenizer.IsControl(token_id):
                    toktype = SentencePieceTokenTypes.CONTROL
                elif tokenizer.IsUnused(token_id):
                    toktype = SentencePieceTokenTypes.UNUSED
                elif tokenizer.IsByte(token_id):
                    toktype = SentencePieceTokenTypes.BYTE

                tokens[token_id] = text
                scores[token_id] = score
                toktypes[token_id] = toktype
        else:
            added_vocab = tokenizer.get_added_vocab()  # ty: ignore[unresolved-attribute]
            unk_token = tokenizer_config_json.get("unk_token")
            unk_token_id = added_vocab.get(unk_token, tokenizer_json["model"].get("unk_id", 3))  # ty: ignore[no-matching-overload]

            for token_id in range(tokenizer.vocab_size):  # ty: ignore[unresolved-attribute]
                piece = tokenizer._convert_id_to_token(token_id)  # ty: ignore[unresolved-attribute]
                if (piece := tokenizer._convert_id_to_token(token_id)) is not None:  # ty: ignore[unresolved-attribute]
                    text = piece.encode("utf-8")
                    score = tokenizer_json["model"]["vocab"][token_id][1]

                    toktype = SentencePieceTokenTypes.NORMAL
                    if token_id == unk_token_id:
                        toktype = SentencePieceTokenTypes.UNKNOWN
                    elif token_id in tokenizer.all_special_ids:  # ty: ignore[unresolved-attribute]
                        toktype = SentencePieceTokenTypes.CONTROL
                    elif token_id in added_vocab.values():
                        toktype = SentencePieceTokenTypes.USER_DEFINED
                    # No reliable way to detect this, but jina doesn't have any
                    # elif tokenizer.IsByte(token_id):
                    #     toktype = SentencePieceTokenTypes.BYTE

                    tokens[token_id] = text
                    scores[token_id] = score
                    toktypes[token_id] = toktype

        if isinstance(tokenizer, SentencePieceProcessor):
            # realign tokens (see HF tokenizer code)
            tokens = [b'<s>', b'<pad>', b'</s>', b'<unk>'] + tokens[3:-1]
            scores = [0.0, 0.0, 0.0, 0.0] + scores[3:-1]
            toktypes = [
                SentencePieceTokenTypes.CONTROL,
                SentencePieceTokenTypes.CONTROL,
                SentencePieceTokenTypes.CONTROL,
                SentencePieceTokenTypes.UNKNOWN,
            ] + toktypes[3:-1]

            if self.model_arch == gguf.MODEL_ARCH.NOMIC_BERT_MOE:
                # Add mask token missing from sentencepiece.bpe.model
                tokens[250001] = b'<mask>'
                scores[250001] = 0.0
                toktypes[250001] = SentencePieceTokenTypes.CONTROL

        self.gguf_writer.add_tokenizer_model("t5")
        self.gguf_writer.add_tokenizer_pre("default")
        self.gguf_writer.add_token_list(tokens)
        self.gguf_writer.add_token_scores(scores)
        self.gguf_writer.add_token_types(toktypes)
        self.gguf_writer.add_add_space_prefix(add_prefix)
        self.gguf_writer.add_token_type_count(self.hparams.get("type_vocab_size", 1))
        self.gguf_writer.add_remove_extra_whitespaces(remove_whitespaces)
        if precompiled_charsmap:
            self.gguf_writer.add_precompiled_charsmap(precompiled_charsmap)

        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
        special_vocab.add_to_gguf(self.gguf_writer)


@ModelBase.register("DistilBertModel", "DistilBertForMaskedLM", "DistilBertForSequenceClassification")
class DistilBertModel(BertModel):
    model_arch = gguf.MODEL_ARCH.BERT

    def set_gguf_parameters(self):
        self.gguf_writer.add_layer_norm_eps(1e-12)
        logger.info("gguf: layer norm epsilon = 1e-12")
        super().set_gguf_parameters()

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        if name.startswith("distilbert."):
            name = name[11:]

        # These layers act as MLM head, so we don't need them
        if name.startswith("vocab_"):
            return None

        return super().filter_tensors((name, gen))


@ModelBase.register("RobertaModel", "RobertaForSequenceClassification")
class RobertaModel(BertModel):
    model_arch = gguf.MODEL_ARCH.BERT

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

        # we need the pad_token_id to know how to chop down position_embd matrix
        if (pad_token_id := self.hparams.get("pad_token_id")) is not None:
            self._position_offset = 1 + pad_token_id
            if "max_position_embeddings" in self.hparams:
                self.hparams["max_position_embeddings"] -= self._position_offset
        else:
            self._position_offset = None

    def set_vocab(self):
        """Support BPE tokenizers for roberta models"""
        bpe_tok_path = self.dir_model / "tokenizer.json"
        if bpe_tok_path.exists():
            self._set_vocab_gpt2()

            # we need this to validate the size of the token_type embeddings
            # though currently we are passing all zeros to the token_type embeddings
            # "Sequence A" or "Sequence B"
            self.gguf_writer.add_token_type_count(self.hparams.get("type_vocab_size", 1))

        else:
            return super().set_vocab()

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        # if name starts with "roberta.", remove the prefix
        # e.g. https://huggingface.co/BAAI/bge-reranker-v2-m3/tree/main
        if name.startswith("roberta."):
            name = name[8:]

        return super().filter_tensors((name, gen))

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        # position embeddings start at pad_token_id + 1, so just chop down the weight tensor
        if name == "embeddings.position_embeddings.weight":
            if self._position_offset is not None:
                data_torch = data_torch[self._position_offset:,:]

        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("NomicBertModel")
class NomicBertModel(BertModel):
    model_arch = gguf.MODEL_ARCH.BERT

    def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, **kwargs: Any):
        hparams = kwargs.pop("hparams", None)
        if hparams is None:
            hparams = ModelBase.load_hparams(dir_model, False)

        self.is_moe = bool(hparams.get("moe_every_n_layers"))
        self.model_arch = gguf.MODEL_ARCH.NOMIC_BERT_MOE if self.is_moe else gguf.MODEL_ARCH.NOMIC_BERT

        super().__init__(dir_model, ftype, fname_out, hparams=hparams, **kwargs)

        self._tokenizer_is_xlmroberta = self._is_tokenizer_xlmroberta()
        if self._tokenizer_is_xlmroberta:
            self._xlmroberta_tokenizer_init()

        npos, mtp = self.hparams["n_positions"], self.hparams.get("max_trained_positions", 2048)
        if npos == 8192 and mtp == 2048:
            self.hparams["n_positions"] = 2048  # nomic-embed-text v1 and v1.5 are trained for 2048 tokens.
        elif npos == 2048 and mtp == 2048:
            self.hparams["n_positions"] = 512   # nomic-embed-text-v2-moe is trained for 512 tokens.
        else:
            raise ValueError(f"unrecognized parameters: n_positions={npos}, max_trained_positions={mtp}")

        assert self.hparams["activation_function"] == "gelu" if self.is_moe else "swiglu"

        # this doesn't do anything in the HF version
        assert self.hparams["causal"] is False
        # no bias tensors unless MoE
        assert self.hparams["qkv_proj_bias"] == self.is_moe
        assert self.hparams["mlp_fc1_bias"]  == self.is_moe
        assert self.hparams["mlp_fc2_bias"]  == self.is_moe

        # norm at end of layer
        assert self.hparams["prenorm"] is False
        # standard RoPE
        assert self.hparams["rotary_emb_fraction"] == 1.0
        assert self.hparams["rotary_emb_interleaved"] is False
        assert self.hparams["rotary_emb_scale_base"] is None

    def set_vocab(self) -> None:
        if self._tokenizer_is_xlmroberta:
            return self._xlmroberta_set_vocab()
        return super().set_vocab()

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        # If the tensor is an experts bias tensor, skip it.
        if "mlp.experts.bias" in name:
            return None

        return super().filter_tensors(item)

    def modify_tensors(self, data_torch: torch.Tensor, name: str, bid: int | None) -> Iterable[tuple[str, torch.Tensor]]:
        n_experts = self.find_hparam(["num_local_experts", "num_experts"])
        if "mlp.experts.mlp.w1" in name:
            data_torch = data_torch.view(n_experts, self.hparams["n_inner"], self.hparams["n_embd"])
            name += ".weight"

        if "mlp.experts.mlp.w2" in name:
            data_torch = data_torch.view(n_experts, self.hparams["n_inner"], self.hparams["n_embd"])
            data_torch = data_torch.transpose(1, 2)
            name += ".weight"

        yield from super().modify_tensors(data_torch, name, bid)

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        if self.is_moe:
            self.gguf_writer.add_moe_every_n_layers(self.hparams["moe_every_n_layers"])
            self.gguf_writer.add_expert_used_count(self.hparams["moe_top_k"])

    def _is_tokenizer_xlmroberta(self) -> bool:
        with open(self.dir_model / "tokenizer.json") as f:
            tokenizer_json = json.load(f)
        toktyp = tokenizer_json["model"]["type"]
        if toktyp == "Unigram":
            return True
        if toktyp == "WordPiece":
            return False
        raise ValueError(f"unknown tokenizer: {toktyp}")


@ModelBase.register("NeoBERT", "NeoBERTLMHead", "NeoBERTForSequenceClassification")
class NeoBert(BertModel):
    model_arch = gguf.MODEL_ARCH.NEO_BERT

    def set_gguf_parameters(self):
        super().set_gguf_parameters()

        # NeoBERT uses 2/3 of the intermediate size as feed forward length
        self.gguf_writer.add_feed_forward_length(int(2 * self.hparams["intermediate_size"] / 3))
        self.gguf_writer.add_rope_freq_base(10000.0)  # default value for NeoBERT
        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)

        f_rms_eps = self.hparams.get("norm_eps", 1e-6)  # default value for NeoBERT
        self.gguf_writer.add_layer_norm_rms_eps(f_rms_eps)
        logger.info(f"gguf: rms norm epsilon = {f_rms_eps}")

        self.gguf_writer.add_pooling_type(gguf.PoolingType.CLS) # https://huggingface.co/chandar-lab/NeoBERT#how-to-use

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        if name.startswith("decoder."):
            return None

        if name.startswith("model."):
            name = name[6:]

        return super().filter_tensors((name, gen))


@ModelBase.register("EuroBertModel", "JinaEmbeddingsV5Model")
class EuroBertModel(TextModel):
    model_arch = gguf.MODEL_ARCH.EUROBERT

    def set_vocab(self):
        self.gguf_writer.add_add_bos_token(False)
        self._set_vocab_gpt2()

    def set_gguf_parameters(self):
        super().set_gguf_parameters()

        # EuroBert is bidirectional (encoder)
        self.gguf_writer.add_causal_attention(False)

        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)

        self._try_set_pooling_type()

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        if name.startswith("model."):
            name = name[6:]

        return super().filter_tensors((name, gen))


@ModelBase.register("XLMRobertaModel", "XLMRobertaForSequenceClassification")
class XLMRobertaModel(BertModel):
    model_arch = gguf.MODEL_ARCH.BERT
    _lora_files = {}
    _lora_names = []

    def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, **kwargs: Any):
        hparams = kwargs.pop("hparams", None)
        if hparams is None:
            hparams = ModelBase.load_hparams(dir_model, False)

        if lora_names := hparams.get("lora_adaptations"):
            self._lora_names = lora_names
            self.model_arch = gguf.MODEL_ARCH.JINA_BERT_V3

        super().__init__(dir_model, ftype, fname_out, hparams=hparams, **kwargs)
        self._xlmroberta_tokenizer_init()

    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
        if self._lora_names:
            for name in self._lora_names:
                fname = self.add_prefix_to_filename(self.fname_out, f"lora-{name}-")
                self._lora_files[name] = gguf.GGUFWriter(fname, arch=gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=self.use_temp_file, dry_run=self.dry_run)

        return super().generate_extra_tensors()

    def set_type(self):
        for lora_writer in self._lora_files.values():
            lora_writer.add_type(gguf.GGUFType.ADAPTER)
            lora_writer.add_string(gguf.Keys.Adapter.TYPE, "lora")
        super().set_type()

    def set_vocab(self):
        self._xlmroberta_set_vocab()

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        # if name starts with "roberta.", remove the prefix
        # e.g. https://huggingface.co/BAAI/bge-reranker-v2-m3/tree/main
        if name.startswith("roberta."):
            name = name[8:]

        # jina-embeddings-v3
        if ".parametrizations." in name:
            name = name.replace(".parametrizations.", ".")
            if name.endswith(".original"):
                name = name[:-9]

        return super().filter_tensors((name, gen))

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        # position embeddings start at pad_token_id + 1, so just chop down the weight tensor
        if name == "embeddings.position_embeddings.weight":
            if self._position_offset is not None:
                data_torch = data_torch[self._position_offset:,:]

        if name.endswith(".0.lora_A") or name.endswith(".0.lora_B"):
            if name.startswith("pooler.dense"):
                return

            num_loras = data_torch.size(0)
            assert num_loras == len(self._lora_names)

            # Split out each LoRA in their own GGUF
            for i, lora_writer in enumerate(self._lora_files.values()):
                new_name = self.map_tensor_name(name[:-9]) + name[-7:].lower()
                data = data_torch[i, :, :]
                # Transpose/flip token_embd/types into correct shape
                if new_name == "token_embd.weight.lora_b":
                    data = data.T
                elif new_name.startswith("token_types.weight."):
                    new_name = new_name[:-1] + ("a" if new_name[-1:] == "b" else "b")
                lora_writer.add_tensor(new_name, data.float().numpy(), raw_dtype=gguf.GGMLQuantizationType.F32)

            return

        yield from super().modify_tensors(data_torch, name, bid)

    def set_gguf_parameters(self):
        super().set_gguf_parameters()

        # jina-embeddings-v3
        lora_alpha = self.hparams.get("lora_alpha")
        if lora_prompt_prefixes := self.hparams.get("task_instructions"):
            assert self._lora_files and all(lora_name in lora_prompt_prefixes for lora_name in self._lora_files.keys())
        for lora_name, lora_writer in self._lora_files.items():
            lora_writer.add_float32(gguf.Keys.Adapter.LORA_ALPHA, lora_alpha if lora_alpha is not None else 1.0)
            lora_writer.add_string(gguf.Keys.Adapter.LORA_TASK_NAME, lora_name)
            if lora_prompt_prefixes:
                lora_writer.add_string(gguf.Keys.Adapter.LORA_PROMPT_PREFIX, lora_prompt_prefixes[lora_name])

    def write(self):
        super().write()
        for lora_writer in self._lora_files.values():
            lora_writer.write_header_to_file()
            lora_writer.write_kv_data_to_file()
            lora_writer.write_tensors_to_file(progress=True)
            lora_writer.close()


@ModelBase.register("GemmaForCausalLM")
class GemmaModel(TextModel):
    model_arch = gguf.MODEL_ARCH.GEMMA

    def set_vocab(self):
        self._set_vocab_sentencepiece()

        # TODO: these special tokens should be exported only for the CodeGemma family
        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False,
                                          special_token_types = ['prefix', 'suffix', 'middle', 'fsep', 'eot'])
        special_vocab._set_special_token("prefix", 67)
        special_vocab._set_special_token("suffix", 69)
        special_vocab._set_special_token("middle", 68)
        special_vocab._set_special_token("fsep",   70)
        special_vocab._set_special_token("eot",    107)
        special_vocab.chat_template = None  # do not add it twice
        special_vocab.add_to_gguf(self.gguf_writer)

        self.gguf_writer.add_add_space_prefix(False)

    def set_gguf_parameters(self):
        hparams = self.hparams

        self.gguf_writer.add_context_length(hparams["max_position_embeddings"])
        self.gguf_writer.add_embedding_length(hparams["hidden_size"])
        self.gguf_writer.add_block_count(self.block_count)
        self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
        self.gguf_writer.add_head_count(hparams["num_attention_heads"])
        self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"] if "num_key_value_heads" in hparams else hparams["num_attention_heads"])
        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
        self.gguf_writer.add_key_length(hparams["head_dim"])
        self.gguf_writer.add_value_length(hparams["head_dim"])
        self.gguf_writer.add_file_type(self.ftype)

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        # lm_head is not used in llama.cpp, while autoawq will include this tensor in model
        # To prevent errors, skip loading lm_head.weight.
        if name == "lm_head.weight":
            logger.debug(f"Skipping get tensor {name!r} in safetensors so that convert can end normally.")
            return None

        return super().filter_tensors(item)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        # ref: https://github.com/huggingface/transformers/blob/fc37f38915372c15992b540dfcbbe00a916d4fc6/src/transformers/models/gemma/modeling_gemma.py#L89
        if name.endswith("norm.weight"):
            data_torch = data_torch + 1

        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("Gemma2ForCausalLM")
class Gemma2Model(TextModel):
    model_arch = gguf.MODEL_ARCH.GEMMA2

    def set_vocab(self):
        self._set_vocab_sentencepiece()

        self.gguf_writer.add_add_space_prefix(False)

    def set_gguf_parameters(self):
        hparams = self.hparams

        self.gguf_writer.add_context_length(hparams["max_position_embeddings"])
        self.gguf_writer.add_embedding_length(hparams["hidden_size"])
        self.gguf_writer.add_block_count(self.block_count)
        self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
        self.gguf_writer.add_head_count(hparams["num_attention_heads"])
        self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"] if "num_key_value_heads" in hparams else hparams["num_attention_heads"])
        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
        self.gguf_writer.add_key_length(hparams["head_dim"])
        self.gguf_writer.add_value_length(hparams["head_dim"])
        self.gguf_writer.add_file_type(self.ftype)
        self.gguf_writer.add_attn_logit_softcapping(
            self.hparams["attn_logit_softcapping"]
        )
        self.gguf_writer.add_final_logit_softcapping(
            self.hparams["final_logit_softcapping"]
        )
        self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        # lm_head is not used in llama.cpp, while autoawq will include this tensor in model
        # To prevent errors, skip loading lm_head.weight.
        if name == "lm_head.weight":
            logger.debug(f"Skipping get tensor {name!r} in safetensors so that convert can end normally.")
            return None

        return super().filter_tensors(item)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        # ref: https://github.com/huggingface/transformers/blob/fc37f38915372c15992b540dfcbbe00a916d4fc6/src/transformers/models/gemma/modeling_gemma.py#L89
        if name.endswith("norm.weight"):
            data_torch = data_torch + 1

        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("Gemma3ForCausalLM", "Gemma3ForConditionalGeneration")
class Gemma3Model(TextModel):
    model_arch = gguf.MODEL_ARCH.GEMMA3

    def norm_shift(self, name: str) -> float:
        return 1.0 if name.endswith("norm.weight") else 0.0  # Gemma3RMSNorm adds 1.0 to the norm value

    def set_vocab(self):
        if (self.dir_model / "tokenizer.model").is_file():
            self._set_vocab_sentencepiece()
            self.gguf_writer.add_add_space_prefix(False)
        else:
            self._set_vocab_gpt2()

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        hparams = self.hparams

        # some default values are not specified in the hparams
        self.gguf_writer.add_context_length(hparams.get("max_position_embeddings", 131072))
        self.gguf_writer.add_head_count(hparams.get("num_attention_heads", 8))
        self.gguf_writer.add_layer_norm_rms_eps(self.hparams.get("rms_norm_eps", 1e-6))
        self.gguf_writer.add_key_length(hparams.get("head_dim", 256))
        self.gguf_writer.add_value_length(hparams.get("head_dim", 256))
        self.gguf_writer.add_rope_freq_base(self.rope_parameters.get("full_attention", self.rope_parameters).get("rope_theta", 1_000_000.0)) # for global layers
        # attn_logit_softcapping is removed in Gemma3
        assert hparams.get("attn_logit_softcapping") is None
        if (final_logit_softcap := hparams.get("final_logit_softcapping")):
            self.gguf_writer.add_final_logit_softcapping(final_logit_softcap)
        if hparams.get("sliding_window_pattern") != 1:
            self.gguf_writer.add_sliding_window(hparams["sliding_window"])
        self.gguf_writer.add_head_count_kv(hparams.get("num_key_value_heads", 4))

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        # remove OOV (out-of-vocabulary) rows in token_embd
        if "embed_tokens.weight" in name:
            n_vocab_real = -1
            if (self.dir_model / "tokenizer.model").is_file():
                tokens = self._create_vocab_sentencepiece()[0]
                n_vocab_real = len(tokens)
            else:
                with open(self.dir_model / "tokenizer.json", "r", encoding="utf-8") as f:
                    tokenizer_json = json.load(f)
                    n_vocab_real = len(tokenizer_json["model"]["vocab"]) + len(tokenizer_json["added_tokens"])
            data_torch = data_torch[:n_vocab_real]

        # ref code in Gemma3RMSNorm
        # output = output * (1.0 + self.weight.float())
        # note: this is not the case on gemma3n
        f_shift = self.norm_shift(name)
        if f_shift != 0.0:
            data_torch = data_torch + f_shift

        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("Gemma3TextModel")
class EmbeddingGemma(Gemma3Model):
    model_arch = gguf.MODEL_ARCH.GEMMA_EMBEDDING
    module_paths = []
    dense_features_dims = {}

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        if self.sentence_transformers_dense_modules:
            # read modules.json to determine if model has Dense layers
            modules_file = self.dir_model / "modules.json"
            if modules_file.is_file():
                with open(modules_file, encoding="utf-8") as modules_json_file:
                    mods = json.load(modules_json_file)
                for mod in mods:
                    if mod["type"].endswith("Dense"):
                        mod_path = mod["path"]
                        # check if model.safetensors file for Dense layer exists
                        model_tensors_file = self.dir_model / mod_path / "model.safetensors"
                        if model_tensors_file.is_file():
                            self.module_paths.append(mod_path)
                            # read config.json of the Dense layer to get in/out features
                            mod_conf_file = self.dir_model / mod_path / "config.json"
                            if mod_conf_file.is_file():
                                with open(mod_conf_file, encoding="utf-8") as mod_conf_json_file:
                                    mod_conf = json.load(mod_conf_json_file)
                                    # hparams dense_2_feat_out and dense_3_feat_in are required when loading model's dense weights
                                    prefix = self._get_dense_prefix(mod_path)
                                    if mod_conf["in_features"] is not None and mod_conf["out_features"] is not None:
                                        self.dense_features_dims[prefix] = (mod_conf["in_features"], mod_conf["out_features"])

    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
        from safetensors.torch import load_file
        module_paths = list(self.module_paths)
        for i, module_path in enumerate(module_paths):
            tensors_file = self.dir_model / module_path / "model.safetensors"
            local_tensors = load_file(tensors_file)
            tensor_name = self._get_dense_prefix(module_path)
            for name, local_tensor in local_tensors.items():
                if not name.endswith(".weight"):
                    continue
                orig_name = name.replace("linear", tensor_name)
                name = self.map_tensor_name(orig_name)
                yield name, local_tensor.clone()

    @staticmethod
    def _get_dense_prefix(module_path) -> str:
        """Get the tensor name prefix for the Dense layer from module path."""
        tensor_name = "dense_2" if module_path == "2_Dense" else "dense_3"
        return tensor_name

    def set_gguf_parameters(self):
        super().set_gguf_parameters()

        # Override the sliding window size as it gets adjusted by the Gemma3TextConfig
        # constructor. We want to use the value from the original model's config.json.
        # ref: https://github.com/huggingface/transformers/pull/40700
        with open(self.dir_model / "config.json", "r", encoding="utf-8") as f:
            config = json.load(f)
            orig_sliding_window = config.get("sliding_window")
            if orig_sliding_window is None:
                raise ValueError("sliding_window not found in model config - this is required for the model")

            logger.info(f"Using original sliding_window from config: {orig_sliding_window} "
                        f"instead of {self.hparams['sliding_window']}")
            self.gguf_writer.add_sliding_window(orig_sliding_window)
        if self.sentence_transformers_dense_modules:
            for dense, dims in self.dense_features_dims.items():
                logger.info(f"Setting dense layer {dense} in/out features to {dims}")
                self.gguf_writer.add_dense_features_dims(dense, dims[0], dims[1])

        self._try_set_pooling_type()


@ModelBase.register("Gemma3ForConditionalGeneration")
class Gemma3VisionModel(MmprojModel):
    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        hparams = self.hparams
        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.GEMMA3)
        # default values below are taken from HF transformers code
        self.gguf_writer.add_vision_attention_layernorm_eps(hparams.get("layer_norm_eps", 1e-6))
        self.gguf_writer.add_vision_use_gelu(True)
        # calculate proj_scale_factor (used by tinygemma3 test model)
        image_seq_length = self.preprocessor_config.get("image_seq_length", 256)
        n_per_side = int(image_seq_length ** 0.5)
        image_size = self.hparams["image_size"]
        patch_size = self.hparams["patch_size"]
        proj_scale_factor = (image_size // patch_size) // n_per_side
        if proj_scale_factor > 0 and proj_scale_factor != 4:
            # we only need to write this if it's not the default value
            # in this case, we are converting a test model
            self.gguf_writer.add_vision_projector_scale_factor(proj_scale_factor)

    def tensor_force_quant(self, name, new_name, bid, n_dims):
        # related to https://github.com/ggml-org/llama.cpp/issues/13025
        if "input_projection" in name:
            return gguf.GGMLQuantizationType.F16
        if ".embeddings." in name:
            return gguf.GGMLQuantizationType.F32
        return super().tensor_force_quant(name, new_name, bid, n_dims)

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        if "vision_model.head." in name:
            # skip redundant tensors for tinygemma3
            return None

        if not name.startswith(("multi_modal_projector.", "vision_tower.", "multimodal_projector.", "vision_model.")):
            return None

        name = name.replace("_weight", ".weight")

        return super().filter_tensors((name, gen))

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        # correct norm value ; only this "soft_emb_norm" need to be corrected as it's part of Gemma projector
        # the other norm values are part of SigLIP model, and they are already correct
        # ref code: Gemma3RMSNorm
        if "soft_emb_norm.weight" in name:
            logger.info(f"Correcting norm value for '{name}'")
            data_torch = data_torch + 1

        yield from super().modify_tensors(data_torch, name, bid)


class ConformerAudioModel(MmprojModel):
    _batch_norm_tensors: list[dict[str, Tensor]] | None = None

    @staticmethod
    def is_audio_tensor(name: str):
        return any(p in name for p in ["audio", "codebook", "conformer", "depth_embedding", "depthformer", "depth_linear"])

    def tensor_force_quant(self, name, new_name, bid, n_dims):
        if ConformerAudioModel.is_audio_tensor(name):
            if ".conv" in name or "_conv" in name and ".weight" in name:
                return gguf.GGMLQuantizationType.F32
        return super().tensor_force_quant(name, new_name, bid, n_dims)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        # fold running_mean, running_var and eps into weight and bias for batch_norm
        if "batch_norm" in name:
            if self._batch_norm_tensors is None:
                self._batch_norm_tensors = [{} for _ in range(self.block_count)]
            assert bid is not None
            self._batch_norm_tensors[bid][name] = data_torch

            if len(self._batch_norm_tensors[bid]) < 5:
                return

            weight = self._batch_norm_tensors[bid][f"conformer.layers.{bid}.conv.batch_norm.weight"]
            bias = self._batch_norm_tensors[bid][f"conformer.layers.{bid}.conv.batch_norm.bias"]
            running_mean = self._batch_norm_tensors[bid][f"conformer.layers.{bid}.conv.batch_norm.running_mean"]
            running_var = self._batch_norm_tensors[bid][f"conformer.layers.{bid}.conv.batch_norm.running_var"]
            eps = 1e-5 # default value

            a = weight / torch.sqrt(running_var + eps)
            b = bias - running_mean * a
            yield from super().modify_tensors(a, f"conformer.layers.{bid}.conv.batch_norm.weight", bid)
            yield from super().modify_tensors(b, f"conformer.layers.{bid}.conv.batch_norm.bias", bid)
            return

        # reshape conv weights
        if name.startswith("conformer.pre_encode.conv.") and name.endswith(".bias"):
            data_torch = data_torch[:, None, None]
        if "conv.depthwise_conv" in name and name.endswith(".weight"):
            assert data_torch.shape[1] == 1
            data_torch = data_torch.reshape(data_torch.shape[0], data_torch.shape[2])
        if "conv.pointwise_conv" in name and name.endswith(".weight"):
            assert data_torch.shape[2] == 1
            data_torch = data_torch.reshape(data_torch.shape[0], data_torch.shape[1])

        mapped_name = self.map_tensor_name(name, (".weight", ".bias", ".input_max", ".input_min", ".output_max", ".output_min"))
        yield (mapped_name, data_torch)


@ModelBase.register("DeepseekOCRForCausalLM")
class DeepseekOCRVisionModel(MmprojModel):
    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        hparams = self.hparams
        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.DEEPSEEKOCR)
        # default values below are taken from HF tranformers code
        self.gguf_writer.add_vision_attention_layernorm_eps(hparams.get("layer_norm_eps", 1e-6))
        self.gguf_writer.add_vision_use_gelu(True)
        # calculate proj_scale_factor (used by tinygemma3 test model)
        image_seq_length = self.preprocessor_config.get("image_seq_length", 256)
        n_per_side = int(image_seq_length ** 0.5)
        image_size = self.hparams["image_size"]
        patch_size = self.hparams["patch_size"]
        proj_scale_factor = (image_size // patch_size) // n_per_side
        if proj_scale_factor > 0 and proj_scale_factor != 4:
            # we only need to write this if it's not the default value
            # in this case, we are converting a test model
            self.gguf_writer.add_vision_projector_scale_factor(proj_scale_factor)
        # @bluebread: there's no window_size in config but just add it here anyway
        self.gguf_writer.add_vision_window_size(self.hparams.get("window_size", 14))

        # SAM configuration
        sam_hparams = hparams['sam']
        self.gguf_writer.add_vision_sam_layers_count(sam_hparams['layers'])
        self.gguf_writer.add_vision_sam_embedding_length(sam_hparams['width'])
        self.gguf_writer.add_vision_sam_head_count(sam_hparams['heads'])

    def get_vision_config(self) -> dict[str, Any]:
        vision_config: dict[str, Any] | None = self.global_config.get("vision_config")

        if not vision_config:
            raise ValueError("DeepseekOCR model requires 'vision_config' in the model configuration, but it was not found")

        vision_config['sam'] = vision_config['width']['sam_vit_b']
        vision_config.update(vision_config['width']['clip-l-14-224'])
        vision_config['hidden_size'] = vision_config['width']
        vision_config['num_heads'] = vision_config['heads']
        vision_config['intermediate_size'] = vision_config['heads'] * 4

        return vision_config

    def tensor_force_quant(self, name, new_name, bid, n_dims):
        if ".embeddings." in name or 'pos_embed' in name:
            return gguf.GGMLQuantizationType.F32
        if ".rel_pos_h" in name or '.rel_pos_w' in name:
            return gguf.GGMLQuantizationType.F32
        if ".neck." in name or ".net_" in name:
            return gguf.GGMLQuantizationType.F32
        return super().tensor_force_quant(name, new_name, bid, n_dims)

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        # Only process vision-related tensors, skip language model tensors
        # Vision components: sam_model, vision_model, projector, image_newline, view_seperator
        # Language model components to skip: lm_head, embed_tokens, layers, norm
        if name.startswith(("lm_head.", "model.embed_tokens.", "model.layers.", "model.norm.")):
            return None

        if name.endswith("pos_embed") or name.endswith("rel_pos_h") or name.endswith("rel_pos_w"):
            name += ".weight"

        return super().filter_tensors((name, gen))


@ModelBase.register("Gemma3nForConditionalGeneration")
class Gemma3nVisionAudioModel(ConformerAudioModel):
    has_audio_encoder = True
    has_vision_encoder = True

    # Double indexed mapping for MobileNetV5 blocks (not supported by tensor_mapping.py)
    # This is the only known model having this, so we prefer implementing it outside of tensor_mapping.py
    block_tensor_mapping = {
        "model.vision_tower.timm_model.blocks.{bid}.{sid}.conv_exp.weight":             "v.blk.{bid}.{sid}.conv_exp.weight",
        "model.vision_tower.timm_model.blocks.{bid}.{sid}.bn1.weight":                  "v.blk.{bid}.{sid}.bn1.weight",
        "model.vision_tower.timm_model.blocks.{bid}.{sid}.conv_pwl.weight":             "v.blk.{bid}.{sid}.conv_pwl.weight",
        "model.vision_tower.timm_model.blocks.{bid}.{sid}.bn2.weight":                  "v.blk.{bid}.{sid}.bn2.weight",
        "model.vision_tower.timm_model.blocks.{bid}.{sid}.dw_start.conv.weight":        "v.blk.{bid}.{sid}.dw_start.conv.weight",
        "model.vision_tower.timm_model.blocks.{bid}.{sid}.dw_start.bn.weight":          "v.blk.{bid}.{sid}.dw_start.bn.weight",
        "model.vision_tower.timm_model.blocks.{bid}.{sid}.dw_mid.conv.weight":          "v.blk.{bid}.{sid}.dw_mid.conv.weight",
        "model.vision_tower.timm_model.blocks.{bid}.{sid}.dw_mid.bn.weight":            "v.blk.{bid}.{sid}.dw_mid.bn.weight",
        "model.vision_tower.timm_model.blocks.{bid}.{sid}.pw_exp.conv.weight":          "v.blk.{bid}.{sid}.pw_exp.conv.weight",
        "model.vision_tower.timm_model.blocks.{bid}.{sid}.pw_exp.bn.weight":            "v.blk.{bid}.{sid}.pw_exp.bn.weight",
        "model.vision_tower.timm_model.blocks.{bid}.{sid}.pw_proj.conv.weight":         "v.blk.{bid}.{sid}.pw_proj.conv.weight",
        "model.vision_tower.timm_model.blocks.{bid}.{sid}.pw_proj.bn.weight":           "v.blk.{bid}.{sid}.pw_proj.bn.weight",
        "model.vision_tower.timm_model.blocks.{bid}.{sid}.layer_scale.gamma":           "v.blk.{bid}.{sid}.layer_scale.gamma",
        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.query.proj.weight":      "v.blk.{bid}.{sid}.attn.query.proj.weight",
        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.key.proj.weight":        "v.blk.{bid}.{sid}.attn.key.proj.weight",
        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.value.proj.weight":      "v.blk.{bid}.{sid}.attn.value.proj.weight",
        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.output.proj.weight":     "v.blk.{bid}.{sid}.attn.output.proj.weight",
        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.key.down_conv.weight":   "v.blk.{bid}.{sid}.attn.key.down_conv.weight",
        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.key.norm.weight":        "v.blk.{bid}.{sid}.attn.key.norm.weight",
        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.value.down_conv.weight": "v.blk.{bid}.{sid}.attn.value.down_conv.weight",
        "model.vision_tower.timm_model.blocks.{bid}.{sid}.attn.value.norm.weight":      "v.blk.{bid}.{sid}.attn.value.norm.weight",
        "model.vision_tower.timm_model.blocks.{bid}.{sid}.norm.weight":                 "v.blk.{bid}.{sid}.norm.weight",
    }

    def __init__(self, *args, **kwargs):
        # Parent init will call find_hparam which now returns 0 for empty keys
        super().__init__(*args, **kwargs)
        assert self.hparams_vision is not None
        self.hparams_vision["n_layers"] = 128 # fake value for audio encoder, vision encoder doesn't use it
        self.hparams_vision["intermediate_size"] = self.hparams_vision.get("intermediate_size", 2048) * 4
        self.hparams_vision["num_attention_heads"] = self.hparams_vision.get("num_attention_heads", 8)

        # MobileNetV5 does not use image_mean/std
        self.preprocessor_config["image_mean"] = [0.0 ,0.0 , 0.0]
        self.preprocessor_config["image_std"] = [1.0 ,1.0 ,1.0]
        self.hparams_vision["image_size"] = self.preprocessor_config.get(
            "size", {"height": 768, "width": 768}
        )["height"]

        # Image sequence length (256 tokens = 16x16 for Gemma3n)
        image_seq_length = self.preprocessor_config.get("image_seq_length", 256)
        image_size = self.hparams_vision["image_size"]
        self.hparams_vision["patch_size"] = image_size // image_seq_length

        # remap audio hparams
        assert self.hparams_audio is not None
        self.hparams_audio["n_layers"] = self.hparams_audio["conf_num_hidden_layers"]
        self.hparams_audio["num_attention_heads"] = self.hparams_audio["conf_num_attention_heads"]
        self.hparams_audio["feat_in"] = self.hparams_audio["input_feat_size"]
        self.hparams_audio["intermediate_size"] = self.hparams_audio.get("intermediate_size", 6144)

    def set_gguf_parameters(self):
        super().set_gguf_parameters()

        # vision params
        self.gguf_writer.add_clip_vision_projector_type(gguf.VisionProjectorType.GEMMA3NV)
        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams.get("layer_norm_eps", 1e-6))

        # audio params
        assert self.hparams_audio is not None
        self.gguf_writer.add_clip_audio_projector_type(gguf.VisionProjectorType.GEMMA3NA)
        self.gguf_writer.add_audio_num_mel_bins(self.hparams_audio["feat_in"])
        self.gguf_writer.add_audio_attention_layernorm_eps(1e-5)

    def tensor_force_quant(self, name, new_name, bid, n_dims):
        # Force quantization settings for specific tensor types
        if "input_projection" in name or "input_proj" in name:
            return gguf.GGMLQuantizationType.F16
        if ".embeddings." in name or "stem" in name:
            return gguf.GGMLQuantizationType.F32
        return super().tensor_force_quant(name, new_name, bid, n_dims)

    def custom_map(self, name: str) -> str:
        """Parses names like model.vision_tower.timm_model.blocks.1.2.suffix and applies template mapping."""
        parts = name.split(".")
        # MobileNet blocks have at least 7 parts: model, vision_tower, timm_model, blocks, bid, sid, and suffix
        if len(parts) >= 7:
            bid, sid = parts[4], parts[5]
            suffix = ".".join(parts[6:])
            template = f"model.vision_tower.timm_model.blocks.{{bid}}.{{sid}}.{suffix}"
            if template in self.block_tensor_mapping:
                return self.block_tensor_mapping[template].format(bid=bid, sid=sid)

        raise ValueError(f"Unknown name: {name}")

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        if (ConformerAudioModel.is_audio_tensor(name)):
            name = name.replace("model.audio_tower.conformer.", "conformer.layers.")
            yield from super().modify_tensors(data_torch, name, bid)

        # Gemma3n uses
        # - model.embed_vision.* for projection layers
        # - model.vision_tower.* for vision encoder
        # Skip non-vision tensors
        if not (name.startswith("model.embed_vision.") or name.startswith("model.vision_tower.")):
            return

        if name.startswith("model.vision_tower.timm_model.blocks."):
            # Double-indexed block tensors through custom logic
            yield (self.custom_map(name), data_torch)
            return
        else:
            # Route non-repeating (conv_stem, msfa, embedding, etc.) and un-catched through tensor_mapping.py
            new_name = self.map_tensor_name(name)

        if new_name.endswith("conv_stem.conv.bias") or new_name.endswith("layer_scale.gamma"):
            data_torch = data_torch.unsqueeze(0).unsqueeze(-1).unsqueeze(-1) # [1, C, 1, 1]

        yield from ModelBase.modify_tensors(self, data_torch, new_name, bid)


@ModelBase.register("Gemma3nForCausalLM", "Gemma3nForConditionalGeneration")
class Gemma3NModel(Gemma3Model):
    model_arch = gguf.MODEL_ARCH.GEMMA3N

    _altup_proj: list[Tensor] = []
    _altup_unembd: list[Tensor] = []

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        assert self.hparams["altup_num_inputs"] == 4, "Current conversion only supports 4 altup inputs"
        self._altup_proj = [
            torch.Tensor(), # to be replaced
            torch.Tensor(), # to be replaced
            torch.Tensor(), # to be replaced
        ]
        self._altup_unembd = [
            torch.Tensor(), # to be replaced
            torch.Tensor(), # to be replaced
            torch.Tensor(), # to be replaced
        ]

    def norm_shift(self, name: str) -> float:
        del name
        return 0.0 # same value with Gemma3p5RMSNorm scale_shift on python code

    def set_vocab(self):
        # For Gemma3n multimodal models, we need the FULL vocab_size (262400)
        # which includes special tokens from 262144-262399 for vision/audio.
        # The vocab_size_per_layer_input (262144) is only the embedding size per layer.
        # Temporarily override the hparams lookup order to prioritize vocab_size.

        # Store original vocab_size_per_layer_input if it exists
        vocab_size_per_layer_input = self.hparams.get("vocab_size_per_layer_input")

        # Temporarily remove vocab_size_per_layer_input to force using vocab_size
        if vocab_size_per_layer_input is not None:
            del self.hparams["vocab_size_per_layer_input"]

        # Call parent set_vocab which will now use vocab_size (262400)
        super().set_vocab()

        # Restore vocab_size_per_layer_input for later use
        if vocab_size_per_layer_input is not None:
            self.hparams["vocab_size_per_layer_input"] = vocab_size_per_layer_input

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        self.gguf_writer.add_altup_active_idx(self.hparams["altup_active_idx"])
        self.gguf_writer.add_altup_num_inputs(self.hparams["altup_num_inputs"])
        self.gguf_writer.add_embedding_length_per_layer_input(self.hparams["hidden_size_per_layer_input"])
        self.gguf_writer.add_shared_kv_layers(self.hparams["num_kv_shared_layers"])

        activation_sparsity_scale = []
        for s in self.hparams["activation_sparsity_pattern"]:
            normal_dist = torch.distributions.normal.Normal(0, 1)
            std_multiplier = normal_dist.icdf(torch.tensor(s, dtype=torch.float32))
            activation_sparsity_scale.append(std_multiplier.item())
        self.gguf_writer.add_activation_sparsity_scale(activation_sparsity_scale)

        sliding_window_pattern = []
        for t in self.hparams["layer_types"]:
            sliding_window_pattern.append(t == "sliding_attention")
        self.gguf_writer.add_sliding_window_pattern(sliding_window_pattern)

    def _stack_matrices(self, matrices: list[Tensor]) -> Tensor | None:
        has_all = all(m.numel() > 0 for m in matrices)
        if not has_all:
            return None
        else:
            return torch.stack(matrices, dim=0)

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        if name.endswith("_scale"):
            name = name + ".weight"

        return super().filter_tensors((name, gen))

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        # TODO: implement self.prediction_coefs.weight.clamp_(...)

        # Pad token embeddings for vision/audio special tokens (262144-262399)
        if "embed_tokens.weight" in name or "embed_tokens_per_layer" in name:
            # Move to CPU to avoid meta device issues during padding
            data_torch = data_torch.to(device="cpu")

            vocab_size = self.hparams.get("vocab_size", 262400)
            current_size = data_torch.shape[0]  # First dimension is vocab_size

            if current_size < vocab_size:
                # Pad with zeros for vision/audio tokens (they get embeddings from vision tower)
                padding_size = vocab_size - current_size
                tensor_type = "per-layer embeddings" if "per_layer" in name else "token embeddings"
                logger.info(f"Padding {tensor_type} shape {list(data_torch.shape)} from {current_size} to {vocab_size} (adding {padding_size} vision/audio token slots)")

                # Create padding with zeros (vision tokens won't use these embeddings)
                padding = torch.zeros((padding_size, data_torch.shape[1]), dtype=data_torch.dtype, device=data_torch.device)
                data_torch = torch.cat([data_torch, padding], dim=0)

            # Continue with normal processing
            yield from ModelBase.modify_tensors(self, data_torch, name, bid)
            return

        if "altup_unembed_projections" in name:
            data_torch = data_torch.to(device="cpu")
            # altup_unembed matrices are [hidden_size, hidden_size], NOT vocab-based
            # They should NOT be padded
            if ".0." in name:
                self._altup_unembd[0] = data_torch
            elif ".1." in name:
                self._altup_unembd[1] = data_torch
            elif ".2." in name:
                self._altup_unembd[2] = data_torch
            else:
                raise ValueError(f"Unknown name: {name}")
            out = self._stack_matrices(self._altup_unembd)
            if out is not None:
                yield from ModelBase.modify_tensors(self, out, "model.altup_unembed_projections.weight", bid)
                return
            else:
                return

        if "altup_projections" in name:
            data_torch = data_torch.to(device="cpu")
            if ".0." in name:
                self._altup_proj[0] = data_torch
            elif ".1." in name:
                self._altup_proj[1] = data_torch
            elif ".2." in name:
                self._altup_proj[2] = data_torch
            else:
                raise ValueError(f"Unknown name: {name}")
            out = self._stack_matrices(self._altup_proj)
            if out is not None:
                yield from ModelBase.modify_tensors(self, out, "model.altup_projections.weight", bid)
                return
            else:
                return

        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("Gemma4ForConditionalGeneration")
class Gemma4Model(Gemma3Model):
    model_arch = gguf.MODEL_ARCH.GEMMA4

    def norm_shift(self, name: str) -> float:
        del name # unused
        return 0.0

    def set_vocab(self):
        vocab = gguf.LlamaHfVocab(self.dir_model)
        tokens = []
        scores = []
        toktypes = []
        visible_tokens = {"<|channel>", "<channel|>", "<|tool_call>", "<tool_call|>", "<|tool_response>", "<tool_response|>", "<|\"|>"}

        for text, score, toktype in vocab.all_tokens():
            tokens.append(text)
            scores.append(score)
            text_str = text.decode()
            if text_str in visible_tokens:
                # always render these tokens, so that the chat parser can read them
                toktypes.append(gguf.TokenType.USER_DEFINED)
                logger.info(f"Token '{text_str}' is set to USER_DEFINED")
            else:
                toktypes.append(toktype)

        assert len(tokens) == vocab.vocab_size

        self.gguf_writer.add_tokenizer_model("gemma4")
        self.gguf_writer.add_token_list(tokens)
        self.gguf_writer.add_token_scores(scores)
        self.gguf_writer.add_token_types(toktypes)

        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
        special_vocab.add_to_gguf(self.gguf_writer)
        self.gguf_writer.add_add_space_prefix(False)
        self.gguf_writer.add_add_bos_token(True)

    def set_gguf_parameters(self):
        super().set_gguf_parameters()

        num_kv_shared_layers = self.hparams["num_kv_shared_layers"]
        self.gguf_writer.add_shared_kv_layers(num_kv_shared_layers)

        # per-layer embedding is optional
        n_pl_embd = self.hparams.get("hidden_size_per_layer_input") or 0
        self.gguf_writer.add_embedding_length_per_layer_input(n_pl_embd)

        swa_layers = [t == "sliding_attention" for t in self.hparams["layer_types"]]
        self.gguf_writer.add_sliding_window_pattern(swa_layers)

        head_dim_full = self.hparams["global_head_dim"]
        head_dim_swa = self.hparams["head_dim"]
        # correct the head dim for global/swa layers
        self.gguf_writer.add_key_length(head_dim_full)
        self.gguf_writer.add_value_length(head_dim_full)
        self.gguf_writer.add_key_length_swa(head_dim_swa)
        self.gguf_writer.add_value_length_swa(head_dim_swa)

        expert_intermediate_size = self.find_hparam(["expert_intermediate_size", "moe_intermediate_size"])
        if expert_intermediate_size is not None:
            self.gguf_writer.add_expert_feed_forward_length(expert_intermediate_size)

        # if use_double_wide_mlp is set, we need to adjust the value for kv shared layers
        use_double_wide_mlp = self.hparams.get("use_double_wide_mlp", False)
        first_kv_shared_layer_idx = self.block_count - num_kv_shared_layers
        if use_double_wide_mlp:
            n_ff = self.hparams["intermediate_size"]
            n_ff_arr = [n_ff if il < first_kv_shared_layer_idx else n_ff * 2 for il in range(self.block_count)]
            self.gguf_writer.add_feed_forward_length(n_ff_arr)

        # handle num_global_key_value_heads
        num_key_value_heads_full = self.hparams.get("num_global_key_value_heads")
        num_key_value_heads_swa = self.hparams.get("num_key_value_heads")
        if num_key_value_heads_full is not None and num_key_value_heads_swa is not None:
            value_arr = [num_key_value_heads_swa if is_swa else num_key_value_heads_full for is_swa in swa_layers]
            self.gguf_writer.add_head_count_kv(value_arr)

        # handle n_rot differently for global vs swa layers
        partial_rotary_factor_swa = self.hparams.get("partial_rotary_factor", 1.0)
        n_rot_full = int(head_dim_full) # "proportional" is used, see generate_extra_tensors
        n_rot_swa = int(head_dim_swa * partial_rotary_factor_swa)
        self.gguf_writer.add_rope_dimension_count(n_rot_full)
        self.gguf_writer.add_rope_dimension_count_swa(n_rot_swa)

    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
        # full layer uses "proportional" rope with partial_rotary_factor=0.25
        # the expected ordering is cc000000ss000000 (c = cos, s = sin, 0 = unrotated),
        # but ggml neox only supports ccss000000000000, and we cannot rearrange the head because that will break use_alternative_attention
        # solution is to set specific freq_factors for the unrotated dims

        # IMPORTANT: this ROPE_FREQS tensor is ONLY used by the full_attention layers
        rope_params_full = self.hparams["rope_parameters"]["full_attention"]
        assert rope_params_full["rope_type"] == "proportional"
        head_dim_full = (self.hparams["global_head_dim"])
        partial_rotary_factor_full = rope_params_full["partial_rotary_factor"]
        n_rot_full = int(head_dim_full * partial_rotary_factor_full / 2)
        n_unrot_full = int(head_dim_full / 2) - n_rot_full
        values = [1.0] * n_rot_full + [1e30] * n_unrot_full
        rope_freqs_full = torch.tensor(values, dtype=torch.float32)
        yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), rope_freqs_full)

    def _generate_nvfp4_tensors(self):
        # Gemma-4 stores a per-layer router.per_expert_scale ([n_expert]) that scales
        # each expert's contribution. It's mathematically equivalent to a per-expert
        # scalar on the down_proj output, which is exactly where ffn_down_exps_s is
        # applied at inference. Fold it into each expert's NVFP4 weight_scale_2 so the
        # existing NVFP4 path produces the right scales.
        n_experts = self.find_hparam(["num_local_experts", "num_experts"], optional=True) or 0
        for name in [n for n in self.model_tensors if n.endswith(".router.per_expert_scale")]:
            bid_match = re.search(r"\.layers\.(\d+)\.", name)
            if bid_match is None:
                continue
            bid = bid_match.group(1)
            prefix = name[: name.index(f".layers.{bid}.") + len(f".layers.{bid}.")]
            w2_targets = [f"{prefix}experts.{e}.down_proj.weight_scale_2" for e in range(n_experts)]
            present = [w2 in self.model_tensors for w2 in w2_targets]
            if not any(present):
                continue
            assert all(present), f"layer {bid}: partial NVFP4 quantization across experts"
            r = self.model_tensors.pop(name)
            for e, w2 in enumerate(w2_targets):
                s = self.model_tensors[w2]
                self.model_tensors[w2] = lambda s=s, r=r, i=e: s() * r()[i]
        super()._generate_nvfp4_tensors()

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        if name.endswith("per_dim_scale") or name.endswith("layer_scalar"):
            name = name + ".weight"
        if ".experts." in name and not name.endswith((".weight", ".weight_scale", ".weight_scale_2", ".input_scale")):
            name += ".weight"

        return super().filter_tensors((name, gen))

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        if name.endswith("router.scale"):
            name = self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE_INP, bid, ".scale")
            yield (name, data_torch)
            return
        if ".per_expert_scale" in name:
            # convert per-expert scale to FFN down scale
            name = self.format_tensor_name(gguf.MODEL_TENSOR.FFN_DOWN_EXP, bid, ".scale")
            yield (name, data_torch)
            return

        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("Gemma4ForConditionalGeneration")
class Gemma4VisionAudioModel(MmprojModel):
    has_audio_encoder = True
    has_vision_encoder = True

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        assert self.hparams_vision is not None
        self.hparams_vision["image_size"] = 224 # unused, but set to avoid error

        # remap audio hparams
        if self.hparams_audio:
            self.hparams_audio["feat_in"] = self.hparams_audio.get("input_feat_size", 128)
            self.hparams_audio["intermediate_size"] = self.hparams_audio["hidden_size"] * 4
        else:
            self.has_audio_encoder = False

    def set_gguf_parameters(self):
        super().set_gguf_parameters()

        # vision params
        self.gguf_writer.add_clip_vision_projector_type(gguf.VisionProjectorType.GEMMA4V)
        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams.get("layer_norm_eps", 1e-6))

        # audio params
        if self.hparams_audio:
            self.gguf_writer.add_clip_audio_projector_type(gguf.VisionProjectorType.GEMMA4A)
            self.gguf_writer.add_audio_num_mel_bins(self.hparams_audio["feat_in"])
            self.gguf_writer.add_audio_attention_layernorm_eps(1e-5)

    def is_audio_tensor(self, name: str) -> bool:
        return "audio_tower" in name or "embed_audio" in name

    def tensor_force_quant(self, name, new_name, bid, n_dims):
        if self.is_audio_tensor(name):
            if ".conv" in name or "_conv" in name and ".weight" in name:
                return gguf.GGMLQuantizationType.F32
        if "position_embedding_table" in name:
            return gguf.GGMLQuantizationType.F32
        return super().tensor_force_quant(name, new_name, bid, n_dims)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        del bid # unused

        if len(data_torch.shape) == 0:
            # convert scalar tensors (input/output_mix/max) to 1D tensors
            data_torch = data_torch.unsqueeze(0)

        if self.is_audio_tensor(name):
            assert self.hparams_audio is not None
            name = name.replace("model.audio_tower.", "conformer.")
            name = name.replace(".linear.", ".")
            if name.endswith("per_dim_key_scale") or name.endswith("per_dim_scale"):
                name = name + ".weight"
                data_torch = torch.nn.functional.softplus(data_torch)
            if "lconv1d.depthwise_conv1d" in name and name.endswith(".weight"):
                assert data_torch.shape[1] == 1
                data_torch = data_torch.reshape(data_torch.shape[0], data_torch.shape[2])
            mapped_name = self.map_tensor_name(name, (".weight", ".bias", ".input_max", ".input_min", ".output_max", ".output_min"))
            yield (mapped_name, data_torch)

        else:
            name = name.replace("model.vision_tower.encoder.", "vision_model.model.")
            name = name.replace(".linear.weight", ".weight")
            if name.endswith("layer_scalar") or name.endswith("position_embedding_table"):
                name = name + ".weight"
            if name.endswith("patch_embedder.input_proj.weight"):
                n_embd, ksize_sq_c = data_torch.shape
                patch_size = int((ksize_sq_c // 3) ** 0.5)
                data_torch = data_torch.reshape(n_embd, patch_size, patch_size, 3)
                data_torch = data_torch.permute(0, 3, 1, 2).contiguous()
            mapped_name = self.map_tensor_name(name, (".weight", ".bias", ".input_max", ".input_min", ".output_max", ".output_min"))
            yield (mapped_name, data_torch)


@ModelBase.register("Starcoder2ForCausalLM")
class StarCoder2Model(TextModel):
    model_arch = gguf.MODEL_ARCH.STARCODER2


@ModelBase.register("Rwkv6ForCausalLM")
class Rwkv6Model(TextModel):
    model_arch = gguf.MODEL_ARCH.RWKV6

    def set_vocab(self):
        self._set_vocab_rwkv_world()

    def set_gguf_parameters(self):
        head_size = self.hparams["head_size"]
        hidden_size = self.hparams["hidden_size"]
        layer_norm_eps = self.hparams["layer_norm_epsilon"]
        rescale_every_n_layers = self.hparams["rescale_every"]
        intermediate_size = self.hparams["intermediate_size"] if self.hparams["intermediate_size"] is not None else int((hidden_size * 3.5) // 32 * 32)
        time_mix_extra_dim = 64 if hidden_size == 4096 else 32
        time_decay_extra_dim = 128 if hidden_size == 4096 else 64

        # RWKV isn't context limited
        self.gguf_writer.add_context_length(1048576)
        self.gguf_writer.add_embedding_length(hidden_size)
        self.gguf_writer.add_block_count(self.block_count)
        self.gguf_writer.add_layer_norm_eps(layer_norm_eps)
        self.gguf_writer.add_rescale_every_n_layers(rescale_every_n_layers)
        self.gguf_writer.add_wkv_head_size(head_size)
        self.gguf_writer.add_time_mix_extra_dim(time_mix_extra_dim)
        self.gguf_writer.add_time_decay_extra_dim(time_decay_extra_dim)
        self.gguf_writer.add_feed_forward_length(intermediate_size)
        self.gguf_writer.add_file_type(self.ftype)

        # required by llama.cpp, unused
        self.gguf_writer.add_head_count(0)

    lerp_weights: dict[int, dict[str, Tensor]] = {}

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        new_name = self.map_tensor_name(name)

        if not (new_name.endswith(".weight") or new_name.endswith(".bias")):
            new_name += ".weight"

        if new_name.endswith("time_mix_w1.weight") or new_name.endswith("time_mix_decay_w1.weight") or new_name.endswith("time_mix_decay_w2.weight"):
            data_torch = data_torch.transpose(0, 1)

        if new_name.endswith("time_mix_w2.weight"):
            data_torch = data_torch.permute(0, 2, 1)

        if new_name.endswith("time_mix_decay.weight") or "lerp" in new_name:
            data_torch = data_torch.squeeze()

        try:
            rescale_every_n_layers = self.hparams["rescale_every"]
            if rescale_every_n_layers > 0:
                if new_name.endswith("time_mix_output.weight") or new_name.endswith("channel_mix_value.weight"):
                    data_torch = data_torch.div_(2 ** int(bid // rescale_every_n_layers))
        except KeyError:
            pass

        # concat time_mix_lerp weights to reduce some cpu overhead
        # also reduces the number of tensors in the model
        if bid is not None and "time_mix_lerp" in new_name and "time_mix_lerp_x" not in new_name:
            try:
                self.lerp_weights[bid][new_name] = data_torch
            except KeyError:
                self.lerp_weights[bid] = {new_name: data_torch}
            if all(f"blk.{bid}.time_mix_lerp_{i}.weight" in self.lerp_weights[bid].keys() for i in ["w", "k", "v", "r", "g"]):
                new_name = f"blk.{bid}.time_mix_lerp_fused.weight"
                data = torch.stack([self.lerp_weights[bid][f"blk.{bid}.time_mix_lerp_{i}.weight"].unsqueeze(0) for i in ["w", "k", "v", "r", "g"]], dim=0).unsqueeze(1)
                yield (new_name, data)
            return

        yield (new_name, data_torch)


@ModelBase.register("RWKV6Qwen2ForCausalLM")
class RWKV6Qwen2Model(Rwkv6Model):
    model_arch = gguf.MODEL_ARCH.RWKV6QWEN2

    def set_vocab(self):
        try:
            self._set_vocab_sentencepiece()
        except FileNotFoundError:
            self._set_vocab_gpt2()

    def set_gguf_parameters(self):
        num_attention_heads = self.hparams["num_attention_heads"]
        num_key_value_heads = self.hparams["num_key_value_heads"]
        hidden_size = self.hparams["hidden_size"]
        head_size = hidden_size // num_attention_heads
        rms_norm_eps = self.hparams["rms_norm_eps"]
        intermediate_size = self.hparams["intermediate_size"]
        time_mix_extra_dim = self.hparams.get("lora_rank_tokenshift", 64 if hidden_size >= 4096 else 32)
        time_decay_extra_dim = self.hparams.get("lora_rank_decay", 128 if hidden_size >= 4096 else 64)

        # RWKV isn't context limited
        self.gguf_writer.add_context_length(1048576)
        self.gguf_writer.add_embedding_length(hidden_size)
        self.gguf_writer.add_block_count(self.block_count)
        self.gguf_writer.add_wkv_head_size(head_size)
        self.gguf_writer.add_time_mix_extra_dim(time_mix_extra_dim)
        self.gguf_writer.add_time_decay_extra_dim(time_decay_extra_dim)
        self.gguf_writer.add_feed_forward_length(intermediate_size)
        self.gguf_writer.add_file_type(self.ftype)

        # special parameters for time_mixing in RWKV6QWEN2
        self.gguf_writer.add_layer_norm_rms_eps(rms_norm_eps)
        self.gguf_writer.add_token_shift_count(1)
        # RWKV6QWEN2 use grouped key/value like GQA
        self.gguf_writer.add_head_count_kv(num_key_value_heads)

        # required by llama.cpp, unused
        self.gguf_writer.add_head_count(0)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        for new_name, data in super().modify_tensors(data_torch, name, bid):
            if "time_mix_w1" in new_name or "time_mix_w2" in new_name:
                data = data.view(5, -1, data.shape[-1])
                # rwkv6qwen2 has a different order of rkvwg instead of the original wkvrg
                # permute them here to avoid code changes
                data = torch.stack([data[3], data[1], data[2], data[0], data[4]], dim=0).view(-1, data.shape[-1])
                if "w2" in new_name:
                    data = data.view(5, -1, data.shape[-1])
                yield (new_name, data)
                continue
            yield (new_name, data)


@ModelBase.register("Rwkv7ForCausalLM", "RWKV7ForCausalLM")
class Rwkv7Model(TextModel):
    model_arch = gguf.MODEL_ARCH.RWKV7

    def set_vocab(self):
        self._set_vocab_rwkv_world()

    def calc_lora_rank(self, hidden_size, exponent, multiplier):
        return max(1, round(hidden_size ** exponent * multiplier / 32)) * 32

    def set_gguf_parameters(self):
        try:
            head_size = self.hparams["head_size"]
            layer_norm_eps = self.hparams["layer_norm_epsilon"]
        except KeyError:
            head_size = self.hparams["head_dim"]
            layer_norm_eps = self.hparams["norm_eps"]
        hidden_size = self.hparams["hidden_size"]
        intermediate_size = self.hparams["intermediate_size"] if self.hparams["intermediate_size"] is not None else (hidden_size * 4)

        # ICLR: In-Context-Learning-Rate
        try:
            lora_rank_decay = self.hparams["lora_rank_decay"] if self.hparams["lora_rank_decay"] is not None else self.calc_lora_rank(hidden_size, 0.5, 1.8)
            lora_rank_iclr = self.hparams["lora_rank_iclr"] if self.hparams["lora_rank_iclr"] is not None else self.calc_lora_rank(hidden_size, 0.5, 1.8)
            lora_rank_value_residual_mix = self.hparams["lora_rank_value_residual_mix"] if self.hparams["lora_rank_value_residual_mix"] is not None else self.calc_lora_rank(hidden_size, 0.5, 1.3)
            lora_rank_gate = self.hparams["lora_rank_gate"] if self.hparams["lora_rank_gate"] is not None else self.calc_lora_rank(hidden_size, 0.8, 0.6)
        except KeyError:
            lora_rank_decay = self.hparams["decay_low_rank_dim"] if self.hparams["decay_low_rank_dim"] is not None else self.calc_lora_rank(hidden_size, 0.5, 1.8)
            lora_rank_iclr = self.hparams["a_low_rank_dim"] if self.hparams["a_low_rank_dim"] is not None else self.calc_lora_rank(hidden_size, 0.5, 1.8)
            lora_rank_value_residual_mix = self.hparams["v_low_rank_dim"] if self.hparams["v_low_rank_dim"] is not None else self.calc_lora_rank(hidden_size, 0.5, 1.3)
            lora_rank_gate = self.hparams["gate_low_rank_dim"] if self.hparams["gate_low_rank_dim"] is not None else self.calc_lora_rank(hidden_size, 0.8, 0.6)

        # RWKV isn't context limited
        self.gguf_writer.add_context_length(1048576)
        self.gguf_writer.add_embedding_length(hidden_size)
        self.gguf_writer.add_block_count(self.block_count)
        self.gguf_writer.add_layer_norm_eps(layer_norm_eps)
        self.gguf_writer.add_wkv_head_size(head_size)
        self.gguf_writer.add_decay_lora_rank(lora_rank_decay)
        self.gguf_writer.add_iclr_lora_rank(lora_rank_iclr)
        self.gguf_writer.add_value_residual_mix_lora_rank(lora_rank_value_residual_mix)
        self.gguf_writer.add_gate_lora_rank(lora_rank_gate)
        self.gguf_writer.add_feed_forward_length(intermediate_size)
        self.gguf_writer.add_file_type(self.ftype)

        # required by llama.cpp, unused
        self.gguf_writer.add_head_count(0)

    lerp_weights: dict[int, dict[str, Tensor]] = {}
    lora_needs_transpose: bool = True

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        # unify tensor names here to make life easier
        name = name.replace("blocks", "layers").replace("ffn", "feed_forward")
        name = name.replace("self_attn", "attention").replace("attn", "attention")
        name = name.replace("time_mixer.", "")

        name = name.replace("feed_forward_norm", "ln2")
        name = name.replace("g_norm", "ln_x")

        return super().filter_tensors((name, gen))

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        # lora layer names in fla-hub's impl
        if "_lora.lora" in name:
            self.lora_needs_transpose = False
        name = name.replace("_lora.lora.0.weight", "1.weight")
        name = name.replace("_lora.lora.2.weight", "2.weight")
        name = name.replace("_lora.lora.2.bias", "0.weight")

        if "attention.v" in name and "value" not in self.map_tensor_name(name) and bid == 0:
            # some models have dummy v0/v1/v2 on first layer while others don't
            # ignore them all since they are not used
            return

        wkv_has_gate = self.hparams.get("wkv_has_gate", True)
        lerp_list = ["r", "w", "k", "v", "a", "g"] if wkv_has_gate else ["r", "w", "k", "v", "a"]

        if bid is not None and "attention.x_" in name:
            if "attention.x_x" in name:
                # already concatenated
                new_name = f"blk.{bid}.time_mix_lerp_fused.weight"
                data = data_torch.reshape(len(lerp_list), 1, 1, -1)
                yield (new_name, data)
            else:
                try:
                    self.lerp_weights[bid][name] = data_torch
                except KeyError:
                    self.lerp_weights[bid] = {name: data_torch}
                if all(f"model.layers.{bid}.attention.x_{i}" in self.lerp_weights[bid].keys() for i in lerp_list):
                    new_name = f"blk.{bid}.time_mix_lerp_fused.weight"
                    data = torch.stack([self.lerp_weights[bid][f"model.layers.{bid}.attention.x_{i}"] for i in lerp_list], dim=0)
                    yield (new_name, data)
            return
        else:
            data_torch = data_torch.squeeze()
            new_name = self.map_tensor_name(name)

            if not (new_name.endswith(".weight") or new_name.endswith(".bias")):
                new_name += ".weight"

            if self.lora_needs_transpose and any(
                new_name.endswith(t) for t in [
                    "time_mix_w1.weight", "time_mix_w2.weight",
                    "time_mix_a1.weight", "time_mix_a2.weight",
                    "time_mix_v1.weight", "time_mix_v2.weight",
                    "time_mix_g1.weight", "time_mix_g2.weight",
                ]
            ):
                data_torch = data_torch.transpose(0, 1)

            if 'r_k' in new_name:
                data_torch = data_torch.flatten()

            if bid == 0 and "time_mix_a" in new_name:
                # dummy v0/v1/v2 on first layer
                # easiest way to make llama happy
                yield (new_name.replace("time_mix_a", "time_mix_v"), data_torch)

            yield (new_name, data_torch)


@ModelBase.register("RwkvHybridForCausalLM")
class ARwkv7Model(Rwkv7Model):
    model_arch = gguf.MODEL_ARCH.ARWKV7

    def set_vocab(self):
        try:
            self._set_vocab_sentencepiece()
        except FileNotFoundError:
            self._set_vocab_gpt2()

    def set_gguf_parameters(self):
        hidden_size = self.hparams["hidden_size"]
        head_size = self.hparams["head_size"]
        rms_norm_eps = self.hparams["rms_norm_eps"]
        intermediate_size = self.hparams["intermediate_size"]
        wkv_has_gate = self.hparams["wkv_has_gate"]
        assert self.hparams["wkv_version"] == 7

        # ICLR: In-Context-Learning-Rate
        lora_rank_decay = 64
        lora_rank_iclr = 64
        lora_rank_value_residual_mix = 32
        lora_rank_gate = 128 if wkv_has_gate else 0

        # RWKV isn't context limited
        self.gguf_writer.add_context_length(1048576)
        self.gguf_writer.add_embedding_length(hidden_size)
        self.gguf_writer.add_block_count(self.block_count)
        self.gguf_writer.add_layer_norm_rms_eps(rms_norm_eps)
        self.gguf_writer.add_wkv_head_size(head_size)
        self.gguf_writer.add_decay_lora_rank(lora_rank_decay)
        self.gguf_writer.add_iclr_lora_rank(lora_rank_iclr)
        self.gguf_writer.add_value_residual_mix_lora_rank(lora_rank_value_residual_mix)
        self.gguf_writer.add_gate_lora_rank(lora_rank_gate)
        self.gguf_writer.add_feed_forward_length(intermediate_size)
        self.gguf_writer.add_file_type(self.ftype)
        self.gguf_writer.add_token_shift_count(1)

        # required by llama.cpp, unused
        self.gguf_writer.add_head_count(0)


@ModelBase.register("MaincoderForCausalLM")
class MaincoderModel(TextModel):
    model_arch = gguf.MODEL_ARCH.MAINCODER

    def set_gguf_parameters(self):
        super().set_gguf_parameters()

        if (head_dim := self.hparams.get("head_dim")) is not None:
            self.gguf_writer.add_rope_dimension_count(head_dim)


@ModelBase.register("MambaForCausalLM", "MambaLMHeadModel", "FalconMambaForCausalLM")
class MambaModel(TextModel):
    model_arch = gguf.MODEL_ARCH.MAMBA

    def __init__(self, dir_model: Path, *args, **kwargs):
        # Avoid using AutoConfig for hparams
        hparams = kwargs.pop("hparams", None)
        if hparams is None:
            with open(dir_model / "config.json", "r", encoding="utf-8") as f:
                hparams = json.load(f)
        super().__init__(dir_model, *args, hparams=hparams, **kwargs)

    def set_vocab(self):
        vocab_size = self.hparams["vocab_size"]
        # Round vocab size to next multiple of 8
        pad_vocab = self.hparams.get("pad_vocab_size_multiple", 8)
        # pad using ceiling division
        # ref: https://stackoverflow.com/a/17511341/22827863
        vocab_size = -(vocab_size // -pad_vocab) * pad_vocab
        self.hparams["vocab_size"] = vocab_size

        if (self.dir_model / "tokenizer.json").is_file():
            self._set_vocab_gpt2()
        elif (self.dir_model / "tokenizer.model").is_file():
            self._set_vocab_sentencepiece()
        else:
            # Use the GPT-NeoX tokenizer when no tokenizer files are present
            self._set_vocab_builtin("gpt-neox", vocab_size)

    def set_gguf_parameters(self):
        d_model = self.find_hparam(["hidden_size",       "d_model"])
        d_conv  = self.find_hparam(["conv_kernel",       "d_conv"],  optional=True) or 4
        d_inner = self.find_hparam(["intermediate_size", "d_inner"], optional=True) or 2 * d_model
        d_state = self.find_hparam(["state_size",        "d_state"], optional=True) or 16
        # ceiling division
        # ref: https://stackoverflow.com/a/17511341/22827863
        # ref: https://github.com/state-spaces/mamba/blob/ce59daea3a090d011d6476c6e5b97f6d58ddad8b/mamba_ssm/modules/mamba_simple.py#L58
        dt_rank      = self.find_hparam(["time_step_rank",     "dt_rank"],      optional=True) or -(d_model // -16)
        rms_norm_eps = self.find_hparam(["layer_norm_epsilon", "rms_norm_eps"], optional=True) or 1e-5
        use_dt_b_c_norm = False
        # For falconmamba we do apply RMS norm on B / DT and C layers
        if self.find_hparam(["model_type"], optional=True) in ("falcon_mamba",):
            use_dt_b_c_norm = True
        # Fail early for models which don't have a block expansion factor of 2
        assert d_inner == 2 * d_model

        self.gguf_writer.add_context_length(2**20) # arbitrary value; for those who use the default
        self.gguf_writer.add_embedding_length(d_model)
        self.gguf_writer.add_feed_forward_length(0) # unused, but seemingly required when loading
        self.gguf_writer.add_head_count(0) # unused, but seemingly required when loading
        self.gguf_writer.add_block_count(self.block_count)
        self.gguf_writer.add_ssm_conv_kernel(d_conv)
        self.gguf_writer.add_ssm_inner_size(d_inner)
        self.gguf_writer.add_ssm_state_size(d_state)
        self.gguf_writer.add_ssm_time_step_rank(dt_rank)
        self.gguf_writer.add_layer_norm_rms_eps(rms_norm_eps)
        self.gguf_writer.add_ssm_dt_b_c_rms(use_dt_b_c_norm) # For classic Mamba we don't apply rms norm on B / DT layers
        self.gguf_writer.add_file_type(self.ftype)

    _tok_embd = None

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        output_name = self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT)
        tok_embd_name = self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD)

        new_name = self.map_tensor_name(name)

        if name.endswith(".A_log"):
            logger.debug("A_log --> A ==> " + new_name)
            data_torch = -torch.exp(data_torch)

        # [4 1 8192 1] -> [4 8192 1 1]
        if self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.SSM_CONV1D, bid):
            data_torch = data_torch.squeeze()

        # assuming token_embd.weight is seen before output.weight
        if self._tok_embd is not None and new_name == output_name:
            if torch.equal(self._tok_embd, data_torch):
                logger.debug(f"{output_name} is equivalent to {tok_embd_name}, omitting")
                return
        elif new_name == tok_embd_name:
            self._tok_embd = data_torch

        yield from super().modify_tensors(data_torch, new_name, bid)


@ModelBase.register("Mamba2ForCausalLM")
class Mamba2Model(TextModel):
    model_arch = gguf.MODEL_ARCH.MAMBA2

    def __init__(self, dir_model: Path, *args, **kwargs):
        # Avoid using AutoConfig for hparams
        # It wrongly assumes all Mamba2 models are Mamba-Codestral-7B-v0.1
        hparams = kwargs.pop("hparams", None)
        if hparams is None:
            with open(dir_model / "config.json", "r", encoding="utf-8") as f:
                hparams = json.load(f)
        if "llm_config" in hparams:
            hparams["text_config"] = hparams["llm_config"]
        super().__init__(dir_model, *args, hparams=hparams, **kwargs)
        self.d_model = self.find_hparam(["hidden_size", "d_model", "dim"])
        self.d_inner = self.find_hparam(["mamba_d_ssm", "intermediate_size", "d_inner"], optional=True) or 2 * self.d_model
        self.n_group = self.find_hparam(["n_groups"], optional=True) or 1

    def set_vocab(self):
        vocab_size = self.hparams["vocab_size"]
        # Round vocab size to next multiple of 16
        pad_vocab = self.hparams.get("pad_vocab_size_multiple", 16)
        # pad using ceiling division
        # ref: https://stackoverflow.com/a/17511341/22827863
        vocab_size = -(vocab_size // -pad_vocab) * pad_vocab
        self.hparams["vocab_size"] = vocab_size

        if (self.dir_model / "tokenizer.model").is_file():
            self._set_vocab_sentencepiece()
        elif (self.dir_model / "tokenizer.model.v3").is_file():
            # mamba-codestral
            raise NotImplementedError(f"Please rename {self.dir_model / 'tokenizer.model.v3'} to {self.dir_model / 'tokenizer.model'}")
        elif (self.dir_model / "tokenizer.json").is_file():
            self._set_vocab_gpt2()
        else:
            # Use the GPT-NeoX tokenizer when no tokenizer files are present
            self._set_vocab_builtin("gpt-neox", vocab_size)

    def set_gguf_parameters(self):
        d_conv  = self.find_hparam(["conv_kernel", "d_conv"],     optional=True) or 4
        d_state = self.find_hparam(["state_size",  "d_state"],    optional=True) or 128
        head_dim = self.find_hparam(["mamba_d_head", "head_dim"], optional=True) or 64

        rms_norm_eps = self.find_hparam(["layer_norm_epsilon", "rms_norm_eps"], optional=True) or 1e-5

        # Fail early for models which don't have a block expansion factor of 2
        # TODO: does this really matter?
        # skip the assertion for FalconH1 Model
        if self.model_arch != gguf.MODEL_ARCH.FALCON_H1:
            assert self.d_inner == 2 * self.d_model
            assert self.d_inner % head_dim == 0

        self.gguf_writer.add_context_length(2**20)  # arbitrary value; for those who use the default
        self.gguf_writer.add_embedding_length(self.d_model)
        self.gguf_writer.add_feed_forward_length(0)  # unused, but seemingly required when loading
        self.gguf_writer.add_head_count(0)  # unused, but seemingly required when loading
        self.gguf_writer.add_block_count(self.block_count)
        self.gguf_writer.add_ssm_conv_kernel(d_conv)
        self.gguf_writer.add_ssm_inner_size(self.d_inner)
        self.gguf_writer.add_ssm_state_size(d_state)
        self.gguf_writer.add_ssm_time_step_rank(self.d_inner // head_dim)
        self.gguf_writer.add_ssm_group_count(self.n_group)
        self.gguf_writer.add_layer_norm_rms_eps(rms_norm_eps)
        self.gguf_writer.add_file_type(self.ftype)

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        if name.startswith(("model.backbone", "model.lm_head")):
            # map Mamba-Codestral-7B-v0.1 tensor names to the names used by Mamba-2
            name = name.removeprefix("model.")

        if name.endswith(".dt_bias"):
            name = name.rpartition(".dt_bias")[0] + ".dt_proj.bias"

        return super().filter_tensors((name, gen))

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        new_name = self.map_tensor_name(name)

        if self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.SSM_CONV1D, bid):
            data_torch = data_torch.squeeze()
        elif any(self.match_model_tensor_name(new_name, t, bid, suffix="") for t in [
            gguf.MODEL_TENSOR.SSM_A,
            gguf.MODEL_TENSOR.SSM_D,
        ]):
            # unsqueeze A to use similar shape semantics as Mamba-1
            # (D is also unsqueezed, but for more straightforward broadcast internally)
            data_torch = data_torch.reshape((*data_torch.shape, 1))
        elif self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.SSM_NORM, bid):
            data_torch = data_torch.reshape((self.n_group, self.d_inner // self.n_group))

        if name.endswith(".A_log"):
            logger.debug("A_log --> A ==> " + new_name)
            data_torch = -torch.exp(data_torch)

        yield (new_name, data_torch)


@ModelBase.register("JambaForCausalLM")
class JambaModel(TextModel):
    model_arch = gguf.MODEL_ARCH.JAMBA

    def set_vocab(self):
        if (self.dir_model / "tokenizer.model").is_file():
            self._set_vocab_sentencepiece()
        else:
            self._set_vocab_llama_hf()
            self.gguf_writer.add_add_space_prefix(False)

    def set_gguf_parameters(self):
        d_model = self.find_hparam(["hidden_size", "mamba_d_model"])
        d_conv  = self.find_hparam(["mamba_d_conv"],  optional=True) or 4
        d_inner = self.hparams["mamba_expand"] * d_model
        d_state = self.find_hparam(["mamba_d_state"], optional=True) or 16
        # ceiling division
        # ref: https://stackoverflow.com/a/17511341/22827863
        # ref: https://github.com/state-spaces/mamba/blob/ce59daea3a090d011d6476c6e5b97f6d58ddad8b/mamba_ssm/modules/mamba_simple.py#L58
        dt_rank      = self.find_hparam(["mamba_dt_rank"], optional=True) or -(d_model // -16)
        rms_norm_eps = self.find_hparam(["layer_norm_epsilon", "rms_norm_eps"], optional=True) or 1e-6
        n_kv_head = self.hparams["num_key_value_heads"]
        attn_offset = self.hparams["attn_layer_offset"]
        attn_period = self.hparams["attn_layer_period"]
        n_kv_vec = [0 for _ in range(attn_offset)] + [
            n_kv_head if (i - attn_offset) % attn_period == 0 else 0 for i in range(attn_offset, self.block_count)
        ]

        self.gguf_writer.add_block_count(self.block_count)
        self.gguf_writer.add_context_length(self.find_hparam(["max_position_embeddings", "n_ctx"]))
        self.gguf_writer.add_embedding_length(d_model)
        self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
        self.gguf_writer.add_head_count(self.hparams["num_attention_heads"])
        self.gguf_writer.add_head_count_kv(n_kv_vec)
        self.gguf_writer.add_ssm_conv_kernel(d_conv)
        self.gguf_writer.add_ssm_inner_size(d_inner)
        self.gguf_writer.add_ssm_state_size(d_state)
        self.gguf_writer.add_ssm_time_step_rank(dt_rank)
        self.gguf_writer.add_layer_norm_rms_eps(rms_norm_eps)
        self.gguf_writer.add_expert_count(self.find_hparam(["num_local_experts", "num_experts"]))
        self.gguf_writer.add_expert_used_count(self.find_hparam(["num_experts_per_tok", "num_experts_per_token"]))
        self.gguf_writer.add_file_type(self.ftype)

    _experts: list[dict[str, Tensor]] | None = None

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:

        # Mini-Jamba
        name = name.replace(".moe.", ".feed_forward.")
        if bid is not None:
            moe_offset = self.hparams["expert_layer_offset"]
            moe_period = self.hparams["expert_layer_period"]

            if not (bid >= moe_offset and (bid - moe_offset) % moe_period == 0):
                name = name.replace(".experts.0.", ".")

        # process the experts separately
        if ".feed_forward.experts." in name:
            n_experts = self.find_hparam(["num_local_experts", "num_experts"])

            assert bid is not None

            if self._experts is None:
                self._experts = [{} for _ in range(self.block_count)]

            self._experts[bid][name] = data_torch

            if len(self._experts[bid]) >= n_experts * 3:

                # merge the experts into a single 3d tensor
                for wid in ["down_proj", "gate_proj", "up_proj"]:
                    datas: list[Tensor] = []

                    for xid in range(n_experts):
                        ename = f"model.layers.{bid}.feed_forward.experts.{xid}.{wid}.weight"
                        datas.append(self._experts[bid][ename])
                        del self._experts[bid][ename]

                    data_torch = torch.stack(datas, dim=0)

                    # using the same merged name as qwen2moe
                    merged_name = f"model.layers.{bid}.mlp.experts.{wid}.weight"

                    new_name = self.map_tensor_name(merged_name)

                    yield new_name, data_torch
            return

        new_name = self.map_tensor_name(name)

        if self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.SSM_CONV1D, bid):
            data_torch = data_torch.squeeze()

        if name.endswith(".A_log"):
            logger.debug("A_log --> A ==> " + new_name)
            data_torch = -torch.exp(data_torch)

        yield (new_name, data_torch)

    def prepare_tensors(self):
        super().prepare_tensors()

        if self._experts is not None:
            # flatten `list[dict[str, Tensor]]` into `list[str]`
            experts = [k for d in self._experts for k in d.keys()]
            if len(experts) > 0:
                raise ValueError(f"Unprocessed experts: {experts}")


@ModelBase.register("CohereForCausalLM")
class CommandR2Model(TextModel):
    model_arch = gguf.MODEL_ARCH.COMMAND_R

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

        # max_position_embeddings = 8192 in config.json but model was actually
        # trained on 128k context length
        # aya-23 models don't have model_max_length specified
        self.hparams["max_position_embeddings"] = self.find_hparam(["model_max_length", "max_position_embeddings"])

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        self.gguf_writer.add_logit_scale(self.hparams["logit_scale"])
        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)


@ModelBase.register("Cohere2ForCausalLM")
class Cohere2Model(TextModel):
    model_arch = gguf.MODEL_ARCH.COHERE2

    def set_gguf_parameters(self):
        super().set_gguf_parameters()

        self.gguf_writer.add_logit_scale(self.hparams["logit_scale"])
        self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
        self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])

        rotary_pct = self.hparams["rotary_pct"]
        hidden_size = self.hparams["hidden_size"]
        num_attention_heads = self.hparams["num_attention_heads"]
        self.gguf_writer.add_rope_dimension_count(int(rotary_pct * (hidden_size // num_attention_heads)))
        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        # Cohere2 runtime in llama.cpp expects no bias tensors;
        # the actual weight only contains 0-value tensors as bias, we can skip them
        if name.endswith(".bias"):
            if torch.any(data_torch != 0):
                raise ValueError(f"Bias tensor {name!r} is not zero.")
            logger.debug(f"Skipping bias tensor {name!r} for Cohere2 conversion.")
            return

        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("OlmoForCausalLM")
@ModelBase.register("OLMoForCausalLM")
class OlmoModel(TextModel):
    model_arch = gguf.MODEL_ARCH.OLMO

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        self.gguf_writer.add_layer_norm_eps(1e-5)
        clip_qkv = self.hparams.get("clip_qkv")
        if clip_qkv is not None:
            self.gguf_writer.add_clamp_kqv(clip_qkv)

    # Same as super class, but permuting q_proj, k_proj
    # Copied from: LlamaModel
    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        n_head = self.hparams["num_attention_heads"]
        n_kv_head = self.hparams.get("num_key_value_heads")

        if name.endswith("q_proj.weight"):
            data_torch = LlamaModel.permute(data_torch, n_head, n_head)
        if name.endswith("k_proj.weight"):
            data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)

        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("SeedOssForCausalLM")
class SeedOssModel(TextModel):
    model_arch = gguf.MODEL_ARCH.SEED_OSS


@ModelBase.register("Olmo2ForCausalLM")
@ModelBase.register("Olmo3ForCausalLM")
class Olmo2Model(TextModel):
    model_arch = gguf.MODEL_ARCH.OLMO2

    def set_gguf_parameters(self):
        super().set_gguf_parameters()

        if "sliding_window" in self.hparams:
            self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])

            sliding_window_pattern = []
            if "layer_types" in self.hparams:
                sliding_window_pattern = [t == "sliding_attention" for t in self.hparams["layer_types"]]
            else:
                # Olmo2 does not use sliding window attention.
                # Olmo3 defaults to using sliding window for all layers except every 4th.
                for i in range(self.hparams["num_hidden_layers"]):
                    sliding_window_pattern.append((i + 1) % 4 != 0)

            self.gguf_writer.add_sliding_window_pattern(sliding_window_pattern)


@ModelBase.register("OlmoeForCausalLM")
class OlmoeModel(TextModel):
    model_arch = gguf.MODEL_ARCH.OLMOE

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        self.gguf_writer.add_layer_norm_rms_eps(1e-5)

    _experts: list[dict[str, Tensor]] | None = None

    # Copied from: Qwen2MoeModel
    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        # process the experts separately
        if name.find("experts") != -1:
            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
            assert bid is not None

            if self._experts is None:
                self._experts = [{} for _ in range(self.block_count)]

            self._experts[bid][name] = data_torch

            if len(self._experts[bid]) >= n_experts * 3:
                # merge the experts into a single 3d tensor
                for w_name in ["down_proj", "gate_proj", "up_proj"]:
                    datas: list[Tensor] = []

                    for xid in range(n_experts):
                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
                        datas.append(self._experts[bid][ename])
                        del self._experts[bid][ename]

                    data_torch = torch.stack(datas, dim=0)

                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"

                    yield from super().modify_tensors(data_torch, merged_name, bid)
                return
            else:
                return

        yield from super().modify_tensors(data_torch, name, bid)

    # Copied from: Qwen2MoeModel
    def prepare_tensors(self):
        super().prepare_tensors()

        if self._experts is not None:
            # flatten `list[dict[str, Tensor]]` into `list[str]`
            experts = [k for d in self._experts for k in d.keys()]
            if len(experts) > 0:
                raise ValueError(f"Unprocessed experts: {experts}")


@ModelBase.register("JinaBertModel", "JinaBertForMaskedLM")
class JinaBertV2Model(BertModel):
    model_arch = gguf.MODEL_ARCH.JINA_BERT_V2

    def set_vocab(self):
        tokenizer_class = 'BertTokenizer'
        with open(self.dir_model / "tokenizer_config.json", "r", encoding="utf-8") as f:
            tokenizer_class = json.load(f)['tokenizer_class']

        if tokenizer_class == 'BertTokenizer':
            super().set_vocab()
        elif tokenizer_class == 'RobertaTokenizer':
            self._set_vocab_gpt2()
            self.gguf_writer.add_token_type_count(2)
        else:
            raise NotImplementedError(f'Tokenizer {tokenizer_class} is not supported for JinaBertModel')


@ModelBase.register("OpenELMForCausalLM")
class OpenELMModel(TextModel):
    model_arch = gguf.MODEL_ARCH.OPENELM

    @staticmethod
    def _make_divisible(v: float | int, divisor: int) -> int:
        # ref: https://huggingface.co/apple/OpenELM-270M-Instruct/blob/eb111ff2e6724348e5b905984063d4064d4bc579/configuration_openelm.py#L34-L38
        new_v = max(divisor, int(v + divisor / 2) // divisor * divisor)
        # Make sure that round down does not go down by more than 10%.
        if new_v < 0.9 * v:
            new_v += divisor
        return new_v

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

        ffn_multipliers: list[float] = self.hparams["ffn_multipliers"]
        ffn_dim_divisor: int = self.hparams["ffn_dim_divisor"]
        self._n_embd: int = self.hparams["model_dim"]
        self._num_kv_heads: list[int] = self.hparams["num_kv_heads"]
        self._num_query_heads: list[int] = self.hparams["num_query_heads"]
        self._ffn_dims: list[int] = [
            OpenELMModel._make_divisible(multiplier * self._n_embd, ffn_dim_divisor)
            for multiplier in ffn_multipliers
        ]
        assert isinstance(self._num_kv_heads, list) and isinstance(self._num_kv_heads[0], int)
        assert isinstance(self._num_query_heads, list) and isinstance(self._num_query_heads[0], int)

    # Uses the tokenizer from meta-llama/Llama-2-7b-hf
    def set_vocab(self):
        try:
            self._set_vocab_sentencepiece()
        except FileNotFoundError:
            self._set_vocab_builtin("llama-spm", self.hparams["vocab_size"])

    def set_gguf_parameters(self):
        n_embd = self._n_embd
        head_dim = self.hparams["head_dim"]
        rot_pct = 1.0
        assert self.block_count == len(self._num_kv_heads)
        assert self.block_count == len(self._num_query_heads)
        assert self.block_count == len(self._ffn_dims)

        self.gguf_writer.add_block_count(self.block_count)
        self.gguf_writer.add_context_length(self.hparams["max_context_length"])
        self.gguf_writer.add_embedding_length(n_embd)
        self.gguf_writer.add_feed_forward_length(self._ffn_dims)
        self.gguf_writer.add_head_count(self._num_query_heads)
        self.gguf_writer.add_head_count_kv(self._num_kv_heads)
        self.gguf_writer.add_rope_freq_base(self.hparams["rope_freq_constant"])
        # https://huggingface.co/apple/OpenELM-270M-Instruct/blob/c401df2/modeling_openelm.py#L30
        self.gguf_writer.add_layer_norm_rms_eps(1e-6)
        self.gguf_writer.add_rope_dimension_count(int(rot_pct * head_dim))
        self.gguf_writer.add_key_length(head_dim)
        self.gguf_writer.add_value_length(head_dim)
        self.gguf_writer.add_file_type(self.ftype)

    def find_hparam(self, keys: Iterable[str], optional: bool = False) -> Any:
        if "n_layers" in keys:
            return self.hparams["num_transformer_layers"]

        return super().find_hparam(keys, optional)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:

        # split ff
        if bid is not None and name == f"transformer.layers.{bid}.ffn.proj_1.weight":
            ff_dim = self._ffn_dims[bid]
            yield (self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE, bid), data_torch[:ff_dim])
            yield (self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP, bid), data_torch[ff_dim:])
            return

        yield (self.map_tensor_name(name), data_torch)


@ModelBase.register("ArcticForCausalLM")
class ArcticModel(TextModel):
    model_arch = gguf.MODEL_ARCH.ARCTIC

    def set_vocab(self):
        # The reason for using a custom implementation here is that the
        # snowflake-arctic-instruct model redefined tokens 31998 and 31999 from
        # tokenizer.model and used them as BOS and EOS instead of adding new tokens.
        from sentencepiece import SentencePieceProcessor

        tokenizer_path = self.dir_model / 'tokenizer.model'

        if not tokenizer_path.is_file():
            logger.error(f'Error: Missing {tokenizer_path}')
            sys.exit(1)

        # Read the whole vocabulary from the tokenizer.model file
        tokenizer = SentencePieceProcessor()
        tokenizer.LoadFromFile(str(tokenizer_path))

        vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())

        tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
        scores: list[float] = [-10000.0] * vocab_size
        toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size

        for token_id in range(tokenizer.vocab_size()):

            piece = tokenizer.IdToPiece(token_id)
            text = piece.encode("utf-8")
            score = tokenizer.GetScore(token_id)

            toktype = SentencePieceTokenTypes.NORMAL
            if tokenizer.IsUnknown(token_id):
                toktype = SentencePieceTokenTypes.UNKNOWN
            elif tokenizer.IsControl(token_id):
                toktype = SentencePieceTokenTypes.CONTROL
            elif tokenizer.IsUnused(token_id):
                toktype = SentencePieceTokenTypes.UNUSED
            elif tokenizer.IsByte(token_id):
                toktype = SentencePieceTokenTypes.BYTE

            tokens[token_id] = text
            scores[token_id] = score
            toktypes[token_id] = toktype

        # Use the added_tokens_decoder field from tokeniser_config.json as the source
        # of information about added/redefined tokens and modify them accordingly.
        tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
        if tokenizer_config_file.is_file():
            with open(tokenizer_config_file, "r", encoding="utf-8") as f:
                tokenizer_config_json = json.load(f)

                if "added_tokens_decoder" in tokenizer_config_json:
                    added_tokens_decoder = tokenizer_config_json["added_tokens_decoder"]
                    for token_id, token_json in added_tokens_decoder.items():
                        token_id = int(token_id)
                        if token_id >= vocab_size:
                            logger.debug(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}')
                            continue

                        token_content = token_json["content"]
                        token_type = SentencePieceTokenTypes.USER_DEFINED
                        token_score = -10000.0

                        # Map unk_token to UNKNOWN, other special tokens to CONTROL
                        # Set the score to 0.0 as in the original tokenizer.model
                        if ("special" in token_json) and token_json["special"]:
                            if token_content == tokenizer_config_json["unk_token"]:
                                token_type = SentencePieceTokenTypes.UNKNOWN
                            else:
                                token_type = SentencePieceTokenTypes.CONTROL
                            token_score = 0.0

                        logger.info(f"Setting added token {token_id} to '{token_content}' (type: {token_type}, score: {token_score:.2f})")
                        tokens[token_id] = token_content.encode("utf-8")
                        toktypes[token_id] = token_type
                        scores[token_id] = token_score

        self.gguf_writer.add_tokenizer_model("llama")
        self.gguf_writer.add_tokenizer_pre("default")
        self.gguf_writer.add_token_list(tokens)
        self.gguf_writer.add_token_scores(scores)
        self.gguf_writer.add_token_types(toktypes)

        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
        special_vocab.add_to_gguf(self.gguf_writer)

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        hparams = self.hparams
        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
        self.gguf_writer.add_rope_dimension_count(hparams["hidden_size"] // hparams["num_attention_heads"])

    _experts: list[dict[str, Tensor]] | None = None

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        n_head = self.hparams["num_attention_heads"]
        n_kv_head = self.hparams.get("num_key_value_heads")

        if name.endswith("q_proj.weight"):
            data_torch = LlamaModel.permute(data_torch, n_head, n_head)
        if name.endswith("k_proj.weight"):
            data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)

        # process the experts separately
        if name.find("block_sparse_moe.experts") != -1:
            n_experts = self.hparams["num_local_experts"]

            assert bid is not None

            if self._experts is None:
                self._experts = [{} for _ in range(self.block_count)]

            self._experts[bid][name] = data_torch

            if len(self._experts[bid]) >= n_experts * 3:
                # merge the experts into a single 3d tensor
                for wid in ["w1", "w2", "w3"]:
                    datas: list[Tensor] = []

                    for xid in range(n_experts):
                        ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{wid}.weight"
                        datas.append(self._experts[bid][ename])
                        del self._experts[bid][ename]

                    data_torch = torch.stack(datas, dim=0)

                    merged_name = f"layers.{bid}.feed_forward.experts.{wid}.weight"

                    yield from super().modify_tensors(data_torch, merged_name, bid)
                return
            else:
                return

        yield from super().modify_tensors(data_torch, name, bid)

    def prepare_tensors(self):
        super().prepare_tensors()

        if self._experts is not None:
            # flatten `list[dict[str, Tensor]]` into `list[str]`
            experts = [k for d in self._experts for k in d.keys()]
            if len(experts) > 0:
                raise ValueError(f"Unprocessed experts: {experts}")


@ModelBase.register("DeepseekForCausalLM")
class DeepseekModel(TextModel):
    model_arch = gguf.MODEL_ARCH.DEEPSEEK

    def set_vocab(self):
        try:
            self._set_vocab_sentencepiece()
        except FileNotFoundError:
            self._set_vocab_gpt2()

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        hparams = self.hparams
        if (rope_dim := hparams.get("head_dim")) is None:
            rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]

        self.gguf_writer.add_rope_dimension_count(rope_dim)
        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
        self.gguf_writer.add_leading_dense_block_count(hparams["first_k_dense_replace"])
        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
        self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"])
        self.gguf_writer.add_expert_weights_scale(1.0)
        self.gguf_writer.add_expert_count(hparams["n_routed_experts"])
        self.gguf_writer.add_expert_shared_count(hparams["n_shared_experts"])

    _experts: list[dict[str, Tensor]] | None = None

    @staticmethod
    def permute(weights: Tensor, n_head: int, n_head_kv: int | None):
        if n_head_kv is not None and n_head != n_head_kv:
            n_head = n_head_kv
        return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
                .swapaxes(1, 2)
                .reshape(weights.shape))

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        n_head = self.hparams["num_attention_heads"]
        n_kv_head = self.hparams.get("num_key_value_heads")

        if name.endswith(("q_proj.weight", "q_proj.bias")):
            data_torch = DeepseekModel.permute(data_torch, n_head, n_head)
        if name.endswith(("k_proj.weight", "k_proj.bias")):
            data_torch = DeepseekModel.permute(data_torch, n_head, n_kv_head)

        # process the experts separately
        if name.find("mlp.experts") != -1:
            n_experts = self.hparams["n_routed_experts"]
            assert bid is not None

            if self._experts is None:
                self._experts = [{} for _ in range(self.block_count)]

            self._experts[bid][name] = data_torch

            if len(self._experts[bid]) >= n_experts * 3:
                # merge the experts into a single 3d tensor
                for w_name in ["down_proj", "gate_proj", "up_proj"]:
                    datas: list[Tensor] = []

                    for xid in range(n_experts):
                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
                        datas.append(self._experts[bid][ename])
                        del self._experts[bid][ename]

                    data_torch = torch.stack(datas, dim=0)

                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"

                    yield from super().modify_tensors(data_torch, merged_name, bid)
                return
            else:
                return

        yield from super().modify_tensors(data_torch, name, bid)

    def prepare_tensors(self):
        super().prepare_tensors()

        if self._experts is not None:
            # flatten `list[dict[str, Tensor]]` into `list[str]`
            experts = [k for d in self._experts for k in d.keys()]
            if len(experts) > 0:
                raise ValueError(f"Unprocessed experts: {experts}")


@ModelBase.register(
    "DeepseekV2ForCausalLM",
    "DeepseekV3ForCausalLM",
    "KimiVLForConditionalGeneration",
    "KimiK25ForConditionalGeneration",
    "YoutuForCausalLM",
    "YoutuVLForConditionalGeneration",
)
class DeepseekV2Model(TextModel):
    model_arch = gguf.MODEL_ARCH.DEEPSEEK2

    # TODO @ngxson : remove this when we support MTP for deepseek models
    skip_mtp = True

    merge_expert = True

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        hparams: dict = ModelBase.load_hparams(self.dir_model, is_mistral_format=False)
        self.origin_hf_arch = hparams.get('architectures', [None])[0]

        # special handling for Deepseek OCR
        if self.origin_hf_arch == "DeepseekOCRForCausalLM":
            self.model_arch = gguf.MODEL_ARCH.DEEPSEEK2OCR
            self.gguf_writer.arch = gguf.MODEL_ARCH_NAMES[self.model_arch]
            self.gguf_writer.add_architecture()
            # default jinja template
            self.gguf_writer.add_chat_template("{% for m in messages %}{{m['content']}}{% endfor %}")

    def set_vocab(self):
        try:
            self._set_vocab_gpt2()
            return
        except Exception:
            pass

        from transformers import AutoTokenizer
        tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
        tokpre = self.get_vocab_base_pre(tokenizer)

        if tokpre == "kimi-k2":
            # Build merges list using the approach similar to HunYuanMoE
            merges = []
            vocab = {}
            mergeable_ranks = tokenizer.model._mergeable_ranks  # ty: ignore[unresolved-attribute]
            for token, rank in mergeable_ranks.items():
                vocab[QwenModel.token_bytes_to_string(token)] = rank
                if len(token) == 1:
                    continue
                merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank)
                if len(merged) == 2:
                    merges.append(' '.join(map(QwenModel.token_bytes_to_string, merged)))

            # Build token list
            vocab_size = self.hparams["vocab_size"]
            special_tokens = tokenizer.special_tokens  # ty: ignore[unresolved-attribute]
            reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **special_tokens}.items()}
            tokens: list[str] = []
            toktypes: list[int] = []

            for i in range(vocab_size):
                if i not in reverse_vocab:
                    tokens.append(f"[PAD{i}]")
                    toktypes.append(gguf.TokenType.UNUSED)
                else:
                    token = reverse_vocab[i]
                    tokens.append(token)
                    if i in special_tokens.values():
                        toktypes.append(gguf.TokenType.CONTROL)
                    else:
                        toktypes.append(gguf.TokenType.NORMAL)

            self.gguf_writer.add_tokenizer_model("gpt2")
            self.gguf_writer.add_tokenizer_pre(tokpre)
            self.gguf_writer.add_token_list(tokens)
            self.gguf_writer.add_token_types(toktypes)
            self.gguf_writer.add_token_merges(merges)

            special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False)
            special_vocab.add_to_gguf(self.gguf_writer)
        else:
            raise NotImplementedError(f"Deepseek pre-tokenizer {tokpre!r} is not supported yet!")

    def set_gguf_parameters(self):
        is_ocr = (self.model_arch == gguf.MODEL_ARCH.DEEPSEEK2OCR)

        if is_ocr:
            self.hparams['rope_theta'] = self.hparams.get('rope_theta', 10000.0)
        else:
            # note: deepseek2 using MLA converts into MQA (ie: GQA with 1 group)
            self.hparams["num_key_value_heads"] = 1

        self.hparams['rms_norm_eps'] = self.hparams.get('rms_norm_eps', 1e-6)

        super().set_gguf_parameters()
        hparams = self.hparams

        # first_k_dense_replace: number of leading layers using dense FFN instead of MoE
        # For non-MoE models (like Youtu), set to n_layer to use dense FFN for all layers
        # For MoE models (like DeepSeek-V2), this is the number of leading non-MoE layers
        has_moe = hparams.get("n_routed_experts") is not None
        first_k_dense_replace = hparams.get("first_k_dense_replace")
        if first_k_dense_replace is None:
            # Default: if no MoE, all layers are dense; if MoE, none are dense
            first_k_dense_replace = hparams["num_hidden_layers"] if not has_moe else 0
        self.gguf_writer.add_leading_dense_block_count(first_k_dense_replace)
        kv_lora_rank = hparams.get("kv_lora_rank", 512)
        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
        if "q_lora_rank" in hparams and hparams["q_lora_rank"] is not None:
            self.gguf_writer.add_q_lora_rank(hparams["q_lora_rank"])

        # note: deepseek2 using MLA converts into MQA with larger heads, then decompresses to MHA
        if not is_ocr:
            self.gguf_writer.add_kv_lora_rank(kv_lora_rank)
            self.gguf_writer.add_key_length(kv_lora_rank + hparams["qk_rope_head_dim"])
            self.gguf_writer.add_value_length(kv_lora_rank)
            self.gguf_writer.add_key_length_mla(hparams["qk_nope_head_dim"] + hparams["qk_rope_head_dim"])
            self.gguf_writer.add_value_length_mla(hparams["v_head_dim"])

        # MoE parameters (required by C++ code for DEEPSEEK2 arch)
        # For non-MoE models like Youtu, use intermediate_size as expert_feed_forward_length
        moe_intermediate_size = self.find_hparam(["moe_intermediate_size", "intermediate_size"], optional=False)
        self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)

        if (n_routed_experts := hparams.get("n_routed_experts")) is not None:
            self.gguf_writer.add_expert_count(n_routed_experts)

        # expert_shared_count is required by C++ code, default to 0 for non-MoE models
        n_shared_experts = hparams.get("n_shared_experts", 0)
        self.gguf_writer.add_expert_shared_count(n_shared_experts)

        # When not set, C++ code will use scale_w = false to skip the no-op scaling
        if (routed_scaling_factor := hparams.get("routed_scaling_factor")) is not None:
            self.gguf_writer.add_expert_weights_scale(routed_scaling_factor)

        if (norm_topk_prob := hparams.get("norm_topk_prob")) is not None and norm_topk_prob:
            self.gguf_writer.add_expert_weights_norm(norm_topk_prob)

        self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"])

        if (rope_mscale_all := self.rope_parameters.get("mscale_all_dim")) is not None:
            # [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX]
            # note: for legacy reasons, this is not consistent with the other usages of self.gguf_writer.add_rope_scaling_yarn_log_mul
            # ref https://github.com/ggml-org/llama.cpp/pull/17945
            self.gguf_writer.add_rope_scaling_yarn_log_mul(0.1 * rope_mscale_all)

    _experts: list[dict[str, Tensor]] | None = None

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        # skip lm_head.weight if tie_word_embeddings is True
        if self.hparams.get("tie_word_embeddings", False):
            if name == "lm_head.weight" or name == "model.lm_head.weight":
                logger.info("Skipping tied output layer 'lm_head.weight' (will use token_embd.weight)")
                return

        # skip Multi-Token Prediction (MTP) layers
        if self.skip_mtp:
            block_count = self.hparams["num_hidden_layers"]
            match = re.match(r"model.layers.(\d+)", name)
            if match and int(match.group(1)) >= block_count:
                return

        # process the experts separately
        if self.merge_expert and name.find("mlp.experts") != -1:
            n_experts = self.hparams["n_routed_experts"]
            assert bid is not None

            if self._experts is None:
                self._experts = [{} for _ in range(self.block_count)]

            self._experts[bid][name] = data_torch

            if len(self._experts[bid]) >= n_experts * 3:
                # merge the experts into a single 3d tensor
                for w_name in ["down_proj", "gate_proj", "up_proj"]:
                    datas: list[Tensor] = []

                    for xid in range(n_experts):
                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
                        datas.append(self._experts[bid][ename])
                        del self._experts[bid][ename]

                    data_torch = torch.stack(datas, dim=0)

                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"

                    yield from super().modify_tensors(data_torch, merged_name, bid)
                return
            else:
                return

        # note: MLA with the absorption optimization, needs these two split and k_b_proj transposed
        if name.endswith("kv_b_proj.weight"):
            name_kb = name.replace("kv_b_proj", "k_b_proj")
            name_vb = name.replace("kv_b_proj", "v_b_proj")

            n_head_kv = self.hparams["num_key_value_heads"]
            v_head_dim = self.hparams["v_head_dim"]
            qk_nope_head_dim = self.hparams["qk_nope_head_dim"]

            assert data_torch.shape[0] == n_head_kv * (v_head_dim + qk_nope_head_dim)

            kv_b = data_torch.view(n_head_kv, v_head_dim + qk_nope_head_dim, data_torch.shape[-1])
            k_b, v_b = torch.split(kv_b, [qk_nope_head_dim, v_head_dim], dim=1)
            k_b = k_b.transpose(1, 2)

            yield from super().modify_tensors(k_b, name_kb, bid)
            yield from super().modify_tensors(v_b, name_vb, bid)
            return

        yield from super().modify_tensors(data_torch, name, bid)

    def prepare_tensors(self):
        super().prepare_tensors()

        if self._experts is not None:
            # flatten `list[dict[str, Tensor]]` into `list[str]`
            experts = [k for d in self._experts for k in d.keys()]
            if len(experts) > 0:
                raise ValueError(f"Unprocessed experts: {experts}")


@ModelBase.register(
    "Mistral3ForConditionalGeneration",
    "Ministral3ForCausalLM",
)
class Mistral3Model(TextModel):
    class Ministral3Model(LlamaModel):
        model_arch = gguf.MODEL_ARCH.MISTRAL3

        def set_gguf_parameters(self):
            super().set_gguf_parameters()
            rope_params = self.rope_parameters
            if self.hparams.get("model_type") == "ministral3":
                assert rope_params, "ministral3 must have 'rope_parameters' config"
                assert rope_params["rope_type"] == "yarn", "ministral3 rope_type must be 'yarn'"
                self.gguf_writer.add_rope_scaling_yarn_log_mul(rope_params["mscale_all_dim"])
                self.gguf_writer.add_attn_temperature_scale(rope_params["llama_4_scaling_beta"])

    class Mistral4Model(DeepseekV2Model):
        model_arch = gguf.MODEL_ARCH.MISTRAL4
        skip_mtp = False # model contains no MTP layers, so no need to skip
        merge_expert = False # experts are already stacked as 3D

        def modify_tensors(self, data_torch, name, bid):
            if name.endswith(".down_proj") or name.endswith(".gate_up_proj"):
                name = name + ".weight"
            yield from super().modify_tensors(data_torch, name, bid)

    model_arch = gguf.MODEL_ARCH.MISTRAL3 # unused
    impl: TextModel

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        if self.hparams.get("model_type") == "mistral4":
            self.impl = Mistral3Model.Mistral4Model(*args, **kwargs)
        else:
            self.impl = Mistral3Model.Ministral3Model(*args, **kwargs)

    def set_vocab(self):
        self.impl.set_vocab()

    def set_gguf_parameters(self):
        self.impl.set_gguf_parameters()

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
        yield from self.impl.modify_tensors(data_torch, name, bid)

    def prepare_tensors(self):
        self.impl.prepare_tensors()

    def write_vocab(self):
        self.impl.write_vocab()

    def write(self):
        self.impl.write()


@ModelBase.register("MiniMaxM2ForCausalLM")
class MiniMaxM2Model(TextModel):
    model_arch = gguf.MODEL_ARCH.MINIMAXM2
    _experts_cache: dict[int, dict[str, Tensor]] = {}

    def set_gguf_parameters(self):
        super().set_gguf_parameters()

        self.gguf_writer.add_expert_feed_forward_length(self.find_hparam(["intermediate_size"]))
        self.gguf_writer.add_rope_dimension_count(self.find_hparam(["rotary_dim"]))

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
        # merge expert weights
        if 'experts' in name:
            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
            assert bid is not None

            expert_cache = self._experts_cache.setdefault(bid, {})
            expert_cache[name] = data_torch
            expert_weights = ["w1", "w2", "w3"]

            # not enough expert weights to merge
            if len(expert_cache) < n_experts * len(expert_weights):
                return

            for w_name in expert_weights:
                datas: list[Tensor] = []

                for xid in range(n_experts):
                    ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{w_name}.weight"
                    datas.append(expert_cache[ename])
                    del expert_cache[ename]

                data_torch = torch.stack(datas, dim=0)
                merged_name = f"model.layers.{bid}.block_sparse_moe.experts.{w_name}.weight"
                new_name = self.map_tensor_name(merged_name)
                yield from super().modify_tensors(data_torch, new_name, bid)

            del self._experts_cache[bid]
            return

        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("MiMoV2FlashForCausalLM", "MiMoV2ForCausalLM")
class MimoV2Model(TextModel):
    model_arch = gguf.MODEL_ARCH.MIMO2

    # MiMo V2-Flash, V2.5 and V2.5-Pro all ship 3 trained MTP layers under model.mtp.layers.{0,1,2}.
    # The HF config does not expose the count, so it's hardcoded to match the count found in the safetensors.
    _n_nextn = 3

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

        self.block_count = self.hparams["num_hidden_layers"] + self._n_nextn
        self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)

    @staticmethod
    def _tp_aware_qkv_dequant(weight: Tensor, scale_inv: Tensor,
                              n_q: int, n_kv: int, hd: int, vhd: int,
                              bs: int = 128) -> Tensor:
        # MiMo-V2.5 (TP=4) and V2.5-Pro (TP=8) ship qkv_proj sharded across TP
        # ranks; per rank, rows are stacked as [Q_per | K_per | V_per].
        # weight_scale_inv has ceil(rows_per_rank/bs) block-rows per rank (last
        # may extend past rows_per_rank with phantom rows not in the weight).
        # Naive repeat_interleave aligns rank 0 only and mis-applies scales to
        # later ranks once rows_per_rank isn't a multiple of bs.
        # Re-group the per-rank [Q_per|K_per|V_per] rows into a single fused
        # [Q | K | V] tensor matching the un-sharded original layout.
        q_size = n_q * hd
        k_size = n_kv * hd
        v_size = n_kv * vhd
        total_rows = q_size + k_size + v_size
        if weight.shape[0] != total_rows:
            raise ValueError(f"qkv_proj weight rows {weight.shape[0]} != q+k+v {total_rows}")

        # detect TP from scale_inv block count, descending order so larger matches first
        tp = None
        for cand in (8, 4):
            if total_rows % cand != 0:
                continue
            rpr = total_rows // cand
            bpr = (rpr + bs - 1) // bs
            if scale_inv.shape[0] == cand * bpr:
                tp = cand
                break
        if tp is None:
            raise ValueError(
                f"qkv_proj: cannot detect TP - scale_inv rows {scale_inv.shape[0]}, "
                f"q+k+v {total_rows}")

        q_per = q_size // tp
        k_per = k_size // tp
        v_per = v_size // tp
        rows_per_rank = q_per + k_per + v_per
        blocks_per_rank = (rows_per_rank + bs - 1) // bs

        scale_inv = scale_inv.float()
        # per-row scale-row index: rank * blocks_per_rank + (rr_in_rank // bs)
        row_idx = torch.arange(total_rows)
        rr = row_idx % rows_per_rank
        rank = row_idx // rows_per_rank
        scale_row_idx = rank * blocks_per_rank + (rr // bs)
        # gather: (total_rows, n_col_blocks)
        scale_per_row_block = scale_inv[scale_row_idx]
        # expand col-blocks -> cols: each block-col covers `bs` weight cols
        scale_full = scale_per_row_block.repeat_interleave(bs, dim=1)
        # crop to weight col count (in case last col-block isn't full)
        scale_full = scale_full[:, : weight.shape[1]]
        dequant = weight.float() * scale_full

        if tp == 1:
            return dequant

        # Re-group per-rank [Q_per|K_per|V_per] rows into unified [Q | K | V]
        qs, ks, vs = [], [], []
        for r in range(tp):
            base = r * rows_per_rank
            qs.append(dequant[base : base + q_per])
            ks.append(dequant[base + q_per : base + q_per + k_per])
            vs.append(dequant[base + q_per + k_per : base + rows_per_rank])
        return torch.cat(qs + ks + vs, dim=0)

    def dequant_model(self):
        # Capture raw FP8 (weight, scale_inv) lambdas for qkv_proj BEFORE super
        # rewrites them with the existing dequant. Replace super's lambda after
        # it runs so scale_inv removal still happens via the standard path.
        qkv_overrides: dict[str, tuple[Callable, Callable, int]] = {}
        qc = self.hparams.get("quantization_config")
        if isinstance(qc, dict) and qc.get("quant_method") == "fp8":
            pat = re.compile(r"^model\.layers\.(\d+)\.self_attn\.qkv_proj\.weight_scale_inv$")
            for name in list(self.model_tensors.keys()):
                m = pat.match(name)
                if not m:
                    continue
                weight_name = name.removesuffix("_scale_inv")
                if weight_name not in self.model_tensors:
                    continue
                qkv_overrides[weight_name] = (
                    self.model_tensors[weight_name],
                    self.model_tensors[name],
                    int(m.group(1)),
                )

        super().dequant_model()

        if not qkv_overrides:
            return

        n_q = self.hparams["num_attention_heads"]
        hd = self.hparams["head_dim"]
        vhd = self.hparams["v_head_dim"]
        hybrid = self.hparams["hybrid_layer_pattern"]
        n_layer_text = self.hparams["num_hidden_layers"]
        for weight_name, (w_fn, s_fn, bid) in qkv_overrides.items():
            # MTP layers (bid >= n_layer_text) use SWA-style attention dims
            is_swa = True if bid >= n_layer_text else hybrid[bid] == 1
            n_kv = self.hparams["swa_num_key_value_heads" if is_swa else "num_key_value_heads"]
            self.model_tensors[weight_name] = (
                lambda w_fn=w_fn, s_fn=s_fn, n_q=n_q, n_kv=n_kv, hd=hd, vhd=vhd:
                    MimoV2Model._tp_aware_qkv_dequant(w_fn(), s_fn(), n_q, n_kv, hd, vhd)
            )

    def set_gguf_parameters(self):
        super().set_gguf_parameters()

        assert self.hparams["swa_head_dim"] == self.hparams["head_dim"]
        assert self.hparams["swa_num_attention_heads"] == self.hparams["num_attention_heads"]
        assert self.hparams["swa_v_head_dim"] == self.hparams["v_head_dim"]
        assert self.hparams["topk_method"] == "noaux_tc"

        n_head_kv = self.hparams["num_key_value_heads"]
        n_head_kv_swa = self.hparams["swa_num_key_value_heads"]
        # Extend the per-layer pattern with SWA entries for the MTP blocks so the
        # runtime arrays (sized to extended block_count) are fully populated.
        hybrid = list(self.hparams["hybrid_layer_pattern"]) + [1] * self._n_nextn
        n_head_kv_arr = [n_head_kv_swa if use_swa == 1 else n_head_kv for use_swa in hybrid]
        self.gguf_writer.add_head_count_kv(n_head_kv_arr)

        self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
        self.gguf_writer.add_sliding_window_pattern(hybrid)
        self.gguf_writer.add_value_length(self.hparams["v_head_dim"])
        self.gguf_writer.add_expert_count(self.hparams["n_routed_experts"])
        self.gguf_writer.add_expert_feed_forward_length(self.hparams["moe_intermediate_size"])

        rope_dim = int(self.hparams["head_dim"] * self.hparams["partial_rotary_factor"])
        self.gguf_writer.add_rope_dimension_count(rope_dim)

        self.gguf_writer.add_layer_norm_rms_eps(self.hparams.get("layernorm_epsilon", 1e-5))

        v_scale = self.hparams.get("attention_value_scale")
        if v_scale is not None:
            self.gguf_writer.add_attn_value_scale(float(v_scale))

        self.gguf_writer.add_nextn_predict_layers(self._n_nextn)

    _experts: list[dict[str, Tensor]] | None = None

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        if "attention_sink" in name and not name.endswith(".weight"):
            name += ".weight"

        return super().filter_tensors((name, gen))

    def modify_tensors(self, data_torch, name, bid):
        # Remap MTP/NextN tensors to additional layer slots so the standard tensor map handles them.
        # HF: model.mtp.layers.{i}.foo  ->  model.layers.{n_layer_text + i}.foo
        m = re.match(r"^model\.mtp\.layers\.(\d+)\.(.*)$", name)
        if m is not None:
            mtp_idx = int(m.group(1))
            assert mtp_idx < self._n_nextn, f"MTP layer index {mtp_idx} >= _n_nextn ({self._n_nextn})"
            rest = m.group(2)
            n_layer_text = self.hparams["num_hidden_layers"]
            new_bid = n_layer_text + mtp_idx
            name = f"model.layers.{new_bid}.{rest}"
            bid = new_bid

        # process the experts separately
        if name.find("mlp.experts") != -1:
            n_experts = self.hparams["n_routed_experts"]
            assert bid is not None

            if self._experts is None:
                self._experts = [{} for _ in range(self.block_count)]

            self._experts[bid][name] = data_torch

            if len(self._experts[bid]) >= n_experts * 3:
                # merge the experts into a single 3d tensor
                for w_name in ["gate_proj", "up_proj", "down_proj"]:
                    datas: list[Tensor] = []

                    for xid in range(n_experts):
                        ename_to_retrieve = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
                        datas.append(self._experts[bid][ename_to_retrieve])
                        del self._experts[bid][ename_to_retrieve]

                    data_torch = torch.stack(datas, dim=0)
                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"

                    yield from super().modify_tensors(data_torch, merged_name, bid)
                return
            else:
                return
        yield from super().modify_tensors(data_torch, name, bid)

    def prepare_tensors(self):
        super().prepare_tensors()

        if self._experts is not None:
            # flatten `list[dict[str, Tensor]]` into `list[str]`
            experts = [k for d in self._experts for k in d.keys()]
            if len(experts) > 0:
                raise ValueError(f"Unprocessed experts: {experts}")


@ModelBase.register("MiMoV2ForCausalLM")
class MiMoV2VisionModel(MmprojModel):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        assert self.hparams_vision is not None
        hp = self.hparams_vision

        hp["image_size"] = hp.get("image_size", 560)
        hp["num_attention_heads"] = hp.get("num_heads", 32)
        hp["num_hidden_layers"] = hp.get("depth", 28)

        self.n_q_heads = int(hp["num_heads"])
        self.num_kv_heads = int(hp.get("num_key_value_heads", 8))
        self.head_dim = int(hp.get("qk_channels", 64))
        self.spatial_merge_size = int(hp["spatial_merge_size"])
        # MiMoV2 vision RMSNorm: HF uses getattr(config, "rms_norm_eps", 1e-6) and the
        # field is absent from MiMo-V2.5's vision_config
        self.rms_norm_eps = float(hp.get("rms_norm_eps", 1e-6))

        # fullatt_block_indexes are also reflected in vit_window_attn_types as -1
        self.fullatt_block_indexes = list(hp.get("fullatt_block_indexes") or [])
        self.vit_window_attn_types = list(hp.get("vit_window_attn_types") or [])
        self.visual_token_window_size = int(hp.get("visual_token_window_size", -1))
        self.use_sink = bool(hp.get("use_sink", False))

    def set_gguf_parameters(self):
        super().set_gguf_parameters()

        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.MIMOVL)
        self.gguf_writer.add_vision_use_silu(True)
        self.gguf_writer.add_vision_head_count_kv(self.num_kv_heads)
        self.gguf_writer.add_vision_spatial_merge_size(self.spatial_merge_size)
        self.gguf_writer.add_uint32(gguf.Keys.ClipVision.WINDOW_SIZE, self.visual_token_window_size)
        self.gguf_writer.add_vision_wa_pattern_mode(self.vit_window_attn_types)
        self.gguf_writer.add_vision_attention_layernorm_eps(self.rms_norm_eps)
        self.gguf_writer.add_vision_min_pixels(int(self.preprocessor_config["min_pixels"]))
        self.gguf_writer.add_vision_max_pixels(int(self.preprocessor_config["max_pixels"]))

    def tensor_force_quant(self, name, new_name, bid, n_dims):
        # Sinks must be F32: any sink-style softmax/mask add in ggml requires
        # F32, and we fold sinks into a host-built F32 mask at encode time.
        if new_name.endswith(".attn_sinks"):
            return gguf.GGMLQuantizationType.F32
        return super().tensor_force_quant(name, new_name, bid, n_dims)

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, _ = item
        if not name.startswith("visual."):
            return None
        return super().filter_tensors(item)

    def modify_tensors(self, data_torch, name, bid):
        # Conv3D patch embed: split along the temporal axis (kt=2) into two Conv2D
        # weights that the existing qwen2vl-style two-Conv2D path consumes.
        if name == "visual.patch_embed.proj.weight":
            _, _, kt, _, _ = data_torch.shape
            if kt != 2:
                raise ValueError(f"unexpected temporal_patch_size: {kt}")
            embd_name = gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_ENC_EMBD_PATCH]
            yield (embd_name + ".weight",   data_torch[:, :, 0, ...])
            yield (embd_name + ".weight.1", data_torch[:, :, 1, ...])
            return

        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("Step3p5ForCausalLM")
class Step35Model(TextModel):
    model_arch = gguf.MODEL_ARCH.STEP35

    def set_gguf_parameters(self):
        rope_theta = self.hparams.get("rope_theta")
        if isinstance(rope_theta, list):
            self.hparams["rope_theta"] = float(rope_theta[0])
            self.hparams["local_rope_theta"] = float(rope_theta[1])
            self.rope_parameters["rope_theta"] = self.hparams["rope_theta"]
            self.rope_parameters["sliding_attention"] = {"rope_theta": self.hparams["local_rope_theta"]}

        super().set_gguf_parameters()

        layer_types = self.hparams.get("layer_types") or []
        partial_rotary_factors = self.hparams.get("partial_rotary_factors") or []
        attn_other = self.hparams.get("attention_other_setting") or {}

        n_head_base = self.hparams["num_attention_heads"]
        n_kv_base = self.hparams["num_attention_groups"]

        n_head_swa = attn_other.get("num_attention_heads", n_head_base)
        n_kv_swa = attn_other.get("num_attention_groups", n_kv_base)

        layer_types = layer_types[: self.block_count]
        partial_rotary_factors = partial_rotary_factors[: self.block_count]
        assert [1.0 if lt == "sliding_attention" else 0.5 for lt in layer_types] == partial_rotary_factors
        head_arr = [n_head_swa if lt == "sliding_attention" else n_head_base for lt in layer_types]
        kv_arr = [n_kv_swa if lt == "sliding_attention" else n_kv_base for lt in layer_types]
        swa_pat = [lt == "sliding_attention" for lt in layer_types]

        self.gguf_writer.add_head_count(head_arr)
        self.gguf_writer.add_head_count_kv(kv_arr)

        self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
        self.gguf_writer.add_sliding_window_pattern(swa_pat)

        self.gguf_writer.add_value_length(self.hparams["head_dim"])

        # MoE params
        self.gguf_writer.add_expert_count(self.hparams["moe_num_experts"])
        self.gguf_writer.add_expert_used_count(self.hparams["moe_top_k"])
        self.gguf_writer.add_expert_feed_forward_length(self.hparams["moe_intermediate_size"])
        self.gguf_writer.add_expert_shared_feed_forward_length(self.hparams["share_expert_dim"])

        if (moe_router_scaling_factor := self.hparams.get("moe_router_scaling_factor")) is not None:
            self.gguf_writer.add_expert_weights_scale(moe_router_scaling_factor)
        if (norm_expert_weight := self.hparams.get("norm_expert_weight")) is not None:
            self.gguf_writer.add_expert_weights_norm(norm_expert_weight)

        # leading dense blocks
        leading_dense = 0
        moe_layers_enum = self.hparams.get("moe_layers_enum")
        if isinstance(moe_layers_enum, str) and moe_layers_enum.strip():
            moe_layers = sorted(int(i) for i in moe_layers_enum.strip().split(","))
            if moe_layers:
                leading_dense = max(0, moe_layers[0])
        self.gguf_writer.add_leading_dense_block_count(leading_dense)
        self.gguf_writer.add_moe_every_n_layers(int(self.hparams.get("moe_every_n_layer", 1)))

        self.gguf_writer.add_layer_norm_rms_eps(self.hparams.get("rms_norm_eps", 1e-5))

        # Optional per-layer SwiGLU clamps.
        if (limits := self.hparams.get("swiglu_limits")) is not None:
            limits_f = [0.0 if v is None else float(v) for v in limits[: self.block_count]]
            self.gguf_writer.add_swiglu_clamp_exp(limits_f)
        if (limits_shared := self.hparams.get("swiglu_limits_shared")) is not None:
            limits_shared_f = [0.0 if v is None else float(v) for v in limits_shared[: self.block_count]]
            self.gguf_writer.add_swiglu_clamp_shexp(limits_shared_f)

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        # Map router bias (expert selection bias) to a GGUF bias tensor
        if name.endswith(".moe.router_bias"):
            name += ".bias"

        return super().filter_tensors((name, gen))

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
        # remove mtp layers
        if (m := re.match(r"model\.layers\.(\d+)\.", name)) is not None:
            il = int(m.group(1))
            n_main = int(self.hparams.get("num_hidden_layers", self.block_count))
            if il >= n_main:
                return
        if name.endswith("norm.weight"):
            data_torch += 1.0

        if name.endswith((".self_attn.g_proj.weight", ".moe.gate.weight", ".moe.up_proj.weight", ".moe.gate_proj.weight", ".moe.down_proj.weight")):
            data_torch = data_torch.squeeze().contiguous()

        yield from super().modify_tensors(data_torch, name, bid)

    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
        # Step35 can optionally use Llama-3 style RoPE scaling (HF: rope_scaling.rope_type == "llama3").
        # llama.cpp represents this via a single extra tensor: "rope_freqs.weight" (aka MODEL_TENSOR.ROPE_FREQS).
        rope_params = self.rope_parameters.get("full_attention", self.rope_parameters)
        rope_type = rope_params.get("rope_type") or ""
        if rope_type.lower() != "llama3":
            return

        # Step35 configs can carry per-layer rope_theta as a list; for llama3 rope factors we use the base value.
        rope_theta = self.hparams.get("rope_theta", 10000.0)
        if isinstance(rope_theta, list):
            rope_theta = rope_theta[0]
        base = float(rope_theta)
        if (dim := self.hparams.get("head_dim")) is None:
            dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
        dim = int(dim)

        freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))

        factor = float(rope_params.get("factor", 8.0))
        low_freq_factor = float(rope_params.get("low_freq_factor", 1.0))
        high_freq_factor = float(rope_params.get("high_freq_factor", 4.0))
        old_context_len = int(rope_params.get("original_max_position_embeddings", self.hparams.get("original_max_position_embeddings", 8192)))

        low_freq_wavelen = old_context_len / low_freq_factor
        high_freq_wavelen = old_context_len / high_freq_factor

        rope_factors: list[float] = []
        for freq in freqs:
            wavelen = 2 * math.pi / float(freq)
            if wavelen < high_freq_wavelen:
                rope_factors.append(1.0)
            elif wavelen > low_freq_wavelen:
                rope_factors.append(factor)
            else:
                smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
                rope_factors.append(1.0 / ((1.0 - smooth) / factor + smooth))

        yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32))


@ModelBase.register("PanguEmbeddedForCausalLM")
class PanguEmbeddedModel(TextModel):
    model_arch = gguf.MODEL_ARCH.PANGU_EMBED

    def set_vocab(self):
        self._set_vocab_sentencepiece()

        tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
        if tokenizer_config_file.is_file():
            with open(tokenizer_config_file, "r", encoding="utf-8") as f:
                tokenizer_config_json = json.load(f)
                if "add_prefix_space" in tokenizer_config_json:
                    self.gguf_writer.add_add_space_prefix(tokenizer_config_json["add_prefix_space"])

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        hparams = self.hparams
        self.gguf_writer.add_vocab_size(hparams["vocab_size"])

        # PanguEmbedded's hparam loaded from config.json without head_dim
        if (rope_dim := hparams.get("head_dim")) is None:
            rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
        self.gguf_writer.add_rope_dimension_count(rope_dim)

        if hparams.get("head_dim") is None:
            self.gguf_writer.add_key_length(rope_dim)
            self.gguf_writer.add_value_length(rope_dim)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        if name == "lm_head.weight":
            if self.hparams.get("tie_word_embeddings", False):
                logger.info("Skipping tied output layer 'lm_head.weight'")
                return
        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("Dots1ForCausalLM")
class Dots1Model(Qwen2MoeModel):
    model_arch = gguf.MODEL_ARCH.DOTS1

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.hparams["num_experts"] = self.hparams["n_routed_experts"]

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        self.gguf_writer.add_leading_dense_block_count(self.hparams["first_k_dense_replace"])
        self.gguf_writer.add_expert_shared_count(self.hparams["n_shared_experts"])
        self.gguf_writer.add_expert_weights_scale(self.hparams["routed_scaling_factor"])
        self.gguf_writer.add_expert_weights_norm(self.hparams["norm_topk_prob"])

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
        if "shared_experts" in name:
            yield from ModelBase.modify_tensors(self, data_torch, name, bid)
        else:
            yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("PLMForCausalLM")
class PLMModel(TextModel):
    model_arch = gguf.MODEL_ARCH.PLM

    def set_vocab(self):
        self._set_vocab_gpt2()

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        hparams = self.hparams
        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
        self.gguf_writer.add_kv_lora_rank(hparams["kv_lora_rank"])
        self.gguf_writer.add_key_length(hparams["qk_nope_head_dim"] + hparams["qk_rope_head_dim"])
        self.gguf_writer.add_value_length(hparams["v_head_dim"])
        self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"])

    def prepare_tensors(self):
        super().prepare_tensors()


@ModelBase.register("T5WithLMHeadModel")
@ModelBase.register("T5ForConditionalGeneration")
@ModelBase.register("MT5ForConditionalGeneration")
@ModelBase.register("UMT5ForConditionalGeneration")
@ModelBase.register("UMT5Model")
class T5Model(TextModel):
    model_arch = gguf.MODEL_ARCH.T5

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.shared_token_embeddings_found = False

    def set_vocab(self):
        # to avoid TypeError: Descriptors cannot be created directly
        # exception when importing sentencepiece_model_pb2
        os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python"
        from sentencepiece import SentencePieceProcessor
        from sentencepiece import sentencepiece_model_pb2 as model

        tokenizer_path = self.dir_model / 'tokenizer.model'

        # many older models use spiece.model tokenizer model filename
        if not tokenizer_path.is_file():
            tokenizer_path = self.dir_model / 'spiece.model'

        if not tokenizer_path.is_file():
            raise FileNotFoundError(f"File not found: {tokenizer_path}")

        sentencepiece_model = model.ModelProto()  # pyright: ignore[reportAttributeAccessIssue] # ty: ignore[unresolved-attribute]
        sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read())

        # some models like Pile-T5 family use BPE tokenizer instead of Unigram
        if sentencepiece_model.trainer_spec.model_type == 2:  # BPE
            # assure the tokenizer model file name is correct
            assert tokenizer_path.name == 'tokenizer.model'
            return self._set_vocab_sentencepiece()
        else:
            assert sentencepiece_model.trainer_spec.model_type == 1  # UNIGRAM

        add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix
        remove_whitespaces = sentencepiece_model.normalizer_spec.remove_extra_whitespaces
        precompiled_charsmap = sentencepiece_model.normalizer_spec.precompiled_charsmap

        tokenizer = SentencePieceProcessor()
        tokenizer.LoadFromFile(str(tokenizer_path))

        vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())

        tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
        scores: list[float] = [-10000.0] * vocab_size
        toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size

        for token_id in range(tokenizer.vocab_size()):
            piece = tokenizer.IdToPiece(token_id)
            text = piece.encode("utf-8")
            score = tokenizer.GetScore(token_id)

            toktype = SentencePieceTokenTypes.NORMAL
            if tokenizer.IsUnknown(token_id):
                toktype = SentencePieceTokenTypes.UNKNOWN
            elif tokenizer.IsControl(token_id):
                toktype = SentencePieceTokenTypes.CONTROL
            elif tokenizer.IsUnused(token_id):
                toktype = SentencePieceTokenTypes.UNUSED
            elif tokenizer.IsByte(token_id):
                toktype = SentencePieceTokenTypes.BYTE

            tokens[token_id] = text
            scores[token_id] = score
            toktypes[token_id] = toktype

        added_tokens_file = self.dir_model / 'added_tokens.json'
        if added_tokens_file.is_file():
            with open(added_tokens_file, "r", encoding="utf-8") as f:
                added_tokens_json = json.load(f)
                for key in added_tokens_json:
                    token_id = added_tokens_json[key]
                    if token_id >= vocab_size:
                        logger.warning(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}')
                        continue

                    tokens[token_id] = key.encode("utf-8")
                    scores[token_id] = -1000.0
                    toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED

        if vocab_size > len(tokens):
            pad_count = vocab_size - len(tokens)
            logger.debug(f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]")
            for i in range(1, pad_count + 1):
                tokens.append(bytes(f"[PAD{i}]", encoding="utf-8"))
                scores.append(-1000.0)
                toktypes.append(SentencePieceTokenTypes.UNUSED)

        self.gguf_writer.add_tokenizer_model("t5")
        self.gguf_writer.add_tokenizer_pre("default")
        self.gguf_writer.add_token_list(tokens)
        self.gguf_writer.add_token_scores(scores)
        self.gguf_writer.add_token_types(toktypes)
        self.gguf_writer.add_add_space_prefix(add_prefix)
        self.gguf_writer.add_remove_extra_whitespaces(remove_whitespaces)
        if precompiled_charsmap:
            self.gguf_writer.add_precompiled_charsmap(precompiled_charsmap)

        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
        special_vocab.add_to_gguf(self.gguf_writer)

    def set_gguf_parameters(self):
        if (n_ctx := self.find_hparam(["n_positions"], optional=True)) is None:
            logger.warning("Couldn't find context length in config.json, assuming default value of 512")
            n_ctx = 512
        self.gguf_writer.add_context_length(n_ctx)
        self.gguf_writer.add_embedding_length(self.hparams["d_model"])
        self.gguf_writer.add_feed_forward_length(self.hparams["d_ff"])
        self.gguf_writer.add_block_count(self.block_count)
        if (dec_n_layer := self.hparams.get("num_decoder_layers")) is not None:
            self.gguf_writer.add_decoder_block_count(dec_n_layer)
        self.gguf_writer.add_head_count(self.hparams["num_heads"])
        self.gguf_writer.add_key_length(self.hparams["d_kv"])
        self.gguf_writer.add_value_length(self.hparams["d_kv"])
        self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
        self.gguf_writer.add_relative_attn_buckets_count(self.hparams["relative_attention_num_buckets"])
        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"])
        self.gguf_writer.add_decoder_start_token_id(self.hparams["decoder_start_token_id"])
        self.gguf_writer.add_file_type(self.ftype)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        # T5 based models contain shared token embeddings tensors saved randomly as either "encoder.embed_tokens.weight",
        # "decoder.embed_tokens.weight" or "shared.weight" tensor. In some models there are even multiple of them stored
        # in the safetensors files. We use the first tensor from these three as the token embeddings for both encoder
        # and decoder and ignore the remaining ones.
        if name in ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight", "shared.weight"]:
            if not self.shared_token_embeddings_found:
                name = "shared.weight"
                self.shared_token_embeddings_found = True
            else:
                logger.debug(f"Skipping shared tensor {name!r} in safetensors so that convert can end normally.")
                return

        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("T5EncoderModel")
class T5EncoderModel(TextModel):
    model_arch = gguf.MODEL_ARCH.T5ENCODER

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.shared_token_embeddings_found = False

    def set_vocab(self):
        # to avoid TypeError: Descriptors cannot be created directly
        # exception when importing sentencepiece_model_pb2
        os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python"
        from sentencepiece import SentencePieceProcessor
        from sentencepiece import sentencepiece_model_pb2 as model

        tokenizer_path = self.dir_model / 'tokenizer.model'

        # many older models use spiece.model tokenizer model filename
        if not tokenizer_path.is_file():
            tokenizer_path = self.dir_model / 'spiece.model'

        if not tokenizer_path.is_file():
            raise FileNotFoundError(f"File not found: {tokenizer_path}")

        sentencepiece_model = model.ModelProto()  # pyright: ignore[reportAttributeAccessIssue] # ty: ignore[unresolved-attribute]
        sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read())

        # some models like Pile-T5 family use BPE tokenizer instead of Unigram
        if sentencepiece_model.trainer_spec.model_type == 2:  # BPE
            # assure the tokenizer model file name is correct
            assert tokenizer_path.name == 'tokenizer.model'
            return self._set_vocab_sentencepiece()
        else:
            assert sentencepiece_model.trainer_spec.model_type == 1  # UNIGRAM

        add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix
        remove_whitespaces = sentencepiece_model.normalizer_spec.remove_extra_whitespaces
        precompiled_charsmap = sentencepiece_model.normalizer_spec.precompiled_charsmap

        tokenizer = SentencePieceProcessor()
        tokenizer.LoadFromFile(str(tokenizer_path))

        vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())

        tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
        scores: list[float] = [-10000.0] * vocab_size
        toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size

        for token_id in range(tokenizer.vocab_size()):
            piece = tokenizer.IdToPiece(token_id)
            text = piece.encode("utf-8")
            score = tokenizer.GetScore(token_id)

            toktype = SentencePieceTokenTypes.NORMAL
            if tokenizer.IsUnknown(token_id):
                toktype = SentencePieceTokenTypes.UNKNOWN
            elif tokenizer.IsControl(token_id):
                toktype = SentencePieceTokenTypes.CONTROL
            elif tokenizer.IsUnused(token_id):
                toktype = SentencePieceTokenTypes.UNUSED
            elif tokenizer.IsByte(token_id):
                toktype = SentencePieceTokenTypes.BYTE

            tokens[token_id] = text
            scores[token_id] = score
            toktypes[token_id] = toktype

        added_tokens_file = self.dir_model / 'added_tokens.json'
        if added_tokens_file.is_file():
            with open(added_tokens_file, "r", encoding="utf-8") as f:
                added_tokens_json = json.load(f)
                for key in added_tokens_json:
                    token_id = added_tokens_json[key]
                    if token_id >= vocab_size:
                        logger.warning(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}')
                        continue

                    tokens[token_id] = key.encode("utf-8")
                    scores[token_id] = -1000.0
                    toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED

        if vocab_size > len(tokens):
            pad_count = vocab_size - len(tokens)
            logger.debug(f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]")
            for i in range(1, pad_count + 1):
                tokens.append(bytes(f"[PAD{i}]", encoding="utf-8"))
                scores.append(-1000.0)
                toktypes.append(SentencePieceTokenTypes.UNUSED)

        self.gguf_writer.add_tokenizer_model("t5")
        self.gguf_writer.add_tokenizer_pre("default")
        self.gguf_writer.add_token_list(tokens)
        self.gguf_writer.add_token_scores(scores)
        self.gguf_writer.add_token_types(toktypes)
        self.gguf_writer.add_add_space_prefix(add_prefix)
        self.gguf_writer.add_remove_extra_whitespaces(remove_whitespaces)
        if precompiled_charsmap:
            self.gguf_writer.add_precompiled_charsmap(precompiled_charsmap)

        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
        special_vocab.add_to_gguf(self.gguf_writer)

    def set_gguf_parameters(self):
        if (n_ctx := self.find_hparam(["n_positions"], optional=True)) is None:
            logger.warning("Couldn't find context length in config.json, assuming default value of 512")
            n_ctx = 512
        self.gguf_writer.add_context_length(n_ctx)
        self.gguf_writer.add_embedding_length(self.hparams["d_model"])
        self.gguf_writer.add_feed_forward_length(self.hparams["d_ff"])
        self.gguf_writer.add_block_count(self.block_count)
        self.gguf_writer.add_head_count(self.hparams["num_heads"])
        self.gguf_writer.add_key_length(self.hparams["d_kv"])
        self.gguf_writer.add_value_length(self.hparams["d_kv"])
        self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
        self.gguf_writer.add_relative_attn_buckets_count(self.hparams["relative_attention_num_buckets"])
        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"])
        self.gguf_writer.add_file_type(self.ftype)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        # T5 based models contain shared token embeddings tensors saved randomly as either "encoder.embed_tokens.weight",
        # "decoder.embed_tokens.weight" or "shared.weight" tensor. In some models there are even multiple of them stored
        # in the safetensors files. We use the first tensor from these three as the token embeddings for both encoder
        # and decoder and ignore the remaining ones.
        if name in ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight", "shared.weight"]:
            if not self.shared_token_embeddings_found:
                name = "shared.weight"
                self.shared_token_embeddings_found = True
            else:
                logger.debug(f"Skipping shared tensor {name!r} in safetensors so that convert can end normally.")
                return

        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("Jais2ForCausalLM")
class Jais2Model(TextModel):
    model_arch = gguf.MODEL_ARCH.JAIS2

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        hparams = self.hparams
        head_dim = hparams.get("head_dim", hparams["hidden_size"] // hparams["num_attention_heads"])
        self.gguf_writer.add_rope_dimension_count(head_dim)


@ModelBase.register("JAISLMHeadModel")
class JaisModel(TextModel):
    model_arch = gguf.MODEL_ARCH.JAIS

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

        # SwigLU activation
        assert self.hparams["activation_function"] == "swiglu"
        # ALiBi position embedding
        assert self.hparams["position_embedding_type"] == "alibi"

        # Embeddings scale
        self.embeddings_scale = 1.0
        if 'mup_embeddings_scale' in self.hparams:
            self.embeddings_scale = self.hparams['mup_embeddings_scale']
        elif 'embeddings_scale' in self.hparams:
            self.embeddings_scale = self.hparams['embeddings_scale']
        else:
            assert False

        self.width_scale = 1.0
        if 'mup_output_alpha' in self.hparams:
            assert 'mup_width_scale' in self.hparams
            self.width_scale = self.hparams['mup_output_alpha'] * self.hparams['mup_width_scale']
        elif 'width_scale' in self.hparams:
            self.width_scale = self.hparams['width_scale']
        else:
            assert False

        self.max_alibi_bias = 8.0

    def set_vocab(self):
        self._set_vocab_gpt2()

    def set_gguf_parameters(self):
        self.gguf_writer.add_block_count(self.block_count)
        self.gguf_writer.add_context_length(self.hparams["n_positions"])
        self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
        self.gguf_writer.add_feed_forward_length(self.hparams["n_inner"])
        self.gguf_writer.add_head_count(self.hparams["n_head"])
        self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
        self.gguf_writer.add_file_type(self.ftype)

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        # we don't need these
        if name.endswith((".attn.bias")):
            return None

        return super().filter_tensors(item)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        if name.endswith(("relative_pe.slopes")):
            # Calculate max ALiBi bias (this is the inverse of the ALiBi calculation)
            # Some other models has max_alibi_bias spelled out explicitly in the hyperparams,
            # but Jais's PyTorch model simply precalculates the slope values and places them
            # in relative_pes.slopes
            n_head_closest_log2 = 2 ** math.floor(math.log2(self.hparams["n_head"]))
            first_val = float(data_torch[0].item())
            self.max_alibi_bias = -round(math.log2(first_val) * n_head_closest_log2)

            return

        if name.endswith((".c_attn.weight", ".c_proj.weight", ".c_fc.weight", ".c_fc2.weight")):
            data_torch = data_torch.transpose(1, 0)

        new_name = self.map_tensor_name(name)

        if new_name == self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD):
            yield from super().modify_tensors(data_torch * self.embeddings_scale, new_name, bid)
        elif new_name == self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT):
            yield from super().modify_tensors(data_torch * self.width_scale, new_name, bid)
        else:
            yield from super().modify_tensors(data_torch, new_name, bid)

    def prepare_tensors(self):
        super().prepare_tensors()
        self.gguf_writer.add_max_alibi_bias(self.max_alibi_bias)


@ModelBase.register("Glm4ForCausalLM", "Glm4vForConditionalGeneration")
class Glm4Model(TextModel):
    model_arch = gguf.MODEL_ARCH.GLM4
    use_mrope = False
    partial_rotary_factor = 0.5

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.partial_rotary_factor = self.rope_parameters.get("partial_rotary_factor", 0.5)
        if "mrope_section" in self.rope_parameters:
            self.use_mrope = True
            logger.info("Q/K weight will need to be permuted for M-RoPE")

    def set_vocab(self):
        from transformers import AutoTokenizer
        tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
        tokens, toktypes, tokpre = self.get_vocab_base()
        self.gguf_writer.add_tokenizer_model("gpt2")
        self.gguf_writer.add_tokenizer_pre(tokpre)
        self.gguf_writer.add_token_list(tokens)
        self.gguf_writer.add_token_types(toktypes)
        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
        special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
        special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"])  # ty: ignore[unresolved-attribute]
        special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
        special_vocab._set_special_token("bos", tokenizer.get_added_vocab()["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
        special_vocab.add_to_gguf(self.gguf_writer)

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        if (rope_dim := self.hparams.get("head_dim")) is None:
            rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
        self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.partial_rotary_factor))

    @staticmethod
    def normal_to_neox(weights: Tensor, n_head: int, n_head_kv: int, head_dim: int, partial_rotary_factor: float) -> Tensor:
        orig_shape = weights.shape
        if len(orig_shape) == 1:
            weights = weights.unsqueeze(1)  # [out_dim, 1]
        if len(weights.shape) != 2:
            raise ValueError("Only 1D and 2D tensors are supported.")
        n_effective_heads = weights.shape[0] // head_dim
        if n_head_kv is not None and n_effective_heads != n_head:
            if n_effective_heads != n_head_kv:
                raise AssertionError(f"Mismatch in effective heads: computed {n_effective_heads}, expected {n_head} or {n_head_kv}")
        rotary_dim = int(head_dim * partial_rotary_factor)
        if rotary_dim % 2 != 0:
            raise ValueError("rotary_dim must be even.")
        reshaped = weights.reshape(n_effective_heads, head_dim, -1)
        rot_part = reshaped[:, :rotary_dim, :]
        non_rot_part = reshaped[:, rotary_dim:, :]
        permuted_rot = torch.cat((rot_part[:, ::2, :], rot_part[:, 1::2, :]), dim=1)
        combined = torch.cat((permuted_rot, non_rot_part), dim=1)
        result = combined.reshape(weights.shape)
        return result if len(orig_shape) != 1 else result.squeeze(1)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        if self.use_mrope:
            n_head = self.hparams["num_attention_heads"]
            n_kv_head = self.hparams["num_key_value_heads"]
            n_embd = self.hparams["hidden_size"]
            head_dim = self.hparams.get("head_dim", n_embd // n_head)
            # because llama.cpp M-RoPE kernel only supports Neox ordering, we have to permute the weights here
            if name.endswith(("q_proj.weight", "q_proj.bias")):
                data_torch = Glm4Model.normal_to_neox(data_torch, n_head, n_head, head_dim, self.partial_rotary_factor)
            if name.endswith(("k_proj.weight", "k_proj.bias")):
                data_torch = Glm4Model.normal_to_neox(data_torch, n_head, n_kv_head, head_dim, self.partial_rotary_factor)
        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("GlmOcrForConditionalGeneration")
class GlmOCRModel(Glm4Model):
    model_arch = gguf.MODEL_ARCH.GLM4
    use_mrope = False
    partial_rotary_factor = 0.5

    # Note: GLM-OCR is the same as GLM4, but with an extra NextN/MTP prediction layer

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        # GLM-OCR has num_hidden_layers + 1 actual layers (including NextN layer)
        self.block_count = self.hparams["num_hidden_layers"] + self.hparams.get("num_nextn_predict_layers", 0)
        self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        # NextN/MTP prediction layers
        if (num_nextn_predict_layers := self.hparams.get("num_nextn_predict_layers")) is not None:
            self.gguf_writer.add_nextn_predict_layers(num_nextn_predict_layers)


@ModelBase.register("Glm4MoeForCausalLM", "Glm4vMoeForConditionalGeneration")
class Glm4MoeModel(TextModel):
    model_arch = gguf.MODEL_ARCH.GLM4_MOE

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        # GLM4_MOE has num_hidden_layers + 1 actual layers (including NextN layer)
        self.block_count = self.hparams["num_hidden_layers"] + self.hparams.get("num_nextn_predict_layers", 0)
        self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)

    def set_vocab(self):
        return self._set_vocab_glm()

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        if (rope_dim := self.hparams.get("head_dim")) is None:
            rope_dim = (
                self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
            )
        self.gguf_writer.add_rope_dimension_count(
            int(rope_dim * self.hparams.get("partial_rotary_factor", 0.5))
        )

        # MoE parameters - Use only routed expert count (shared experts handled separately)
        if (n_routed_experts := self.hparams.get("n_routed_experts")) is not None:
            self.gguf_writer.add_expert_count(n_routed_experts)
        if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None:
            self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
        if (n_shared_experts := self.hparams.get("n_shared_experts")) is not None:
            self.gguf_writer.add_expert_shared_count(n_shared_experts)
        if (first_k_dense_replace := self.hparams.get("first_k_dense_replace")) is not None:
            self.gguf_writer.add_leading_dense_block_count(first_k_dense_replace)

        # Expert gating function (sigmoid for GLM4_MOE)
        self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)

        # Routed scaling factor
        if (routed_scaling_factor := self.hparams.get("routed_scaling_factor")) is not None:
            self.gguf_writer.add_expert_weights_scale(routed_scaling_factor)

        # Normalise topk probabilities
        if (norm_topk_prob := self.hparams.get("norm_topk_prob")) is not None:
            self.gguf_writer.add_expert_weights_norm(norm_topk_prob)

        # NextN/MTP prediction layers
        if (num_nextn_predict_layers := self.hparams.get("num_nextn_predict_layers")) is not None:
            self.gguf_writer.add_nextn_predict_layers(num_nextn_predict_layers)

    _experts: list[dict[str, Tensor]] | None = None

    # note: unlike GLM4V non-MoE, we don't need to permute Q/K here since GLM4V_MOE uses Neox ordering already
    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        # Handle main token embedding (but not layer-specific NextN embeddings)
        if name == "model.embed_tokens.weight" and ".layers." not in name:
            yield from super().modify_tensors(data_torch, "token_embd.weight", bid)
            return

        # Handle routed experts
        if name.find("mlp.experts") != -1:
            n_experts = self.hparams["n_routed_experts"]
            assert bid is not None

            if self._experts is None:
                self._experts = [{} for _ in range(self.block_count)]

            self._experts[bid][name] = data_torch

            if len(self._experts[bid]) >= n_experts * 3:
                # merge the experts into a single 3d tensor
                for w_name in ["down_proj", "gate_proj", "up_proj"]:
                    datas: list[Tensor] = []

                    for xid in range(n_experts):
                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
                        datas.append(self._experts[bid][ename])
                        del self._experts[bid][ename]

                    data_torch = torch.stack(datas, dim=0)

                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"

                    yield from super().modify_tensors(data_torch, merged_name, bid)
                return
            else:
                return

        yield from super().modify_tensors(data_torch, name, bid)

    def prepare_tensors(self):
        super().prepare_tensors()
        if self._experts is not None:
            # flatten `list[dict[str, Tensor]]` into `list[str]`
            experts = [k for d in self._experts for k in d.keys()]
            if len(experts) > 0:
                raise ValueError(f"Unprocessed experts: {experts}")


@ModelBase.register("Glm4MoeLiteForCausalLM")
class Glm4MoeLiteModel(DeepseekV2Model):
    model_arch = gguf.MODEL_ARCH.DEEPSEEK2

    def set_vocab(self):
        return self._set_vocab_glm()


@ModelBase.register("GlmMoeDsaForCausalLM")
class GlmMoeDsaModel(DeepseekV2Model):
    model_arch = gguf.MODEL_ARCH.GLM_DSA
    skip_mtp = False

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.block_count = self.hparams["num_hidden_layers"] + self.hparams.get("num_nextn_predict_layers", 0)
        self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)

    def set_vocab(self):
        return self._set_vocab_glm()

    def set_gguf_parameters(self):
        super().set_gguf_parameters()

        rope_dim = self.hparams["qk_rope_head_dim"]
        partial_rotary_factor = self.hparams.get("partial_rotary_factor", 1.0)
        self.gguf_writer.add_rope_dimension_count(int(rope_dim * partial_rotary_factor))

        # NextN/MTP prediction layers
        if (num_nextn_predict_layers := self.hparams.get("num_nextn_predict_layers")) is not None:
            self.gguf_writer.add_nextn_predict_layers(num_nextn_predict_layers)

        # DSA indexer parameters
        self.gguf_writer.add_indexer_head_count(self.hparams["index_n_heads"])
        self.gguf_writer.add_indexer_key_length(self.hparams["index_head_dim"])
        self.gguf_writer.add_indexer_top_k(self.hparams["index_topk"])


@ModelBase.register("GlmForCausalLM", "ChatGLMModel", "ChatGLMForConditionalGeneration")
class ChatGLMModel(TextModel):
    model_arch = gguf.MODEL_ARCH.CHATGLM

    def set_vocab_chatglm3(self):
        dir_model = self.dir_model
        hparams = self.hparams
        tokens: list[bytes] = []
        toktypes: list[int] = []
        scores: list[float] = []

        from transformers import AutoTokenizer
        tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
        vocab_size = hparams.get("padded_vocab_size", len(tokenizer.get_vocab()))  # ty: ignore[unresolved-attribute]
        assert max(tokenizer.get_vocab().values()) < vocab_size  # ty: ignore[unresolved-attribute]
        role_special_tokens = ["<|system|>", "<|user|>", "<|assistant|>", "<|observation|>"]
        special_tokens = ["[MASK]", "[gMASK]", "[sMASK]", "sop", "eop"] + role_special_tokens
        for token_id in range(vocab_size):
            piece = tokenizer._convert_id_to_token(token_id)  # ty: ignore[unresolved-attribute]
            if token_id == 0:
                piece = "<unk>"
            elif token_id == 1:
                piece = "<bos>"
            elif token_id == 2:
                piece = "<eos>"

            text = piece.encode("utf-8")  # ty: ignore[unresolved-attribute]
            score = 0.0
            # Referencing the tokenizer Python implementation(https://huggingface.co/THUDM/chatglm3-6b/blob/main/tokenization_chatglm.py),
            # it is only valid if it is less than tokenizer.tokenizer.sp_model.vocab_size()
            if len(piece) != 0 and token_id < tokenizer.tokenizer.sp_model.vocab_size():  # ty: ignore[unresolved-attribute, invalid-argument-type]
                score = tokenizer.tokenizer.sp_model.get_score(token_id)  # ty: ignore[unresolved-attribute]

            if token_id >= tokenizer.tokenizer.sp_model.vocab_size():  # ty: ignore[unresolved-attribute]
                if piece in special_tokens:
                    toktype = SentencePieceTokenTypes.CONTROL
                elif len(piece) == 0:  # ty: ignore[invalid-argument-type]
                    text = f"[PAD{token_id}]".encode("utf-8")
                    toktype = SentencePieceTokenTypes.UNUSED
                else:
                    toktype = SentencePieceTokenTypes.USER_DEFINED
                tokens.append(text)
                scores.append(score)
                toktypes.append(toktype)
                continue

            toktype = SentencePieceTokenTypes.NORMAL
            if tokenizer.tokenizer.sp_model.is_unknown(token_id):  # ty: ignore[unresolved-attribute]
                toktype = SentencePieceTokenTypes.UNKNOWN
            elif tokenizer.tokenizer.sp_model.is_control(token_id):  # ty: ignore[unresolved-attribute]
                toktype = SentencePieceTokenTypes.CONTROL
            elif tokenizer.tokenizer.sp_model.is_unused(token_id):  # ty: ignore[unresolved-attribute]
                toktype = SentencePieceTokenTypes.UNUSED
            elif tokenizer.tokenizer.sp_model.is_byte(token_id):  # ty: ignore[unresolved-attribute]
                toktype = SentencePieceTokenTypes.BYTE

            tokens.append(text)
            scores.append(score)
            toktypes.append(toktype)

        self.gguf_writer.add_tokenizer_model("llama")
        # glm3 needs prefix and suffix formatted as:
        # prompt = "[gMASK]sop<|user|>\n" + prompt + "<|assistant|>"
        self.gguf_writer.add_tokenizer_pre("chatglm-spm")
        self.gguf_writer.add_token_list(tokens)
        self.gguf_writer.add_token_scores(scores)
        self.gguf_writer.add_token_types(toktypes)

        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
        special_vocab.add_to_gguf(self.gguf_writer)

    @staticmethod
    def token_bytes_to_string(b):
        from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode  # ty: ignore[unresolved-import]
        byte_encoder = bytes_to_unicode()
        return ''.join([byte_encoder[ord(char)] for char in b.decode('latin-1')])

    @staticmethod
    def bpe(mergeable_ranks: dict[bytes, int], token: bytes, max_rank: int | None = None) -> list[bytes]:
        parts = [bytes([b]) for b in token]
        while True:
            min_idx = None
            min_rank = None
            for i, pair in enumerate(zip(parts[:-1], parts[1:])):
                rank = mergeable_ranks.get(pair[0] + pair[1])
                if rank is not None and (min_rank is None or rank < min_rank):
                    min_idx = i
                    min_rank = rank
            if min_rank is None or (max_rank is not None and min_rank >= max_rank):
                break
            assert min_idx is not None
            parts = parts[:min_idx] + [parts[min_idx] + parts[min_idx + 1]] + parts[min_idx + 2:]
        return parts

    def set_vocab(self):
        if "THUDM/chatglm3-6b" in self.hparams.get("_name_or_path", ""):
            self.set_vocab_chatglm3()
            return

        dir_model = self.dir_model
        hparams = self.hparams
        tokens: list[str] = []
        toktypes: list[int] = []

        from transformers import AutoTokenizer
        tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
        vocab_size = hparams.get("padded_vocab_size",hparams["vocab_size"])
        assert max(tokenizer.get_vocab().values()) < vocab_size  # ty: ignore[unresolved-attribute]

        tokens, toktypes, tokpre = self.get_vocab_base()
        self.gguf_writer.add_tokenizer_model("gpt2")
        self.gguf_writer.add_tokenizer_pre(tokpre)
        self.gguf_writer.add_token_list(tokens)
        self.gguf_writer.add_token_types(toktypes)
        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
        # only add special tokens when they were not already loaded from config.json
        special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
        special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"])  # ty: ignore[unresolved-attribute]
        # this one is usually not in config.json anyway
        special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
        special_vocab.add_to_gguf(self.gguf_writer)

    def set_gguf_parameters(self):
        n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
        assert n_embed is not None
        n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
        assert n_head is not None
        n_head_kv = self.hparams.get("multi_query_group_num", self.hparams.get("num_key_value_heads", n_head))
        self.gguf_writer.add_context_length(self.hparams.get("seq_length", n_embed))
        self.gguf_writer.add_embedding_length(n_embed)
        self.gguf_writer.add_feed_forward_length(self.hparams.get("ffn_hidden_size", self.hparams.get("intermediate_size", 4 * n_embed)))
        self.gguf_writer.add_block_count(self.block_count)
        self.gguf_writer.add_head_count(n_head)
        self.gguf_writer.add_head_count_kv(n_head_kv)
        self.gguf_writer.add_layer_norm_rms_eps(self.hparams.get("layernorm_epsilon",1e-5))
        self.gguf_writer.add_file_type(self.ftype)
        if "attention_dim" in self.hparams:
            rope_dim = self.hparams["attention_dim"]
        else:
            rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
        self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.hparams.get("partial_rotary_factor", 0.5)))
        self.gguf_writer.add_add_bos_token(False)
        rope_freq = 10000
        if "rope_ratio" in self.hparams:
            rope_freq = rope_freq * self.hparams["rope_ratio"]
        self.gguf_writer.add_rope_freq_base(rope_freq)

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        if name.endswith(".rotary_pos_emb.inv_freq"):
            return None

        name = name.removeprefix("transformer.")

        return super().filter_tensors((name, gen))


@ModelBase.register("NemotronForCausalLM")
class NemotronModel(TextModel):
    model_arch = gguf.MODEL_ARCH.NEMOTRON

    def set_vocab(self):
        self._set_vocab_sentencepiece()
        self.gguf_writer.add_pad_token_id(0)
        self.gguf_writer.add_unk_token_id(1)

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        hparams = self.hparams
        self.gguf_writer.add_vocab_size(hparams["vocab_size"])

        f_norm_eps = self.find_hparam(["layer_norm_eps", "layer_norm_epsilon", "norm_epsilon", "norm_eps"])
        self.gguf_writer.add_layer_norm_eps(f_norm_eps)

        # * Partial RoPE
        rot_pct = self.find_hparam(["partial_rotary_factor", "rope_pct", "rope_percent"])
        n_embd = self.find_hparam(["hidden_size", "n_embd"])
        n_head = self.find_hparam(["num_attention_heads", "n_head"])
        self.gguf_writer.add_rope_dimension_count(int(rot_pct * n_embd) // n_head)

        # * RopeScaling for Nemotron
        if "rope_scaling" not in self.hparams or self.hparams["rope_scaling"] is None:
            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
        else:
            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
            self.gguf_writer.add_rope_scaling_factor(self.hparams["factor"])

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        # * Adding +1 to LayerNorm's weights here to implement layernorm1p w/o changing anything on the GGML engine side
        #   model.layers.{l}.input_layernorm.weight
        #   model.layers.{l}.post_attention_layernorm.weight
        #   model.norm.weight
        if name.endswith("norm.weight"):
            data_torch = data_torch + 1

        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("ExaoneForCausalLM")
class ExaoneModel(TextModel):
    model_arch = gguf.MODEL_ARCH.EXAONE

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        hparams = self.hparams

        assert (hparams["activation_function"] == "silu")

        rotary_factor = self.find_hparam(["partial_rotary_factor", "rope_pct"], optional=True)
        rotary_factor = rotary_factor if rotary_factor is not None else 1.0
        self.gguf_writer.add_rope_dimension_count(int(rotary_factor * (hparams["hidden_size"] // hparams["num_attention_heads"])))

    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
        if rope_params := self.rope_parameters.get("full_attention", self.rope_parameters):
            if rope_params.get("rope_type", '').lower() == "llama3":
                base = self.rope_parameters.get("rope_theta", 10000.0)
                if (dim := self.hparams.get("head_dim")) is None:
                    dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
                freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))

                factor = rope_params.get("factor", 8.0)
                low_freq_factor = rope_params.get("low_freq_factor", 1.0)
                high_freq_factor = rope_params.get("high_freq_factor", 4.0)
                old_context_len = self.hparams.get("original_max_position_embeddings", 8192)

                low_freq_wavelen = old_context_len / low_freq_factor
                high_freq_wavelen = old_context_len / high_freq_factor
                assert low_freq_wavelen != high_freq_wavelen

                rope_factors = []
                for freq in freqs:
                    wavelen = 2 * math.pi / freq
                    if wavelen < high_freq_wavelen:
                        rope_factors.append(1)
                    elif wavelen > low_freq_wavelen:
                        rope_factors.append(factor)
                    else:
                        smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
                        rope_factors.append(1 / ((1 - smooth) / factor + smooth))

                yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32))


@ModelBase.register("Exaone4ForCausalLM")
class Exaone4Model(TextModel):
    model_arch = gguf.MODEL_ARCH.EXAONE4

    def set_vocab(self):
        tokens, toktypes, tokpre = self.get_vocab_base()
        self.gguf_writer.add_tokenizer_model("gpt2")
        self.gguf_writer.add_tokenizer_pre(tokpre)
        self.gguf_writer.add_token_list(tokens)
        self.gguf_writer.add_token_types(toktypes)

        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
        special_vocab.add_to_gguf(self.gguf_writer)

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        hparams = self.hparams
        self.gguf_writer.add_vocab_size(hparams["vocab_size"])

        if hparams.get("sliding_window") is not None:
            self.gguf_writer.add_sliding_window(hparams["sliding_window"])
            if "layer_types" in hparams:
                self.gguf_writer.add_sliding_window_pattern([t == "sliding_attention" for t in hparams["layer_types"]])
            elif "sliding_window_pattern" in hparams:
                sliding_window_pattern = []
                if isinstance(hparams["sliding_window_pattern"], str):  # e.g. LLLG
                    for i in range(hparams["num_hidden_layers"]):
                        sliding_window_pattern.append(hparams["sliding_window_pattern"][i % len(hparams["sliding_window_pattern"])] == "L")
                if isinstance(hparams["sliding_window_pattern"], int):  # e.g. 4
                    for i in range(hparams["num_hidden_layers"]):
                        sliding_window_pattern.append((i + 1) % hparams["sliding_window_pattern"] != 0)
                if len(sliding_window_pattern) == hparams["num_hidden_layers"]:
                    self.gguf_writer.add_sliding_window_pattern(sliding_window_pattern)

    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
        if rope_params := self.rope_parameters.get("full_attention", self.rope_parameters):
            if rope_params.get("rope_type", '').lower() == "llama3":
                base = rope_params.get("rope_theta", 10_000.0)
                if (dim := self.hparams.get("head_dim")) is None:
                    dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
                freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))

                factor = rope_params.get("factor", 16.0)
                low_freq_factor = rope_params.get("low_freq_factor", 1.0)
                high_freq_factor = rope_params.get("high_freq_factor", 4.0)
                old_context_len = self.hparams.get("original_max_position_embeddings", 8192)

                low_freq_wavelen = old_context_len / low_freq_factor
                high_freq_wavelen = old_context_len / high_freq_factor

                rope_factors = []
                for freq in freqs:
                    wavelen = 2 * math.pi / freq
                    if wavelen < high_freq_wavelen:
                        rope_factors.append(1)
                    elif wavelen > low_freq_wavelen:
                        rope_factors.append(factor)
                    else:
                        smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
                        rope_factors.append(1 / ((1 - smooth) / factor + smooth))

                yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32))


@ModelBase.register("ExaoneMoEForCausalLM")
class ExaoneMoEModel(Exaone4Model):
    model_arch = gguf.MODEL_ARCH.EXAONE_MOE

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.block_count = self.hparams["num_hidden_layers"] + self.hparams.get("num_nextn_predict_layers", 0)
        self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        moe_intermediate_size = self.hparams["moe_intermediate_size"]
        num_shared_experts = self.hparams["num_shared_experts"]
        self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
        self.gguf_writer.add_expert_shared_count(num_shared_experts)
        self.gguf_writer.add_expert_shared_feed_forward_length(moe_intermediate_size * num_shared_experts)
        self.gguf_writer.add_expert_weights_scale(self.hparams["routed_scaling_factor"])
        self.gguf_writer.add_expert_weights_norm(self.hparams["norm_topk_prob"])
        n_dense_layer = self.hparams.get("first_k_dense_replace", self.hparams.get("first_last_k_dense_replace", 0))
        self.gguf_writer.add_leading_dense_block_count(n_dense_layer)
        self.gguf_writer.add_nextn_predict_layers(self.hparams.get("num_nextn_predict_layers", 0))

        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)

    _experts: list[dict[str, Tensor]] | None = None

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        if name.startswith("mtp."):
            if name.find("layers.") != -1:
                # `mtp.layers.0.[module_name]` format
                name = name.replace(f"mtp.layers.{bid}", f"model.layers.{bid + self.hparams['num_hidden_layers']}")
            else:
                # mtp fc/norm weights
                remapper = {
                    "mtp.fc": "model.layers.{bid}.eh_proj",
                    "mtp.pre_fc_norm_embedding": "model.layers.{bid}.enorm",
                    "mtp.pre_fc_norm_hidden": "model.layers.{bid}.hnorm",
                    "mtp.norm": "model.layers.{bid}.shared_head.norm",
                }
                _n = Path(name)
                new_name = remapper[_n.stem] + _n.suffix

                # set shared weights for all NextN/MTP layers
                for bid in range(self.hparams['num_hidden_layers'], self.block_count):
                    yield from super().modify_tensors(data_torch, new_name.format(bid=bid), bid)
                return

        if name.find("mlp.experts") != -1:
            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
            assert bid is not None

            if self._experts is None:
                self._experts = [{} for _ in range(self.block_count)]

            self._experts[bid][name] = data_torch

            if len(self._experts[bid]) >= n_experts * 3:
                # merge the experts into a single 3d tensor
                for w_name in ["down_proj", "gate_proj", "up_proj"]:
                    datas: list[Tensor] = []

                    for xid in range(n_experts):
                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
                        datas.append(self._experts[bid][ename])
                        del self._experts[bid][ename]

                    data_torch = torch.stack(datas, dim=0)

                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"

                    new_name = self.map_tensor_name(merged_name)

                    yield from super().modify_tensors(data_torch, new_name, bid)
                return
            else:
                return

        yield from super().modify_tensors(data_torch, name, bid)

    def prepare_tensors(self):
        super().prepare_tensors()
        if self._experts is not None:
            # flatten `list[dict[str, Tensor]]` into `list[str]`
            experts = [k for d in self._experts for k in d.keys()]
            if len(experts) > 0:
                raise ValueError(f"Unprocessed experts: {experts}")


@ModelBase.register("GraniteForCausalLM", "GraniteSpeechForConditionalGeneration")
class GraniteModel(LlamaModel):
    """Conversion for IBM's GraniteForCausalLM"""
    model_arch = gguf.MODEL_ARCH.GRANITE

    def set_gguf_parameters(self):
        """Granite uses standard llama parameters with the following differences:

        - No head_dim support
        - New multiplier params:
            - attention_scale
            - embedding_scale
            - residual_scale
        - logits_scaling
        """
        if head_dim := self.hparams.pop("head_dim", None):
            logger.warning("Ignoring head_dim (%s) from config for Granite", head_dim)
        super().set_gguf_parameters()
        # NOTE: Convert _multiplier params to _scale params for naming
        #   consistency
        if attention_scale := self.hparams.get("attention_multiplier"):
            self.gguf_writer.add_attention_scale(attention_scale)
            logger.info("gguf: (granite) attention_scale = %s", attention_scale)
        if embedding_scale := self.hparams.get("embedding_multiplier"):
            self.gguf_writer.add_embedding_scale(embedding_scale)
            logger.info("gguf: (granite) embedding_scale = %s", embedding_scale)
        if residual_scale := self.hparams.get("residual_multiplier"):
            self.gguf_writer.add_residual_scale(residual_scale)
            logger.info("gguf: (granite) residual_scale = %s", residual_scale)
        if logits_scale := self.hparams.get("logits_scaling"):
            self.gguf_writer.add_logit_scale(logits_scale)
            logger.info("gguf: (granite) logits_scale = %s", logits_scale)

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item
        if name.startswith("encoder."):
            return None
        return super().filter_tensors(item)


@ModelBase.register("GraniteMoeForCausalLM", "GraniteMoeSharedForCausalLM")
class GraniteMoeModel(GraniteModel):
    """Conversion for IBM's GraniteMoeForCausalLM"""
    model_arch = gguf.MODEL_ARCH.GRANITE_MOE

    def set_gguf_parameters(self):
        """GraniteMoeShared uses GraniteMoe parameters plus the following:
        - shared_intermediate_size
        """
        super().set_gguf_parameters()
        if shared_feed_forward_length := self.hparams.get("shared_intermediate_size"):
            self.gguf_writer.add_expert_shared_feed_forward_length(shared_feed_forward_length)
            logger.info("gguf: (granitemoeshared) shared_feed_forward_length = %s", shared_feed_forward_length)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        """In modeling_granitemoe, the JetMoe implementation of parallel experts
        is used. This essentially merges w1 and w3 into a single tensor with 2x
        the hidden size that is then split during forward. To keep compatibility
        with existing mixtral support, we pull them apart here.
        """

        if name.endswith("block_sparse_moe.input_linear.weight"):
            ffn_dim = self.hparams["intermediate_size"]
            assert data_torch.shape[-2] == 2 * ffn_dim, "Merged FFN tensor size must be 2 * intermediate_size"
            gate, up = data_torch.split(ffn_dim, dim=-2)
            yield from ModelBase.modify_tensors(self, gate, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE_EXP, bid), bid)
            yield from ModelBase.modify_tensors(self, up, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP_EXP, bid), bid)
            return

        has_experts = bool(self.hparams.get('num_local_experts'))

        if name.endswith("shared_mlp.input_linear.weight"):
            ffn_dim = self.hparams["shared_intermediate_size"]
            assert data_torch.shape[-2] == 2 * ffn_dim, "Merged FFN tensor size must be 2 * shared_intermediate_size"
            gate, up = data_torch.split(ffn_dim, dim=-2)
            if has_experts:
                yield from ModelBase.modify_tensors(self, gate,self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE_SHEXP, bid), bid)
                yield from ModelBase.modify_tensors(self, up, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP_SHEXP, bid), bid)
                return
            yield from ModelBase.modify_tensors(self, gate, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE, bid), bid)
            yield from ModelBase.modify_tensors(self, up, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP, bid), bid)
            return

        if not has_experts and name.endswith("shared_mlp.output_linear.weight"):
            yield from ModelBase.modify_tensors(self, data_torch, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_DOWN, bid), bid)
            return

        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("GraniteMoeHybridForCausalLM", "BambaForCausalLM")
class GraniteHybridModel(Mamba2Model, GraniteMoeModel):
    """GraniteHybrid is a hybrid SSM + Attention model that uses Mamba2 SSM
    layers and optionally uses MoE w/ a shared expert"""
    model_arch = gguf.MODEL_ARCH.GRANITE_HYBRID
    undo_permute = True

    def __init__(self, *args, **kwargs):

        # Hybrid mamba models use a prefix for the mamba-specific params.
        # TODO: Extend this if the prefix(es) need to be configurable
        self.hparam_prefixes = ["mamba"]

        super().__init__(*args, **kwargs)

        # Lists of which layers use ssm vs attention
        self._attn_layers = self.get_attn_layers()
        self._ssm_layers = [
            i for i in range(self.block_count)
            if i not in self._attn_layers
        ]

        # There are some models in this family that are non-hybrid, but keep the
        # same parent class by setting all layers to "attention." If this is the
        # case, the model architecture needs to be updated to a standard
        # "granite" or "granitemoe" model
        if not self._ssm_layers:
            has_experts = self.find_hparam(["num_experts_per_tok", "num_experts_per_token"], optional=True)
            new_arch = (
                gguf.MODEL_ARCH.GRANITE_MOE
                if has_experts else
                gguf.MODEL_ARCH.GRANITE
            )
            self.model_arch = new_arch
            self.gguf_writer.arch = gguf.MODEL_ARCH_NAMES[new_arch]
            self.gguf_writer.add_architecture()

        # n_group and d_inner are used during reshape_tensors for mamba2
        # NOTE: Explicitly include hparam prefix prefix for d_model to
        #   disambiguate with top-level head_dim
        # NOTE 2: If needed for future models, this can be isolated in a method
        #   to separate the prefix setting and the keys used
        self.d_model = self.find_hparam([f"{self.hparam_prefixes[0]}_head_dim", "hidden_size", "d_model"])
        self.n_group = self.find_hparam(["n_groups", "num_groups"])
        self.d_inner = self.find_hparam(["expand", "num_heads"]) * self.d_model

    def get_attn_layers(self):
        # Explicit list of layer type names
        if layer_types := self.hparams.get("layer_types"):
            return [
                i for i, typ in enumerate(layer_types)
                if typ == "attention"
            ]

        # Layer types indicated by index or period
        attn_layers = self.hparams.get("attn_layer_indices", [])
        if not attn_layers:
            attn_period = self.hparams.get("attn_layer_period")
            assert attn_period, "Didn't find attn_layer_indices or attn_layer_period"
            attn_offset = self.hparams.get("attn_layer_offset")
            assert attn_offset is not None, "No attention layer offset set with attn_layer_period"
            attn_layers = [
                i for i in range(self.block_count)
                if i % attn_period == attn_offset
            ]
        return attn_layers

    def find_hparam(self, keys: Iterable[str], *args, **kwargs) -> Any:
        prefixed = []
        for pfx in self.hparam_prefixes:
            prefixed.extend(
                "_".join([pfx, k])
                for k in keys
            )
        keys = list(keys) + prefixed
        return Mamba2Model.find_hparam(self, keys, *args, **kwargs)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        if (
            name.endswith("block_sparse_moe.input_linear.weight")
            or "shared_mlp" in name
        ):
            yield from GraniteMoeModel.modify_tensors(self, data_torch, name, bid)
            return

        # Determine whether this is a mamba layer or an attention layer
        if bid in self._ssm_layers:
            yield from Mamba2Model.modify_tensors(self, data_torch, name, bid)
            return
        elif bid in self._attn_layers:
            yield from GraniteMoeModel.modify_tensors(self, data_torch, name, bid)
            return
        yield from ModelBase.modify_tensors(self, data_torch, name, bid)

    def set_gguf_parameters(self):
        """This method merges params from both parents and some that are
        specific to this model. The result is some duplication of how the params
        get set. The following warnings are expected during conversion:

        WARNING:Duplicated key name 'granitehybrid.attention.head_count_kv'
        WARNING:Duplicated key name 'granitehybrid.context_length'
        """
        GraniteMoeModel.set_gguf_parameters(self)

        ## Mamba mixer params ##
        self.gguf_writer.add_ssm_conv_kernel(self.find_hparam(["conv_kernel", "d_conv"]))
        self.gguf_writer.add_ssm_state_size(self.find_hparam(["state_size", "d_state", "state_dim", "ssm_state_size"]))
        self.gguf_writer.add_ssm_group_count(self.n_group)
        self.gguf_writer.add_ssm_inner_size(self.d_inner)
        # NOTE: The mamba_dt_rank is _not_ the right field for how this is used
        #   in llama.cpp
        self.gguf_writer.add_ssm_time_step_rank(self.find_hparam(["n_heads", "num_heads"]))

        ## Attention params ##
        head_count_kv = self.find_hparam(["num_key_value_heads", "n_head_kv"])
        head_count_kv_vec = [
            head_count_kv if i in self._attn_layers else 0 for i in range(self.block_count)
        ]
        if rope_dim := self.hparams.get("attn_rotary_emb"):
            self.gguf_writer.add_rope_dimension_count(rope_dim)
        self.gguf_writer.add_head_count_kv(head_count_kv_vec)

        ## If Bamba or non-hybrid, use rope, otherwise don't
        use_rope = (
            "BambaForCausalLM" in self.hparams["architectures"]
            or not self._ssm_layers
        )
        self.gguf_writer.add_rope_scaling_finetuned(use_rope)
        if not use_rope:
            self.gguf_writer.add_context_length(2**20)

        ## Validation ##
        d_head = self.find_hparam(["d_head"], optional=True) or 64
        assert self.hparams.get("hidden_act") in [None, "silu"], "Only SILU activation supported"
        assert self.d_inner % d_head == 0, f"SSM inner size {self.d_inner} not a multiple of head dim {d_head}"

    def set_vocab(self):
        self.hparams["pad_vocab_size_multiple"] = 8
        Mamba2Model.set_vocab(self)


@ModelBase.register("NemotronHForCausalLM")
class NemotronHModel(GraniteHybridModel):
    """Hybrid mamba2/attention model from NVIDIA"""
    model_arch = gguf.MODEL_ARCH.NEMOTRON_H
    is_moe: bool = False

    def __init__(self, *args, **kwargs):
        # We have to determine the correct model architecture (MoE vs non-MoE) before
        # calling the parent __init__. This is because the parent constructor
        # uses self.model_arch to build the tensor name map, and all MoE-specific
        # mappings would be missed if it were called with the default non-MoE arch.
        hparams = ModelBase.load_hparams(args[0], self.is_mistral_format)
        has_moe_params = (
            "num_experts_per_tok" in hparams
            or (isinstance(hparams.get("llm_config"), dict) and "num_experts_per_tok" in hparams["llm_config"])
        )
        if has_moe_params:
            self.model_arch = gguf.MODEL_ARCH.NEMOTRON_H_MOE
            self.is_moe = True

        super().__init__(*args, **kwargs)

        # Save the top-level head_dim for later
        self.head_dim = self.hparams.get("head_dim", self.hparams.get("attention_head_dim"))
        assert self.head_dim is not None, "Could not find the attention head dim in config"

        # Don't use expand to calculate d_inner
        self.d_inner = self.find_hparam(["num_heads"]) * self.d_model

        # Update the ssm / attn / mlp layers
        # M: Mamba2, *: Attention, -: MLP
        # MoE:
        # M: Mamba2, *: Attention, E: Expert
        pattern = self.hparams.get("hybrid_override_pattern") or self.hparams.get("layers_block_type")
        if pattern is None:
            self._ssm_layers = []
            self._mlp_layers = []
        elif isinstance(pattern, str):
            self._ssm_layers = [i for i, val in enumerate(pattern) if val == "M"]
            self._mlp_layers = [i for i, val in enumerate(pattern) if val == ("E" if self.is_moe else "-")]
        else:
            self._ssm_layers = [i for i, val in enumerate(pattern) if val == "mamba"]
            self._mlp_layers = [i for i, val in enumerate(pattern) if val == "moe"]

    def get_attn_layers(self):
        pattern = self.hparams.get("hybrid_override_pattern") or self.hparams.get("layers_block_type")
        if pattern is None:
            return []
        assert len(pattern) == self.block_count, f"Mismatch between pattern ({len(pattern)}) and block_count ({self.block_count})!"
        if isinstance(pattern, str):
            return [i for i, val in enumerate(pattern) if val == "*"]

        return [i for i, val in enumerate(pattern) if val == "attention"]

    def set_gguf_parameters(self):
        super().set_gguf_parameters()

        head_dim = self.head_dim
        if head_dim is None:
            raise ValueError("Could not find the attention head dim in config")
        self.gguf_writer.add_key_length(head_dim)
        self.gguf_writer.add_value_length(head_dim)

        # Set feed_forward_length
        # NOTE: This will trigger an override warning. This is preferable to
        #   duplicating all the parent logic
        if not self.is_moe:
            n_ff = self.find_hparam(["intermediate_size", "n_inner", "hidden_dim"])
            self.gguf_writer.add_feed_forward_length([
                n_ff if i in self._mlp_layers else 0 for i in range(self.block_count)
            ])
        else:
            moe_intermediate_size = self.hparams["moe_intermediate_size"]
            self.gguf_writer.add_feed_forward_length([
                moe_intermediate_size if i in self._mlp_layers else 0 for i in range(self.block_count)
            ])
            self.gguf_writer.add_expert_used_count(self.hparams["num_experts_per_tok"])
            self.gguf_writer.add_expert_feed_forward_length(self.hparams["moe_intermediate_size"])
            self.gguf_writer.add_expert_shared_feed_forward_length(self.hparams["moe_shared_expert_intermediate_size"])
            self.gguf_writer.add_expert_count(self.hparams["n_routed_experts"])
            self.gguf_writer.add_expert_shared_count(self.hparams["n_shared_experts"])
            self.gguf_writer.add_expert_weights_norm(self.hparams["norm_topk_prob"])
            self.gguf_writer.add_expert_weights_scale(self.hparams["routed_scaling_factor"])
            self.gguf_writer.add_expert_group_count(self.hparams["n_group"])

            # number of experts used per token (top-k)
            if (n_experts_used := self.hparams.get("num_experts_per_tok")) is not None:
                self.gguf_writer.add_expert_used_count(n_experts_used)

            if (latent_size := self.hparams.get("moe_latent_size")) is not None:
                self.gguf_writer.add_moe_latent_size(latent_size)

    def set_vocab(self):
        # The NemotronH config uses pattern characters (e.g. '-') that may not
        # be supported by the installed transformers version. AutoTokenizer
        # internally calls AutoConfig which triggers this parsing failure.
        # Using trust_remote_code=True to load the model's own config class.
        tokens: list[str] = []
        toktypes: list[int] = []

        from transformers import AutoTokenizer
        tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)

        # Pad vocab size (from Mamba2Model/GraniteHybridModel)
        self.hparams["pad_vocab_size_multiple"] = 8 # Setting this here since GraniteHybridModel.set_vocab() isn't being invoked now.
        # From Mamba2Model.set_vocab():
        vocab_size = self.hparams["vocab_size"]
        pad_vocab = self.hparams.get("pad_vocab_size_multiple", 16)
        # ref: https://stackoverflow.com/a/17511341/22827863
        vocab_size = -(vocab_size // -pad_vocab) * pad_vocab
        self.hparams["vocab_size"] = vocab_size

        assert max(tokenizer.vocab.values()) < vocab_size  # ty: ignore[unresolved-attribute]

        tokpre = self.get_vocab_base_pre(tokenizer)

        reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()}  # ty: ignore[unresolved-attribute]
        added_vocab = tokenizer.get_added_vocab()  # ty: ignore[unresolved-attribute]

        added_tokens_decoder = tokenizer.added_tokens_decoder  # ty: ignore[unresolved-attribute]

        for i in range(vocab_size):
            if i not in reverse_vocab:
                tokens.append(f"[PAD{i}]")
                toktypes.append(gguf.TokenType.UNUSED)
            else:
                token: str = reverse_vocab[i]
                if token in added_vocab:
                    if not added_tokens_decoder[i].normalized:
                        previous_token = token
                        token = tokenizer.decode(tokenizer.encode(token, add_special_tokens=False))  # ty: ignore[unresolved-attribute, invalid-assignment]
                        if previous_token != token:
                            logger.info(f"{repr(previous_token)} is encoded and decoded back to {repr(token)} using AutoTokenizer")

                    if added_tokens_decoder[i].special or self.does_token_look_special(token):
                        toktypes.append(gguf.TokenType.CONTROL)
                    else:
                        token = token.replace(b"\xe2\x96\x81".decode("utf-8"), " ")  # pre-normalize user-defined spaces
                        toktypes.append(gguf.TokenType.USER_DEFINED)
                else:
                    toktypes.append(gguf.TokenType.NORMAL)
                tokens.append(token)

        # From TextModel.set_vocab_gpt2():
        self.gguf_writer.add_tokenizer_model("gpt2")
        self.gguf_writer.add_tokenizer_pre(tokpre)
        self.gguf_writer.add_token_list(tokens)
        self.gguf_writer.add_token_types(toktypes)

        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
        special_vocab.add_to_gguf(self.gguf_writer)

        # The tokenizer _does_ add a BOS token (via post_processor type
        # TemplateProcessing) but does not set add_bos_token to true in the
        # config, so we need to explicitly override it here.
        if not self.is_moe:
            self.gguf_writer.add_add_bos_token(True)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        if self.is_moe and bid is not None:
            # Skip Multi-Token Prediction (MTP) tensors. These are used for
            # for speculative decoding but we don't include them in this model
            # conversion. See https://github.com/ggml-org/llama.cpp/pull/18886
            if name.startswith("mtp."):
                logger.info(f"gguf: Skipping MTP (Speculative) layer: {name}")
                return

            if name.endswith("mixer.gate.e_score_correction.bias"):
                yield from ModelBase.modify_tensors(self, data_torch, name, bid)
                return

            if name.endswith("mixer.dt_bias"):
                new_name = name.replace("dt_bias", "dt.bias")
                yield from ModelBase.modify_tensors(self, data_torch, new_name, bid)
                return

            if name.endswith("mixer.conv1d.weight"):
                squeezed_data = data_torch.squeeze()
                yield from ModelBase.modify_tensors(self, squeezed_data, name, bid)
                return

            if name.endswith("mixer.A_log"):
                transformed_data = -torch.exp(data_torch)
                reshaped_data = transformed_data.squeeze().reshape(-1, 1)
                yield from ModelBase.modify_tensors(self, reshaped_data, name, bid)
                return

            if name.endswith("mixer.D"):
                reshaped_data = data_torch.squeeze().reshape(-1, 1)
                yield from ModelBase.modify_tensors(self, reshaped_data, name, bid)
                return

            if name.endswith("mixer.norm.weight"):
                reshaped_data = data_torch.reshape(self.n_group, -1)
                yield from ModelBase.modify_tensors(self, reshaped_data, name, bid)
                return

            if name.find("mixer.experts") != -1:
                n_experts = self.hparams["n_routed_experts"]
                assert bid is not None

                if self._experts is None:
                    self._experts = [{} for _ in range(self.block_count)]

                self._experts[bid][name] = data_torch

                if len(self._experts[bid]) >= n_experts * 2:
                    # merge the experts into a single tensor
                    for w_name in ["down_proj", "up_proj"]:
                        datas: list[Tensor] = []

                        for xid in range(n_experts):
                            ename = f"backbone.layers.{bid}.mixer.experts.{xid}.{w_name}.weight"
                            datas.append(self._experts[bid][ename])
                            del self._experts[bid][ename]

                        data_torch = torch.stack(datas, dim=0)
                        merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"

                        yield from ModelBase.modify_tensors(self, data_torch, merged_name, bid)
                    return
                else:
                    return

        yield from super().modify_tensors(data_torch, name, bid)

    def prepare_tensors(self):
        super().prepare_tensors()

        if self._experts is not None:
            # flatten `list[dict[str, Tensor]]` into `list[str]`
            experts = [k for d in self._experts for k in d.keys()]
            if len(experts) > 0:
                raise ValueError(f"Unprocessed experts: {experts}")


@ModelBase.register("LlamaBidirectionalModel")
class LlamaEmbedNemotronModel(LlamaModel):
    model_arch = gguf.MODEL_ARCH.LLAMA_EMBED


@ModelBase.register("BailingMoeForCausalLM")
class BailingMoeModel(TextModel):
    model_arch = gguf.MODEL_ARCH.BAILINGMOE

    def set_vocab(self):
        self._set_vocab_gpt2()

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        hparams = self.hparams
        if (rope_dim := hparams.get("head_dim")) is None:
            rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]

        self.gguf_writer.add_rope_dimension_count(rope_dim)
        self.gguf_writer.add_leading_dense_block_count(hparams["first_k_dense_replace"])
        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
        self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"])
        self.gguf_writer.add_expert_weights_scale(1.0)
        self.gguf_writer.add_expert_shared_count(hparams["num_shared_experts"])
        self.gguf_writer.add_expert_weights_norm(hparams["norm_topk_prob"])

    _experts: list[dict[str, Tensor]] | None = None

    @staticmethod
    def permute(weights: Tensor, n_head: int, n_head_kv: int | None):
        if n_head_kv is not None and n_head != n_head_kv:
            n_head = n_head_kv
        return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
                .swapaxes(1, 2)
                .reshape(weights.shape))

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        n_head = self.hparams["num_attention_heads"]
        n_kv_head = self.hparams.get("num_key_value_heads")
        n_embd = self.hparams["hidden_size"]
        if (head_dim := self.hparams.get("head_dim")) is None:
            head_dim = n_embd // n_head

        output_name = self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT)

        if name.endswith("attention.dense.weight"):
            yield from super().modify_tensors(data_torch, self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_OUT, bid), bid)
            return
        elif name.endswith("query_key_value.weight"):
            q, k, v = data_torch.split([n_head * head_dim, n_kv_head * head_dim, n_kv_head * head_dim], dim=-2)

            yield from super().modify_tensors(BailingMoeModel.permute(q, n_head, n_head), self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_Q, bid), bid)
            yield from super().modify_tensors(BailingMoeModel.permute(k, n_head, n_kv_head), self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_K, bid), bid)
            yield from super().modify_tensors(v,self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_V, bid), bid)
            return
        elif name.find("mlp.experts") != -1:
            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
            assert bid is not None

            if self._experts is None:
                self._experts = [{} for _ in range(self.block_count)]

            self._experts[bid][name] = data_torch

            if len(self._experts[bid]) >= n_experts * 3:
                # merge the experts into a single 3d tensor
                for w_name in ["down_proj", "gate_proj", "up_proj"]:
                    datas: list[Tensor] = []

                    for xid in range(n_experts):
                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
                        datas.append(self._experts[bid][ename])
                        del self._experts[bid][ename]

                    data_torch = torch.stack(datas, dim=0)

                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"

                    new_name = self.map_tensor_name(merged_name)

                    yield from super().modify_tensors(data_torch, new_name, bid)

            return

        new_name = self.map_tensor_name(name)

        if new_name == output_name and self.hparams.get("norm_head"):
            data_torch = data_torch.float()
            data_torch /= torch.norm(data_torch, p=2, dim=0, keepdim=True) + 1e-7

        yield from super().modify_tensors(data_torch, new_name, bid)

    def prepare_tensors(self):
        super().prepare_tensors()

        if self._experts is not None:
            # flatten `list[dict[str, Tensor]]` into `list[str]`
            experts = [k for d in self._experts for k in d.keys()]
            if len(experts) > 0:
                raise ValueError(f"Unprocessed experts: {experts}")


@ModelBase.register("BailingMoeV2ForCausalLM")
class BailingMoeV2Model(TextModel):
    model_arch = gguf.MODEL_ARCH.BAILINGMOE2

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        if nextn_layers := self.hparams.get("num_nextn_predict_layers", 0):
            self.block_count = self.hparams["num_hidden_layers"] + nextn_layers
            self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)

    def set_vocab(self):
        self._set_vocab_gpt2()

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        hparams = self.hparams
        if (rope_dim := hparams.get("head_dim")) is None:
            rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]

        self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.hparams.get("partial_rotary_factor", 0.5)))
        self.gguf_writer.add_leading_dense_block_count(hparams["first_k_dense_replace"])
        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
        self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"])
        self.gguf_writer.add_expert_shared_feed_forward_length(hparams.get("moe_shared_expert_intermediate_size", hparams["moe_intermediate_size"] * hparams["num_shared_experts"]))
        self.gguf_writer.add_expert_weights_scale(hparams["routed_scaling_factor"])
        self.gguf_writer.add_expert_shared_count(hparams["num_shared_experts"])
        self.gguf_writer.add_expert_weights_norm(hparams["norm_topk_prob"])

        if (nextn_layers := self.hparams.get("num_nextn_predict_layers")) is not None:
            self.gguf_writer.add_nextn_predict_layers(nextn_layers)

    _experts: list[dict[str, Tensor]] | None = None

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        if name.endswith(".expert_bias"):
            name = name.replace(".expert_bias", ".expert_bias.bias")

        return super().filter_tensors((name, gen))

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        if "mlp.experts" in name:
            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
            assert bid is not None

            if self._experts is None:
                self._experts = [{} for _ in range(self.block_count)]

            self._experts[bid][name] = data_torch

            if len(self._experts[bid]) >= n_experts * 3:
                # merge the experts into a single 3d tensor
                for w_name in ["down_proj", "gate_proj", "up_proj"]:
                    datas: list[Tensor] = []

                    for xid in range(n_experts):
                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
                        datas.append(self._experts[bid][ename])
                        del self._experts[bid][ename]

                    data_torch = torch.stack(datas, dim=0)

                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"

                    yield from super().modify_tensors(data_torch, merged_name, bid)
            return

        yield from super().modify_tensors(data_torch, name, bid)

    def prepare_tensors(self):
        super().prepare_tensors()

        if self._experts is not None:
            # flatten `list[dict[str, Tensor]]` into `list[str]`
            experts = [k for d in self._experts for k in d.keys()]
            if len(experts) > 0:
                raise ValueError(f"Unprocessed experts: {experts}")


@ModelBase.register("SarvamMoEForCausalLM", "modeling_sarvam_moe.SarvamMoEForCausalLM")
class SarvamMoEModel(BailingMoeV2Model):
    model_arch = gguf.MODEL_ARCH.BAILINGMOE2
    # Sarvam-MoE shares the BailingMoeV2 architecture; only differences:
    #  - full rotary (no partial_rotary_factor)
    #  - expert bias is zero-mean normalized at load time

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        hparams = self.hparams
        if (rope_dim := hparams.get("head_dim")) is None:
            rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
        # Override the partial-rotary value written by BailingMoeV2 with the full rotary dim
        self.gguf_writer.add_rope_dimension_count(rope_dim)

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item
        if name.endswith(".expert_bias"):
            # Sarvam normalizes expert bias to zero mean
            inner = gen

            def gen():
                t = inner()
                return t - t.mean()
        return super().filter_tensors((name, gen))


@ModelBase.register("GroveMoeForCausalLM", "modeling_grove_moe.GroveMoeForCausalLM")
class GroveMoeModel(TextModel):
    model_arch = gguf.MODEL_ARCH.GROVEMOE

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None:
            self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
            logger.info(f"gguf: expert feed forward length = {moe_intermediate_size}")
        # FIXME?: Hardcoded https://huggingface.co/inclusionAI/GroveMoE-Inst/blob/c4c69e5970d18907b5e6ddccdfd55176fe292df1/modeling_grove_moe.py#L299
        self.gguf_writer.add_expert_chunk_feed_forward_length(self.hparams.get("head_dim") or 128)
        # FIXME?: Hardcoded https://huggingface.co/inclusionAI/GroveMoE-Inst/blob/c4c69e5970d18907b5e6ddccdfd55176fe292df1/modeling_grove_moe.py#L298
        self.gguf_writer.add_experts_per_group(2)
        # FIXME?: Hardcoded https://huggingface.co/inclusionAI/GroveMoE-Inst/blob/c4c69e5970d18907b5e6ddccdfd55176fe292df1/modeling_grove_moe.py#L376
        self.gguf_writer.add_expert_group_scale(0.05)

    _experts: list[dict[str, Tensor]] | None = None
    _chunk_experts: list[dict[str, Tensor]] | None = None

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        if name.endswith(".expert_bias"):
            # FIXME?: Unused https://huggingface.co/inclusionAI/GroveMoE-Inst/blob/c4c69e5970d18907b5e6ddccdfd55176fe292df1/modeling_grove_moe.py#L303
            return

        # process the experts separately
        if name.find("chunk_experts") != -1:
            n_experts = self.find_hparam(["num_local_experts", "num_experts"]) // 2 # see add_experts_per_group
            assert bid is not None

            if self._chunk_experts is None:
                self._chunk_experts = [{} for _ in range(self.block_count)]

            self._chunk_experts[bid][name] = data_torch

            if len(self._chunk_experts[bid]) >= n_experts * 3:
                # merge the experts into a single 3d tensor
                for w_name in ["down_proj", "gate_proj", "up_proj"]:
                    datas: list[Tensor] = []

                    for xid in range(n_experts):
                        ename = f"model.layers.{bid}.mlp.chunk_experts.{xid}.{w_name}.weight"
                        datas.append(self._chunk_experts[bid][ename])
                        del self._chunk_experts[bid][ename]

                    data_torch = torch.stack(datas, dim=0)

                    merged_name = f"model.layers.{bid}.mlp.chunk_experts.{w_name}.weight"

                    yield from super().modify_tensors(data_torch, merged_name, bid)
                return
            else:
                return
        elif name.find("experts") != -1:
            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
            assert bid is not None

            if self._experts is None:
                self._experts = [{} for _ in range(self.block_count)]

            self._experts[bid][name] = data_torch

            if len(self._experts[bid]) >= n_experts * 3:
                # merge the experts into a single 3d tensor
                for w_name in ["down_proj", "gate_proj", "up_proj"]:
                    datas: list[Tensor] = []

                    for xid in range(n_experts):
                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
                        datas.append(self._experts[bid][ename])
                        del self._experts[bid][ename]

                    data_torch = torch.stack(datas, dim=0)

                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"

                    yield from super().modify_tensors(data_torch, merged_name, bid)
                return
            else:
                return

        yield from super().modify_tensors(data_torch, name, bid)

    def prepare_tensors(self):
        super().prepare_tensors()

        if self._chunk_experts is not None:
            # flatten `list[dict[str, Tensor]]` into `list[str]`
            chunk_experts = [k for d in self._chunk_experts for k in d.keys()]
            if len(chunk_experts) > 0:
                raise ValueError(f"Unprocessed adjugate experts: {chunk_experts}")

        if self._experts is not None:
            # flatten `list[dict[str, Tensor]]` into `list[str]`
            experts = [k for d in self._experts for k in d.keys()]
            if len(experts) > 0:
                raise ValueError(f"Unprocessed experts: {experts}")


@ModelBase.register("ChameleonForConditionalGeneration")
@ModelBase.register("ChameleonForCausalLM")  # obsolete
class ChameleonModel(TextModel):
    model_arch = gguf.MODEL_ARCH.CHAMELEON

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        self.gguf_writer.add_swin_norm(self.hparams.get("swin_norm", False))

    def set_vocab(self):
        self._set_vocab_gpt2()

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        # ignore image tokenizer for now
        # TODO: image support for Chameleon
        if name.startswith("model.vqmodel"):
            return None

        return super().filter_tensors(item)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        n_head = self.hparams["num_attention_heads"]
        n_kv_head = self.hparams.get("num_key_value_heads")
        hidden_dim = self.hparams.get("hidden_size")

        if name.endswith(("q_proj.weight", "q_proj.bias")):
            data_torch = LlamaModel.permute(data_torch, n_head, n_head)
        if name.endswith(("k_proj.weight", "k_proj.bias")):
            data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)
        if name.endswith(("q_norm.weight", "q_norm.bias")):
            data_torch = ChameleonModel._reverse_hf_permute(data_torch, n_head, hidden_dim)
        if name.endswith(("k_norm.weight", "k_norm.bias")):
            data_torch = ChameleonModel._reverse_hf_permute(data_torch, n_kv_head, hidden_dim)

        yield from super().modify_tensors(data_torch, name, bid)

    # see: https://github.com/huggingface/transformers/blob/72fb02c47dbbe1999ae105319f24631cad6e2e00/src/transformers/models/chameleon/convert_chameleon_weights_to_hf.py#L176-L203
    @staticmethod
    def _reverse_hf_permute(data_torch, n_heads, hidden_dim):
        head_dim = hidden_dim // n_heads
        data_torch = data_torch[0].view(2, head_dim // 2).t().reshape(1, -1)
        data_torch = data_torch.repeat_interleave(n_heads, 0)
        return data_torch


@ModelBase.register("UltravoxModel")
class UltravoxModel(TextModel):
    model_arch = gguf.MODEL_ARCH.LLAMA # dummy

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        raise NotImplementedError("Ultravox does not have text decoder. Instead, it uses Llama or other models for text. If you want to get the audio encoder, please use --mmproj argument")


@ModelBase.register("GlmasrModel")
class GlmASRWhisperEncoderModel(MmprojModel):
    has_vision_encoder = False
    has_audio_encoder = True

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        if "hidden_size" not in self.hparams and "intermediate_size" not in self.hparams:
            self.hparams["hidden_size"] = self.hparams["d_model"]
            self.hparams["intermediate_size"] = self.hparams["encoder_ffn_dim"]
            self.hparams["num_attention_heads"] = self.hparams["encoder_attention_heads"]

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.GLMA)
        self.gguf_writer.add_audio_num_mel_bins(self.hparams["num_mel_bins"])
        self.gguf_writer.add_audio_attention_layernorm_eps(self.hparams.get("layer_norm_eps", 1e-5))
        self.gguf_writer.add_audio_stack_factor(self.global_config["merge_factor"])

    def tensor_force_quant(self, name, new_name, bid, n_dims):
        if ".conv" in name and ".weight" in name:
            return gguf.GGMLQuantizationType.F16
        return super().tensor_force_quant(name, new_name, bid, n_dims)

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        if name.startswith(("model.", "lm_head.")):
            # skip language model tensors
            return None

        if name.startswith("audio_encoder.whisper."):
            name = name.replace("audio_encoder.whisper.","audio_tower.")
        if "audio_encoder.layer_norm." in name or "audio_encoder.proj." in name:
            name = name.replace("audio_encoder.", "audio_encoder.adapting.")
        if name.startswith("audio_encoder.adapting."):
            name = name.replace("audio_encoder.adapting.","audio.multi_modal_projector.")
            if ".layer_norm." in name:
                name = name.replace(".layer_norm.", ".ln_pre.")
            if ".0." in name:
                name = name.replace(".0.", ".linear_1.")
            if ".2." in name:
                name = name.replace(".2.", ".linear_2.")

        return super().filter_tensors((name, gen))

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        if name.startswith("audio_encoder.audio_bos_eos_token."):
            yield from super().modify_tensors(data_torch[0], "model.vision.boi", bid)
            yield from super().modify_tensors(data_torch[1], "model.vision.eoi", bid)
            return

        if name.startswith("audio_encoder.adapting."):
            if ".proj." in name:
                return

        if "conv1.bias" in name or "conv2.bias" in name:
            # transpose conv1 and conv2 bias
            data_torch = data_torch.unsqueeze(-1)

        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("Qwen2AudioForConditionalGeneration")
class WhisperEncoderModel(MmprojModel):
    has_vision_encoder = False # no vision encoder
    has_audio_encoder = True

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        if "hidden_size" not in self.hparams and "intermediate_size" not in self.hparams:
            self.hparams["hidden_size"] = self.hparams["d_model"]
            self.hparams["intermediate_size"] = self.hparams["encoder_ffn_dim"]
            self.hparams["num_attention_heads"] = self.hparams["encoder_attention_heads"]

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.QWEN2A)
        self.gguf_writer.add_audio_num_mel_bins(self.hparams["num_mel_bins"])
        self.gguf_writer.add_audio_attention_layernorm_eps(self.hparams.get("layer_norm_eps", 1e-5))

    def tensor_force_quant(self, name, new_name, bid, n_dims):
        if ".conv" in name and ".weight" in name:
            return gguf.GGMLQuantizationType.F16
        return super().tensor_force_quant(name, new_name, bid, n_dims)

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        # prevent clash naming with vision tensors
        if name.startswith("multi_modal_projector"):
            name = "audio." + name

        return super().filter_tensors((name, gen))

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        if "conv1.bias" in name or "conv2.bias" in name:
            # transpose conv1 and conv2 bias
            data_torch = data_torch.unsqueeze(-1)

        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("UltravoxModel")
class UltravoxWhisperEncoderModel(WhisperEncoderModel):
    has_vision_encoder = False # no vision encoder
    has_audio_encoder = True

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.ULTRAVOX)
        self.gguf_writer.add_audio_stack_factor(self.global_config["stack_factor"])


@ModelBase.register("MERaLiON2ForConditionalGeneration")
class MERaLiONWhisperEncoderModel(WhisperEncoderModel):
    has_vision_encoder = False
    has_audio_encoder = True

    def get_audio_config(self) -> dict[str, Any] | None:
        return self.global_config.get("speech_config")

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.MERALION)
        self.gguf_writer.add_audio_stack_factor(self.global_config.get("speech_mlp_scale_factor", 15))

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        if name.startswith("text_decoder."):
            return None

        if name.startswith("speech_encoder."):
            name = name.replace("speech_encoder.", "audio_tower.")

        return super().filter_tensors((name, gen))

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        suffix = "." + name.rsplit(".", 1)[-1]

        if name.startswith("ln_speech."):
            yield (self.format_tensor_name(gguf.MODEL_TENSOR.A_MM_NORM_PRE, suffix=suffix), data_torch)
            return

        if name.startswith("speech_audio_adapter."):
            if ".mlp_adapter.0." in name:
                yield (self.format_tensor_name(gguf.MODEL_TENSOR.A_MMPROJ, 0, suffix=suffix), data_torch)
            elif ".gate_proj." in name:
                yield (self.format_tensor_name(gguf.MODEL_TENSOR.A_MMPROJ, 1, suffix=suffix), data_torch)
            elif ".pool_proj." in name:
                yield (self.format_tensor_name(gguf.MODEL_TENSOR.A_MMPROJ, 2, suffix=suffix), data_torch)
            elif ".out_proj." in name:
                yield (self.format_tensor_name(gguf.MODEL_TENSOR.A_MMPROJ, 3, suffix=suffix), data_torch)
            return

        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("VoxtralForConditionalGeneration")
class VoxtralWhisperEncoderModel(WhisperEncoderModel):
    has_vision_encoder = False # no vision encoder
    has_audio_encoder = True

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.VOXTRAL)
        self.gguf_writer.add_audio_stack_factor(4) # == intermediate_size // hidden_size


@ModelBase.register("AudioFlamingo3ForConditionalGeneration")
class AudioFlamingo3WhisperEncoderModel(WhisperEncoderModel):
    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.MUSIC_FLAMINGO)

    def tensor_force_quant(self, name, new_name, bid, n_dims):
        if ".conv" in name and ".weight" in name:
            # Was trained in BF16, being safe, avoiding quantizing to FP16
            return gguf.GGMLQuantizationType.F32
        return super().tensor_force_quant(name, new_name, bid, n_dims)


@ModelBase.register("FalconH1ForCausalLM")
class FalconH1Model(Mamba2Model):
    model_arch = gguf.MODEL_ARCH.FALCON_H1

    def __init__(self, *args, **kwargs):
        # Set the hparam prefixes for Falcon Mamba2
        self.hparam_prefixes = ["mamba"]

        # Initialize the base Mamba2Model
        super().__init__(*args, **kwargs)

        # Use Llama conversion for attention
        self._transformer_model_class = LlamaModel

        # n_group and d_inner are used during reshape_tensors for mamba2
        self.n_group = self.find_hparam(["n_groups"])
        self.d_inner = self.find_hparam(["mamba_d_ssm"])
        self.d_head = self.find_hparam(["d_head"])

        # Initialize any Falcon Mamba2 specific attributes
        self.has_attention = True  # Falcon Mamba2 has attention components

        # Load Falcon-H1 multipliers from hyperparameters
        self.attention_in_multiplier = self.find_hparam(["attention_in_multiplier"], optional=True)
        self.attention_out_multiplier = self.find_hparam(["attention_out_multiplier"], optional=True)
        self.ssm_in_multiplier = self.find_hparam(["ssm_in_multiplier"], optional=True)
        self.ssm_out_multiplier = self.find_hparam(["ssm_out_multiplier"], optional=True)
        self.mlp_multipliers = self.find_hparam(["mlp_multipliers"], optional=True)
        self.ssm_multipliers = self.find_hparam(["ssm_multipliers"], optional=True)
        self.intermediate_size = self.find_hparam(["intermediate_size"])
        self.key_multiplier = self.find_hparam(["key_multiplier"], optional=True)

    def find_hparam(self, keys: Iterable[str], *args, **kwargs) -> Any:
        prefixed = []
        for pfx in self.hparam_prefixes:
            prefixed.extend(
                "_".join([pfx, k])
                for k in keys
            )
        keys = list(keys) + prefixed
        return super().find_hparam(keys, *args, **kwargs)

    def set_vocab(self):
        self._set_vocab_gpt2()

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        tensors = list(super().modify_tensors(data_torch, name, bid))
        tensor = tensors[0][1]

        if "down_proj" in name:
            tensor = tensor  * self.mlp_multipliers[1]
        elif "gate_proj" in name:
            tensor = tensor * self.mlp_multipliers[0]
        elif "k_proj" in name:
            tensor = tensor * self.key_multiplier * self.attention_in_multiplier
        elif "q_proj" in name:
            tensor = tensor * self.attention_in_multiplier
        elif "v_proj" in name:
            tensor = tensor * self.attention_in_multiplier
        elif "o_proj" in name:
            tensor = tensor * self.attention_out_multiplier
        elif "out_proj" in name:
            tensor = tensor * self.ssm_out_multiplier
        elif "in_proj" in name:
            tensor = tensor * self.ssm_in_multiplier
            zxbcdt_multipliers = self.hparams["ssm_multipliers"]
            intermediate_size = self.hparams["mamba_d_ssm"]
            groups_time_state_size = self.hparams["mamba_n_groups"] * self.hparams["mamba_d_state"]
            tensor[:intermediate_size, :] *= zxbcdt_multipliers[0]
            tensor[intermediate_size:2 * intermediate_size, :] *= zxbcdt_multipliers[1]
            tensor[2 * intermediate_size:2 * intermediate_size + groups_time_state_size, :] *= zxbcdt_multipliers[2]
            tensor[2 * intermediate_size + groups_time_state_size:2 * intermediate_size + 2 * groups_time_state_size, :] *= zxbcdt_multipliers[3]
            tensor[2 * intermediate_size + 2 * groups_time_state_size:, :] *= zxbcdt_multipliers[4]
        elif "lm_head" in name:
            tensor = tensor * self.hparams["lm_head_multiplier"]
        elif "embed_tokens" in name:
            tensor = tensor * self.hparams["embedding_multiplier"]
        elif "mamba.norm" in name:
            tensor = tensor.reshape(self.n_group, self.d_inner // self.n_group)

        tensors = [(tensors[0][0], tensor)]
        return tensors

    def set_gguf_parameters(self):
        super().set_gguf_parameters()

        ## General Params ##
        self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])
        # Override some Mamba2 defaults
        self.gguf_writer.add_block_count(self.block_count)
        self.gguf_writer.add_context_length(self.hparams.get("max_position_embeddings", 0))
        self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])

        ## Attention params ##
        self.gguf_writer.add_head_count(self.hparams["num_attention_heads"]) # Override value 0 from Mamba2
        self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"])
        self.gguf_writer.add_key_length(self.hparams["head_dim"])
        self.gguf_writer.add_value_length(self.hparams["head_dim"])

        ## Validation ##
        assert self.hparams.get("hidden_act") in [None, "silu"], "Only SILU activation supported"
        assert self.d_inner % self.d_head == 0, f"SSM inner size {self.d_inner} not a multiple of head dim {self.d_head}"

        # Add any other Falcon Mamba2 specific configuration
        self.gguf_writer.add_rope_freq_base(self.rope_parameters["rope_theta"])


@ModelBase.register("HunYuanMoEV1ForCausalLM")
class HunYuanMoEModel(TextModel):
    model_arch = gguf.MODEL_ARCH.HUNYUAN_MOE

    def set_vocab(self):
        from transformers import AutoTokenizer
        tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)

        # 1. Get the pre-tokenizer identifier hash
        tokpre = self.get_vocab_base_pre(tokenizer)

        # 2. Reverse-engineer the merges list from mergeable_ranks
        merges = []
        vocab = {}
        mergeable_ranks = tokenizer.mergeable_ranks  # ty: ignore[unresolved-attribute]
        for token, rank in mergeable_ranks.items():
            vocab[QwenModel.token_bytes_to_string(token)] = rank
            if len(token) == 1:
                continue
            merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank)
            if len(merged) == 2: # todo this is an assert in Qwen, why?
                merges.append(' '.join(map(QwenModel.token_bytes_to_string, merged)))

        # 3. Generate the tokens and toktypes lists
        vocab_size = self.hparams["vocab_size"]
        assert tokenizer.vocab_size == vocab_size  # ty: ignore[unresolved-attribute]
        special_tokens = tokenizer.special_tokens  # ty: ignore[unresolved-attribute]
        reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **special_tokens}.items()}
        tokens: list[str] = []
        toktypes: list[int] = []
        for i in range(vocab_size):
            if i not in reverse_vocab:
                tokens.append(f"[PAD{i}]")
                toktypes.append(gguf.TokenType.UNUSED)
            else:
                token = reverse_vocab[i]
                tokens.append(token)
                if i in special_tokens.values():
                    toktypes.append(gguf.TokenType.CONTROL)
                else:
                    toktypes.append(gguf.TokenType.NORMAL)

        # 4. Write all vocab-related fields to the GGUF writer
        self.gguf_writer.add_tokenizer_model("gpt2")
        self.gguf_writer.add_tokenizer_pre(tokpre)
        self.gguf_writer.add_token_list(tokens)
        self.gguf_writer.add_token_types(toktypes)
        self.gguf_writer.add_token_merges(merges)

        # 5. Add special tokens and chat templates
        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False)
        special_vocab.add_to_gguf(self.gguf_writer)
        # FIX for BOS token: Overwrite incorrect id read from config.json
        self.gguf_writer.add_bos_token_id(127959) # <|bos|>

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        hparams = self.hparams

        self.gguf_writer.add_expert_shared_feed_forward_length(hparams["intermediate_size"])

        moe_intermediate_size = hparams["moe_intermediate_size"]
        assert all(n == moe_intermediate_size[0] for n in moe_intermediate_size)
        self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size[0])

        moe_topk = hparams["moe_topk"]
        assert all(topk == moe_topk[0] for topk in moe_topk)
        self.gguf_writer.add_expert_used_count(moe_topk[0])

        moe_shared_expert = hparams["num_shared_expert"]
        assert all(n == moe_shared_expert[0] for n in moe_shared_expert)
        self.gguf_writer.add_expert_shared_count(moe_shared_expert[0])

        # Rope
        if self.rope_parameters.get("rope_type") == "dynamic":
            # HunYuan uses NTK Aware Alpha based scaling. Original implementation: https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/
            # 1000 corresponds to a usable context length of 256k (https://github.com/Tencent-Hunyuan/Hunyuan-A13B/blob/main/report/Hunyuan_A13B_Technical_Report.pdf)
            alpha = self.rope_parameters.get("alpha", 1000)
            base = self.rope_parameters.get("rope_theta", 10000.0)
            dim = (hparams["hidden_size"] // hparams["num_attention_heads"]) # 128
            scaled_base = base * (alpha ** (dim / (dim - 2))) # 10000 * (1000 ** (128 / 126)) = 11158839.9251
            self.gguf_writer.add_rope_freq_base(scaled_base)
            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
            self.gguf_writer.add_rope_scaling_factor(1)
            # There is no consistent way to calculate ctx from alpha, and the config is incorrectly set to 32k
            self.gguf_writer.add_rope_scaling_orig_ctx_len(256 * 1024) # 256k context length
            self.gguf_writer.add_context_length(256 * 1024) # 256k context length

            # if any of our assumptions about the values are wrong, something has changed and this may need to be updated
            assert alpha == 1000 and base == 10000.0 and dim == 128 and self.hparams["max_position_embeddings"] in [32 * 1024, 256 * 1024] , \
                "HunYuan dynamic RoPE scaling assumptions changed, please update the logic or context length manually"

    _experts: list[dict[str, Tensor]] | None = None

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        if name == "lm_head.weight":
            if self.hparams.get("tie_word_embeddings", False):
                logger.info("Skipping tied output layer 'lm_head.weight'")
                return

        if name.find("mlp.experts") != -1:
            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
            assert bid is not None

            if self._experts is None:
                self._experts = [{} for _ in range(self.block_count)]

            self._experts[bid][name] = data_torch

            if len(self._experts[bid]) >= n_experts * 3:
                # merge the experts into a single 3d tensor
                for w_name in ["down_proj", "gate_proj", "up_proj"]:
                    datas: list[Tensor] = []

                    for xid in range(n_experts):
                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
                        datas.append(self._experts[bid][ename])
                        del self._experts[bid][ename]

                    data_torch = torch.stack(datas, dim=0)
                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"

                    yield from super().modify_tensors(data_torch, merged_name, bid)
                return
            else:
                return

        yield from super().modify_tensors(data_torch, name, bid)

    def prepare_tensors(self):
        super().prepare_tensors()
        if self._experts is not None:
            experts = [k for d in self._experts for k in d.keys()]
            if len(experts) > 0:
                raise ValueError(f"Unprocessed experts: {experts}")


@ModelBase.register("LLaDAMoEModel", "LLaDAMoEModelLM")
class LLaDAMoEModel(TextModel):
    model_arch = gguf.MODEL_ARCH.LLADA_MOE

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        if (expert_intermediate_size := self.hparams.get("expert_intermediate_size")) is not None:
            self.gguf_writer.add_expert_feed_forward_length(expert_intermediate_size)

        self.gguf_writer.add_mask_token_id(156895)
        self.gguf_writer.add_causal_attention(False)
        self.gguf_writer.add_diffusion_shift_logits(False)

    _experts: list[dict[str, Tensor]] | None = None

    # Copied from: Qwen2MoeModel
    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        # process the experts separately
        if name.find("experts") != -1:
            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
            assert bid is not None

            if self._experts is None:
                self._experts = [{} for _ in range(self.block_count)]

            self._experts[bid][name] = data_torch

            if len(self._experts[bid]) >= n_experts * 3:
                # merge the experts into a single 3d tensor
                for w_name in ["down_proj", "gate_proj", "up_proj"]:
                    datas: list[Tensor] = []

                    for xid in range(n_experts):
                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
                        datas.append(self._experts[bid][ename])
                        del self._experts[bid][ename]

                    data_torch = torch.stack(datas, dim=0)

                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"

                    yield from super().modify_tensors(data_torch, merged_name, bid)
                return
            else:
                return

        yield from super().modify_tensors(data_torch, name, bid)

    # Copied from: Qwen2MoeModel
    def prepare_tensors(self):
        super().prepare_tensors()

        if self._experts is not None:
            # flatten `list[dict[str, Tensor]]` into `list[str]`
            experts = [k for d in self._experts for k in d.keys()]
            if len(experts) > 0:
                raise ValueError(f"Unprocessed experts: {experts}")


@ModelBase.register("HunYuanDenseV1ForCausalLM")
class HunYuanModel(TextModel):
    model_arch = gguf.MODEL_ARCH.HUNYUAN_DENSE

    def _get_eod_token_id(self) -> int | None:
        """Get the actual end-of-generation token from config (eod_token_id)."""
        return self.hparams.get("eod_token_id")

    def _get_eot_token_id(self) -> int | None:
        """Get the end-of-turn token from generation_config.json.
        This is the first entry in eos_token_id when it's a list."""
        gen_cfg_path = self.dir_model / "generation_config.json"
        if gen_cfg_path.is_file():
            with open(gen_cfg_path, encoding="utf-8") as f:
                gen_cfg = json.load(f)
            eos = gen_cfg.get("eos_token_id")
            if isinstance(eos, list) and len(eos) >= 2:
                return eos[0]
        return None

    def _fix_special_tokens(self):
        """Fix EOS/EOT tokens that are incorrect in upstream configs."""
        eod_id = self._get_eod_token_id()
        if eod_id is not None:
            self.gguf_writer.add_eos_token_id(eod_id)
        eot_id = self._get_eot_token_id()
        if eot_id is not None:
            self.gguf_writer.add_eot_token_id(eot_id)

    def set_vocab(self):
        if (self.dir_model / "tokenizer.json").is_file():
            tokens, toktypes, tokpre = self.get_vocab_base()
            self.gguf_writer.add_tokenizer_model("gpt2")
            self.gguf_writer.add_tokenizer_pre(tokpre)
            self.gguf_writer.add_token_list(tokens)
            self.gguf_writer.add_token_types(toktypes)

            # HunyuanOCR has pad_token_id=-1 in config.json; exclude pad from SpecialVocab
            token_types = None
            if (self.hparams.get("pad_token_id") or 0) < 0:
                token_types = ('bos', 'eos', 'unk', 'sep', 'cls', 'mask')
            special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True, special_token_types=token_types)
            special_vocab.add_to_gguf(self.gguf_writer)
            self._fix_special_tokens()
        else:
            from transformers import AutoTokenizer
            tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)

            # 1. Get the pre-tokenizer identifier hash
            tokpre = self.get_vocab_base_pre(tokenizer)

            # 2. Reverse-engineer the merges list from mergeable_ranks
            merges = []
            vocab = {}
            mergeable_ranks = tokenizer.mergeable_ranks  # ty: ignore[unresolved-attribute]
            for token, rank in mergeable_ranks.items():
                vocab[QwenModel.token_bytes_to_string(token)] = rank
                if len(token) == 1:
                    continue
                merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank)
                if len(merged) == 2:
                    merges.append(' '.join(map(QwenModel.token_bytes_to_string, merged)))

            # 3. Generate the tokens and toktypes lists
            vocab_size = self.hparams["vocab_size"]
            assert tokenizer.vocab_size == vocab_size  # ty: ignore[unresolved-attribute]
            special_tokens = tokenizer.special_tokens  # ty: ignore[unresolved-attribute]
            reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **special_tokens}.items()}
            tokens: list[str] = []
            toktypes: list[int] = []
            for i in range(vocab_size):
                if i not in reverse_vocab:
                    tokens.append(f"[PAD{i}]")
                    toktypes.append(gguf.TokenType.UNUSED)
                else:
                    token = reverse_vocab[i]
                    tokens.append(token)
                    if i in special_tokens.values():
                        toktypes.append(gguf.TokenType.CONTROL)
                    else:
                        toktypes.append(gguf.TokenType.NORMAL)

            # 4. Write all vocab-related fields to the GGUF writer
            self.gguf_writer.add_tokenizer_model("gpt2")
            self.gguf_writer.add_tokenizer_pre(tokpre)
            self.gguf_writer.add_token_list(tokens)
            self.gguf_writer.add_token_types(toktypes)
            self.gguf_writer.add_token_merges(merges)

            # 5. Add special tokens and chat templates
            special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False)
            special_vocab.add_to_gguf(self.gguf_writer)
            # FIX for BOS token: Overwrite incorrect id read from config.json
            if self.hparams['hidden_size'] == 4096:
                self.gguf_writer.add_bos_token_id(127958) # only for 7b dense, fix <|bos|> token
            self._fix_special_tokens()

    def set_gguf_parameters(self):
        # HunyuanOCR has num_experts=1 which is not MoE, prevent parent from writing it
        saved_num_experts = self.hparams.pop("num_experts", None)
        super().set_gguf_parameters()
        if saved_num_experts is not None and saved_num_experts > 1:
            self.hparams["num_experts"] = saved_num_experts
        hparams = self.hparams

        # Rope
        if self.rope_parameters.get("rope_type") in ("dynamic", "xdrope"):
            # HunYuan uses NTK Aware Alpha based scaling. Original implementation: https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/
            # 1000 corresponds to a usable context length of 256k (https://github.com/Tencent-Hunyuan/Hunyuan-A13B/blob/main/report/Hunyuan_A13B_Technical_Report.pdf)
            alpha = self.rope_parameters.get("alpha", 50)
            base = self.rope_parameters.get("rope_theta", 10000.0)
            dim = hparams["head_dim"]
            scaled_base = base * (alpha ** (dim / (dim - 2)))
            self.gguf_writer.add_rope_freq_base(scaled_base)
            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
            self.gguf_writer.add_rope_scaling_factor(1)
            if self.rope_parameters.get("rope_type") == "dynamic":
                # There is no consistent way to calculate ctx from alpha, and the config is incorrectly set to 32k
                self.gguf_writer.add_rope_scaling_orig_ctx_len(256 * 1024) # 256k context length
                self.gguf_writer.add_context_length(256 * 1024) # 256k context length

                # if any of our assumptions about the values are wrong, something has changed and this may need to be updated
                assert base == 10000.0 and self.hparams["max_position_embeddings"] in [32 * 1024, 256 * 1024] , \
                    "HunYuan dynamic RoPE scaling assumptions changed, please update the logic or context length manually"

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        if name == "lm_head.weight":
            if self.hparams.get("tie_word_embeddings", False):
                logger.info("Skipping tied output layer 'lm_head.weight'")
                return

        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("HunYuanVLForConditionalGeneration")
class HunyuanVLVisionModel(MmprojModel):
    # Handles both HunyuanOCR and HunyuanVL, which share the HF architecture name
    # "HunYuanVLForConditionalGeneration" and the `vit.perceive.*` vision layout.
    # Each variant maps to a different projector type in clip.cpp so image
    # preprocessing follows the correct code path.

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        assert self.hparams_vision is not None
        # HunyuanOCR / HunyuanVL uses max_image_size instead of image_size
        if "image_size" not in self.hparams_vision:
            self.hparams_vision["image_size"] = self.hparams_vision.get("max_image_size", 2048)

    @staticmethod
    def is_ocr_variant(hparams: dict) -> bool:
        """Return True for HunyuanOCR, False for HunyuanVL.

        The projector's output dim must equal the text model's hidden_size by
        construction (that's what "projector" means). HunyuanOCR pairs a 1B text
        backbone (hidden=1024); HunyuanVL pairs a 4B one (hidden=3072). So the
        ViT -> LLM projection dim is a hard architectural signature, not a
        magic number.
        """
        vision_out = int((hparams.get("vision_config") or {}).get("out_hidden_size", 0))
        return vision_out == 1024

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        assert self.hparams_vision is not None
        vcfg = self.hparams_vision

        if self.is_ocr_variant(self.global_config):
            # --- HunyuanOCR ---
            self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.HUNYUANOCR)
            self.gguf_writer.add_vision_use_gelu(True)
            self.gguf_writer.add_vision_attention_layernorm_eps(vcfg.get("rms_norm_eps", 1e-5))
            self.gguf_writer.add_vision_spatial_merge_size(vcfg.get("spatial_merge_size", 2))
            self.gguf_writer.add_vision_min_pixels(self.preprocessor_config["min_pixels"])
            self.gguf_writer.add_vision_max_pixels(self.preprocessor_config["max_pixels"])
            return

        # --- HunyuanVL ---
        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.HUNYUANVL)
        self.gguf_writer.add_vision_use_gelu(str(vcfg["hidden_act"]).lower() == "gelu")
        self.gguf_writer.add_vision_attention_layernorm_eps(float(vcfg["rms_norm_eps"]))
        self.gguf_writer.add_vision_spatial_merge_size(int(vcfg["spatial_merge_size"]))
        self.gguf_writer.add_vision_min_pixels(int(self.preprocessor_config["min_pixels"]))
        self.gguf_writer.add_vision_max_pixels(int(self.preprocessor_config["max_pixels"]))

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        if not name.startswith("vit."):
            return None

        return super().filter_tensors(item)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        # strip CLS token (row 0) from position embeddings so resize_position_embeddings works
        if "position_embedding" in name:
            data_torch = data_torch[1:]  # [n_patches+1, n_embd] -> [n_patches, n_embd]
        yield from super().modify_tensors(data_torch, name, bid)

    def tensor_force_quant(self, name, new_name, bid, n_dims):
        # force conv weights to F32 or F16 to avoid BF16 IM2COL issues on Metal
        # Both HunyuanOCR and HunyuanVL emit the ViT -> LLM projection as mm.0/mm.2.
        if ("mm.0." in new_name or "mm.2." in new_name) and new_name.endswith(".weight"):
            return gguf.GGMLQuantizationType.F16 if self.ftype == gguf.LlamaFileType.MOSTLY_F16 else gguf.GGMLQuantizationType.F32
        return super().tensor_force_quant(name, new_name, bid, n_dims)


@ModelBase.register("HunYuanVLForConditionalGeneration")
class HunyuanVLTextModel(HunYuanModel):
    # The "HunYuanVLForConditionalGeneration" HF architecture covers both HunyuanOCR
    # and HunyuanVL. HunyuanOCR reuses the HunYuan-Dense text backbone (standard RoPE),
    # while HunyuanVL introduces a new LLM arch with XD-RoPE. Detect the variant from
    # the config and pick the matching GGUF architecture.
    model_arch = gguf.MODEL_ARCH.HUNYUAN_VL

    @staticmethod
    def _is_ocr_config(hparams: dict) -> bool:
        # OCR pairs a 1B text backbone (hidden=1024) with a ViT projector that
        # outputs 1024-d; HunyuanVL uses 3072-d. Keep in sync with
        # HunyuanVLVisionModel.is_ocr_variant.
        return int((hparams.get("vision_config") or {}).get("out_hidden_size", 0)) == 1024

    def __init__(self, dir_model: Path, *args, **kwargs):
        raw_hparams = kwargs.get("hparams") or ModelBase.load_hparams(dir_model, is_mistral_format=False)
        if self._is_ocr_config(raw_hparams):
            self.model_arch = gguf.MODEL_ARCH.HUNYUAN_DENSE
        else:
            self.model_arch = gguf.MODEL_ARCH.HUNYUAN_VL
        super().__init__(dir_model, *args, **kwargs)

    def set_gguf_parameters(self):
        super().set_gguf_parameters()

        # Only emit XD-RoPE metadata for the HunyuanVL backbone; HunyuanOCR uses
        # the HunYuan-Dense arch which already handles standard rope in super().
        if self.model_arch != gguf.MODEL_ARCH.HUNYUAN_VL:
            return

        if self.rope_parameters.get("rope_type") != "xdrope":
            return

        # defaults for HunyuanVL. The C++ side later computes:
        #   freq_base = rope_theta * alpha ** (head_dim / (head_dim - 2))
        self.gguf_writer.add_rope_freq_base(float(self.rope_parameters["rope_theta"]))
        self.gguf_writer.add_rope_scaling_alpha(float(self.rope_parameters["alpha"]))
        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
        self.gguf_writer.add_rope_scaling_factor(float(self.rope_parameters.get("factor", 1)))

        ctx_len = int(self.hparams["max_position_embeddings"])
        self.gguf_writer.add_rope_scaling_orig_ctx_len(ctx_len)
        self.gguf_writer.add_context_length(ctx_len)

        self.gguf_writer.add_rope_dimension_sections(list(self.rope_parameters["xdrope_section"]))


@ModelBase.register("SmolLM3ForCausalLM")
class SmolLM3Model(LlamaModel):
    model_arch = gguf.MODEL_ARCH.SMOLLM3


@ModelBase.register("GptOssForCausalLM")
class GptOssModel(TextModel):
    model_arch = gguf.MODEL_ARCH.GPT_OSS

    # TODO: remove once MXFP4 is supported more generally
    def dequant_model(self):
        if self._is_mxfp4:
            return
        return super().dequant_model()

    def transform_nibble_layout(self, tensor):
        assert tensor.dtype == torch.uint8
        assert tensor.shape[-1] == 16
        # swap nibbles
        t_lo = tensor & 0x0F
        t_hi = tensor & 0xF0
        t_swapped = (t_lo << 4) | (t_hi >> 4)
        tensor = t_swapped
        # transform aaaa...bbbb... to abababab...
        blk_a, blk_b = tensor.chunk(2, dim=-1)
        # get a_
        blk_a0 = (blk_a & 0xF0).view(-1, 1)
        blk_a1 = (blk_a << 4).view(-1, 1)
        blk_a = torch.stack((blk_a0, blk_a1), dim=2).view(tensor.shape)
        # get _b
        blk_b0 = (blk_b >> 4).view(-1, 1)
        blk_b1 = (blk_b & 0x0F).view(-1, 1)
        blk_b = torch.stack((blk_b0, blk_b1), dim=2).view(tensor.shape)
        # swap once more
        out = blk_a | blk_b
        out_h = out & 0xF0
        out_l = out & 0x0F
        out = (out_h >> 4) | (out_l << 4)
        return out

    def repack_mxfp4(self, new_name: str, blocks: Tensor, scales: Tensor):
        assert blocks.dtype == torch.uint8
        assert scales.dtype == torch.uint8
        scales = scales.unsqueeze(-1)
        assert len(blocks.shape) == 4
        assert len(scales.shape) == 4
        blocks = self.transform_nibble_layout(blocks)
        new_data = torch.concat((scales, blocks), dim=-1)
        new_shape = [new_data.shape[0], new_data.shape[1], new_data.shape[2] * 32]
        logger.info(f"Repacked {new_name} with shape {new_shape} and quantization MXFP4")
        # flatten last dim
        new_data = new_data.view(new_data.shape[0], new_data.shape[1], new_data.shape[2] * new_data.shape[3])
        new_data = new_data.numpy()
        self.gguf_writer.add_tensor(new_name, new_data, raw_dtype=gguf.GGMLQuantizationType.MXFP4)

    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
        blocks0: Tensor = torch.zeros(1)
        blocks1: Tensor = torch.zeros(1)
        # we assume that tensors are loaded in the correct order
        for name, data_torch in self.get_tensors():
            if "mlp.experts.down_proj_blocks" in name:
                blocks0 = data_torch
            elif "mlp.experts.down_proj_scales" in name:
                new_name = self.map_tensor_name(name.replace("_scales", ".weight"))
                self.repack_mxfp4(new_name, blocks0, data_torch)
            elif "mlp.experts.gate_up_proj_blocks" in name:
                blocks0, blocks1 = data_torch[:, ::2, :, :], data_torch[:, 1::2, :, :]
            elif "mlp.experts.gate_up_proj_scales" in name:
                scales0, scales1 = data_torch[:, ::2, :], data_torch[:, 1::2, :]
                new_name_gate = self.map_tensor_name(name.replace("gate_up_proj_scales", "gate_proj.weight"))
                new_name_up = self.map_tensor_name(name.replace("gate_up_proj_scales", "up_proj.weight"))
                self.repack_mxfp4(new_name_gate, blocks0, scales0)
                self.repack_mxfp4(new_name_up, blocks1, scales1)
        return []

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        if "sinks" in name:
            name += ".weight"

        return super().filter_tensors((name, gen))

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        # correct naming for down_proj
        if "down_proj" in name:
            if name.endswith("_bias"):
                name = name.replace("down_proj_bias", "down_proj.bias")
            elif "_blocks" not in name and "_scales" not in name:
                logger.warning(f"{name} is not in MXFP4, performance may be degraded")
                name = name.replace("down_proj", "down_proj.weight")
                data_torch = data_torch.transpose(-1, -2)
            else:
                # otherwise, it should already be repacked to ggml MXFP4 format
                return

        # split the gate_up into gate and up
        if "gate_up_proj" in name:
            if name.endswith("_bias"):
                name_up = name.replace("gate_up_proj_bias", "up_proj.bias")
                name_gate = name.replace("gate_up_proj_bias", "gate_proj.bias")
                gate_proj_bias, up_proj_bias = data_torch[..., ::2], data_torch[..., 1::2]
                yield from super().modify_tensors(gate_proj_bias, name_gate, bid)
                yield from super().modify_tensors(up_proj_bias, name_up, bid)
            elif "_blocks" not in name and "_scales" not in name:
                logger.warning(f"{name} is not in MXFP4, performance may be degraded")
                name_up = name.replace("gate_up_proj", "up_proj.weight")
                name_gate = name.replace("gate_up_proj", "gate_proj.weight")
                data_torch = data_torch.transpose(-1, -2)
                gate_proj_weight, up_proj_weight = data_torch[:, ::2, :], data_torch[:, 1::2, :]
                yield from super().modify_tensors(gate_proj_weight, name_gate, bid)
                yield from super().modify_tensors(up_proj_weight, name_up, bid)
        else:
            yield from super().modify_tensors(data_torch, name, bid)

    def set_vocab(self):
        self._set_vocab_gpt2()

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
        self.gguf_writer.add_expert_feed_forward_length(self.hparams["intermediate_size"])


@ModelBase.register("Lfm2ForCausalLM", "LFM2ForCausalLM")
class LFM2Model(TextModel):
    model_arch = gguf.MODEL_ARCH.LFM2

    def _add_feed_forward_length(self):
        ff_dim = self.find_hparam(["block_ff_dim", "intermediate_size"])
        auto_adjust_ff_dim = self.hparams["block_auto_adjust_ff_dim"]
        ffn_dim_multiplier = self.hparams["block_ffn_dim_multiplier"]
        multiple_of = self.hparams["block_multiple_of"]

        if auto_adjust_ff_dim:
            ff_dim = int(2 * ff_dim / 3)
            # custom dim factor multiplier
            if ffn_dim_multiplier is not None:
                ff_dim = int(ffn_dim_multiplier * ff_dim)
            ff_dim = multiple_of * ((ff_dim + multiple_of - 1) // multiple_of)

        self.gguf_writer.add_feed_forward_length(ff_dim)

    def set_gguf_parameters(self):
        # set num_key_value_heads only for attention layers
        self.hparams["num_key_value_heads"] = [
            self.hparams["num_key_value_heads"] if layer_type != "conv" else 0
            for layer_type in self.hparams["layer_types"]
        ]

        super().set_gguf_parameters()
        self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])
        self.gguf_writer.add_shortconv_l_cache(self.hparams["conv_L_cache"])
        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["norm_eps"])
        self._add_feed_forward_length()

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        if ConformerAudioModel.is_audio_tensor(name):
            # skip multimodal tensors
            return None

        name = name.replace("lfm.", "model.")      # audio

        return super().filter_tensors((name, gen))

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        # conv op requires 2d tensor
        if 'conv.conv' in name:
            data_torch = data_torch.squeeze(1)

        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("Lfm2Model")
class LFM2ColBertModel(LFM2Model):
    model_arch = gguf.MODEL_ARCH.LFM2
    dense_tensor_name = "dense_2"

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        if not name.startswith(self.dense_tensor_name):
            name = "model." + name

        yield from super().modify_tensors(data_torch, name, bid)

    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
        # dense tensor is stored in a separate safetensors file
        from safetensors.torch import load_file
        tensors_file = self.dir_model / "1_Dense" / "model.safetensors"
        assert tensors_file.is_file()
        tensor = load_file(tensors_file)["linear.weight"]
        self.gguf_writer.add_embedding_length_out(tensor.shape[0])
        yield f"{self.dense_tensor_name}.weight", tensor.clone()


@ModelBase.register("Lfm2MoeForCausalLM")
class LFM2MoeModel(TextModel):
    model_arch = gguf.MODEL_ARCH.LFM2MOE

    def set_gguf_parameters(self):
        # set num_key_value_heads only for attention layers
        self.hparams["num_key_value_heads"] = [
            self.hparams["num_key_value_heads"] if layer_type == "full_attention" else 0
            for layer_type in self.hparams["layer_types"]
        ]

        super().set_gguf_parameters()

        self.gguf_writer.add_expert_feed_forward_length(self.hparams["moe_intermediate_size"])
        self.gguf_writer.add_leading_dense_block_count(self.hparams["num_dense_layers"])
        self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)

        self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])
        self.gguf_writer.add_shortconv_l_cache(self.hparams["conv_L_cache"])

    # cache for experts weights for merging
    _experts_cache: dict[int, dict[str, Tensor]] = {}

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        if name.endswith(".expert_bias"):
            name = name.replace(".expert_bias", ".expert_bias.bias")

        return super().filter_tensors((name, gen))

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        # conv op requires 2d tensor
        if 'conv.conv' in name:
            data_torch = data_torch.squeeze(1)

        # merge expert weights
        if 'experts' in name:
            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
            assert bid is not None

            expert_cache = self._experts_cache.setdefault(bid, {})
            expert_cache[name] = data_torch
            expert_weights = ["w1", "w2", "w3"]

            # not enough expert weights to merge
            if len(expert_cache) < n_experts * len(expert_weights):
                return

            for w_name in expert_weights:
                datas: list[Tensor] = []

                for xid in range(n_experts):
                    ename = f"model.layers.{bid}.feed_forward.experts.{xid}.{w_name}.weight"
                    datas.append(expert_cache[ename])
                    del expert_cache[ename]

                data_torch = torch.stack(datas, dim=0)
                merged_name = f"layers.{bid}.feed_forward.experts.{w_name}.weight"

                yield from super().modify_tensors(data_torch, merged_name, bid)

            del self._experts_cache[bid]
            return

        yield from super().modify_tensors(data_torch, name, bid)

    def prepare_tensors(self):
        super().prepare_tensors()
        assert not self._experts_cache


@ModelBase.register("Lfm2VlForConditionalGeneration")
class LFM2VLModel(MmprojModel):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        assert self.hparams_vision is not None
        # TODO(tarek): for dynamic resolution image_size is not specified, setting here for compatibility
        self.hparams_vision["image_size"] = 256

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.LFM2)
        self.gguf_writer.add_vision_attention_layernorm_eps(self.find_vparam(["layer_norm_eps"]))
        self.gguf_writer.add_vision_projector_scale_factor(self.global_config.get("downsample_factor", 2))
        self.gguf_writer.add_vision_use_gelu(True)
        # python notation, e.g. for vision_feature_layer == -1, we pick last layer -> vision_feature_layers_to_drop = 0
        vision_feature_layers_to_drop = -(self.global_config.get("vision_feature_layer", -1) + 1)
        self.gguf_writer.add_vision_block_count(self.find_vparam(self.n_block_keys) - vision_feature_layers_to_drop)

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        name = name.replace("model.vision_tower.", "vision_tower.")
        name = name.replace("model.multi_modal_projector.", "multi_modal_projector.")

        return super().filter_tensors((name, gen))

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        if "patch_embedding.weight" in name:
            data_torch = data_torch.view(data_torch.shape[0], 16, 16, 3).permute(0, 3, 1, 2)

        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("Lfm2AudioForConditionalGeneration")
class LFM2AudioModel(ConformerAudioModel):
    has_vision_encoder = False
    has_audio_encoder = True
    model_name = "Lfm2AudioEncoder"

    def get_audio_config(self) -> dict[str, Any] | None:
        return self.global_config.get("encoder")

    def set_gguf_parameters(self):
        assert self.hparams_audio is not None
        self.hparams_audio["hidden_size"] = self.hparams_audio["d_model"]
        self.hparams_audio["intermediate_size"] = self.hparams_audio["d_model"]
        self.hparams_audio["num_attention_heads"] = self.hparams_audio["n_heads"]
        super().set_gguf_parameters()
        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.LFM2A)
        self.gguf_writer.add_audio_num_mel_bins(self.hparams_audio["feat_in"])
        self.gguf_writer.add_audio_attention_layernorm_eps(1e-5)

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        # skip language model tensors
        if name.startswith("lfm."):
            return None

        # for training only
        if any(p in name for p in ["audio_loss_weight"]):
            return None

        # for audio output
        if any(p in name for p in ["codebook_offsets", "depth_embeddings", "depth_linear", "depthformer"]):
            return None

        return super().filter_tensors(item)


@ModelBase.register("GraniteSpeechForConditionalGeneration")
class GraniteSpeechMmprojModel(MmprojModel):
    has_vision_encoder = False
    has_audio_encoder = True

    _batch_norm_tensors: list[dict[str, Tensor]] | None = None

    def get_audio_config(self) -> dict[str, Any] | None:
        return self.global_config.get("encoder_config")

    def set_gguf_parameters(self):
        assert self.hparams_audio is not None
        a = self.hparams_audio
        a["hidden_size"] = a["hidden_dim"]
        a["intermediate_size"] = a["hidden_dim"] * a["feedforward_mult"]
        a["num_attention_heads"] = a["num_heads"]
        a["num_hidden_layers"] = a["num_layers"]

        super().set_gguf_parameters()

        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.GRANITE_SPEECH)
        self.gguf_writer.add_audio_num_mel_bins(a["input_dim"])
        self.gguf_writer.add_audio_attention_layernorm_eps(1e-5)
        self.gguf_writer.add_audio_chunk_size(a["context_size"])
        self.gguf_writer.add_audio_conv_kernel_size(a["conv_kernel_size"])
        self.gguf_writer.add_audio_max_pos_emb(a["max_pos_emb"])

        p = self.global_config
        self.gguf_writer.add_audio_projector_window_size(p["window_size"])
        self.gguf_writer.add_audio_projector_downsample_rate(p["downsample_rate"])
        self.gguf_writer.add_audio_projector_head_count(p["projector_config"]["num_attention_heads"])

    def tensor_force_quant(self, name, new_name, bid, n_dims):
        if "encoder" in name or "projector" in name:
            if ".conv" in name and ".weight" in name:
                return gguf.GGMLQuantizationType.F32
        return super().tensor_force_quant(name, new_name, bid, n_dims)

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item
        if "attention_dists" in name or "num_batches_tracked" in name:
            return None
        return super().filter_tensors(item)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        # fold running_mean, running_var and eps into weight and bias for batch_norm
        if "batch_norm" in name and "encoder.layers." in name:
            if self._batch_norm_tensors is None:
                self._batch_norm_tensors = [{} for _ in range(self.block_count)]
            assert bid is not None
            self._batch_norm_tensors[bid][name] = data_torch
            if len(self._batch_norm_tensors[bid]) < 4:
                return
            prefix = f"encoder.layers.{bid}.conv.batch_norm"
            weight = self._batch_norm_tensors[bid][f"{prefix}.weight"]
            bias = self._batch_norm_tensors[bid][f"{prefix}.bias"]
            running_mean = self._batch_norm_tensors[bid][f"{prefix}.running_mean"]
            running_var = self._batch_norm_tensors[bid][f"{prefix}.running_var"]
            eps = 1e-5
            a = weight / torch.sqrt(running_var + eps)
            b = bias - running_mean * a
            yield from super().modify_tensors(a, f"encoder.layers.{bid}.conv.batch_norm.weight", bid)
            yield from super().modify_tensors(b, f"encoder.layers.{bid}.conv.batch_norm.bias", bid)
            return

        if ".attn.to_kv.weight" in name:
            k_weight, v_weight = data_torch.chunk(2, dim=0)
            yield from super().modify_tensors(k_weight, name.replace("to_kv", "to_k"), bid)
            yield from super().modify_tensors(v_weight, name.replace("to_kv", "to_v"), bid)
            return

        if ("up_conv" in name or "down_conv" in name) and name.endswith(".weight"):
            if data_torch.ndim == 3 and data_torch.shape[2] == 1:
                data_torch = data_torch.squeeze(2)

        if "depth_conv" in name and name.endswith(".weight"):
            if data_torch.ndim == 3 and data_torch.shape[1] == 1:
                data_torch = data_torch.squeeze(1)

        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("Lfm25AudioTokenizer")
class LFM25AudioTokenizer(LFM2Model):
    model_arch = gguf.MODEL_ARCH.LFM2

    def set_vocab(self):
        self._set_vocab_none()

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
        self.gguf_writer.add_embedding_length_out(self.hparams["output_size"])

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        # skip language model tensors
        if name == "istft.window" or name.startswith("emb.emb"):
            return None

        if name.startswith("lin"):
            name = name.replace("lin", "dense_2_out")

        return super().filter_tensors((name, gen))


@ModelBase.register("SmallThinkerForCausalLM")
class SmallThinkerModel(TextModel):
    model_arch = gguf.MODEL_ARCH.SMALLTHINKER

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        if (n_experts := self.hparams.get("moe_num_primary_experts")) is not None:
            self.gguf_writer.add_expert_count(n_experts)
        if (n_experts_used := self.hparams.get("moe_num_active_primary_experts")) is not None:
            self.gguf_writer.add_expert_used_count(n_experts_used)
        if (moe_intermediate_size := self.hparams.get("moe_ffn_hidden_size")) is not None:
            self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
            self.gguf_writer.add_feed_forward_length(moe_intermediate_size)
            logger.info(f"gguf: expert feed forward length = {moe_intermediate_size}")
        if (self.hparams.get('moe_primary_router_apply_softmax')):
            self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SOFTMAX)
        else:
            self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)

        sliding_window_layout = self.hparams.get("sliding_window_layout")
        if sliding_window_layout:
            for i in sliding_window_layout:
                if i != 0:
                    sliding_window = self.hparams.get("sliding_window_size")
                    if sliding_window:
                        self.gguf_writer.add_sliding_window(sliding_window)
                    break

    _experts: list[dict[str, Tensor]] | None = None

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        # process the experts separately
        if name.find("experts") != -1:
            n_experts = self.hparams.get("moe_num_primary_experts") or self.find_hparam(["num_local_experts", "num_experts"])
            assert bid is not None

            if self._experts is None:
                self._experts = [{} for _ in range(self.block_count)]

            self._experts[bid][name] = data_torch

            if len(self._experts[bid]) >= n_experts * 3:
                # merge the experts into a single 3d tensor
                for w_name in ["down", "gate", "up"]:
                    datas: list[Tensor] = []

                    for xid in range(n_experts):
                        ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{w_name}.weight"
                        datas.append(self._experts[bid][ename])
                        del self._experts[bid][ename]

                    data_torch = torch.stack(datas, dim=0)

                    merged_name = f"model.layers.{bid}.block_sparse_moe.experts.{w_name}.weight"

                    yield from super().modify_tensors(data_torch, merged_name, bid)
                return
            else:
                return

        yield from super().modify_tensors(data_torch, name, bid)

    def prepare_tensors(self):
        super().prepare_tensors()

        if self._experts is not None:
            # flatten `list[dict[str, Tensor]]` into `list[str]`
            experts = [k for d in self._experts for k in d.keys()]
            if len(experts) > 0:
                raise ValueError(f"Unprocessed experts: {experts}")


@ModelBase.register("ModernBertModel", "ModernBertForMaskedLM", "ModernBertForSequenceClassification")
class ModernBertModel(BertModel):
    model_arch = gguf.MODEL_ARCH.MODERN_BERT

    def set_vocab(self):
        self.gguf_writer.add_add_bos_token(True)
        self.gguf_writer.add_add_eos_token(True)
        self.gguf_writer.add_add_sep_token(True)
        self._set_vocab_gpt2()

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        self.gguf_writer.add_sliding_window(self.hparams["local_attention"])
        if (sliding_window_pattern := self.hparams.get("global_attn_every_n_layers")) is not None:
            self.gguf_writer.add_sliding_window_pattern(sliding_window_pattern)
        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
        self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        if name.startswith("model."):
            name = name[6:]

        return super().filter_tensors((name, gen))

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        if self.cls_out_labels:
            # For BertForSequenceClassification (direct projection layer)
            if name == "classifier.weight":
                name = "classifier.out_proj.weight"

            if name == "classifier.bias":
                name = "classifier.out_proj.bias"

        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("ApertusForCausalLM")
class ApertusModel(LlamaModel):
    model_arch = gguf.MODEL_ARCH.APERTUS
    undo_permute = False

    _alpha_n = {}
    _alpha_p = {}
    _beta = {}
    _eps = {}

    def modify_tensors(self, data_torch, name, bid):
        # Handle xIELU activation parameters
        n_layers = self.hparams["num_hidden_layers"]
        if name.endswith(".act_fn.alpha_n"):
            self._alpha_n[bid] = data_torch.to("cpu").float().item()
            if (len(self._alpha_n) == n_layers):
                self.gguf_writer.add_xielu_alpha_n([self._alpha_n[k] for k in sorted(self._alpha_n)])
            return
        if name.endswith(".act_fn.alpha_p"):
            self._alpha_p[bid] = data_torch.to("cpu").float().item()
            if (len(self._alpha_p) == n_layers):
                self.gguf_writer.add_xielu_alpha_p([self._alpha_p[k] for k in sorted(self._alpha_p)])
            return
        if name.endswith(".act_fn.beta"):
            self._beta[bid] = data_torch.to("cpu").float().item()
            if (len(self._beta) == n_layers):
                self.gguf_writer.add_xielu_beta([self._beta[k] for k in sorted(self._beta)])
            return
        if name.endswith(".act_fn.eps"):
            self._eps[bid] = data_torch.to("cpu").float().item()
            if (len(self._eps) == n_layers):
                self.gguf_writer.add_xielu_eps([self._eps[k] for k in sorted(self._eps)])
            return

        yield from super().modify_tensors(data_torch, name, bid)


class MistralModel(LlamaModel):
    model_arch = gguf.MODEL_ARCH.MISTRAL3
    model_name = "Mistral"
    hf_arch = ""
    is_mistral_format = True
    undo_permute = False

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        # for compatibility, we use LLAMA arch for older models
        # TODO: remove this once everyone migrates to newer version of llama.cpp
        if "llama_4_scaling" not in self.hparams:
            self.model_arch = gguf.MODEL_ARCH.LLAMA
            self.gguf_writer.arch = gguf.MODEL_ARCH_NAMES[self.model_arch]
            self.gguf_writer.add_architecture()
            self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)

    def dequant_model(self):
        # transform quantization config into HF format
        quant_config = self.hparams.get("quantization")
        if quant_config is not None:
            assert quant_config["qformat_weight"] == "fp8_e4m3"
            self.hparams["quantization_config"] = {
                "activation_scheme": "static",
                "quant_method": "fp8",
                "weight_block_size": None,
            }
        return super().dequant_model()

    @staticmethod
    def get_community_chat_template(vocab: MistralVocab, templates_dir: Path, is_mistral_format: bool):
        assert TokenizerVersion is not None and Tekkenizer is not None and SentencePieceTokenizer is not None, _mistral_import_error_msg
        assert isinstance(vocab.tokenizer, (Tekkenizer, SentencePieceTokenizer)), (
            f"Expected Tekkenizer or SentencePieceTokenizer, got {type(vocab.tokenizer)}"
        )

        if vocab.tokenizer.version == TokenizerVersion.v1:
            return "mistral-v1"
        elif vocab.tokenizer.version == TokenizerVersion.v3 and vocab.tokenizer_type == MistralTokenizerType.spm:
            return "mistral-v3"
        elif vocab.tokenizer.version == TokenizerVersion.v3 and vocab.tokenizer_type == MistralTokenizerType.tekken:
            return "mistral-v3-tekken"
        elif vocab.tokenizer.version == TokenizerVersion.v7 and vocab.tokenizer_type == MistralTokenizerType.spm:
            return "mistral-v7"
        elif vocab.tokenizer.version == TokenizerVersion.v7 and vocab.tokenizer_type == MistralTokenizerType.tekken:
            return "mistral-v7-tekken"
        elif vocab.tokenizer.version == TokenizerVersion.v11:
            template_file = "Mistral-Small-3.2-24B-Instruct-2506.jinja"
        elif vocab.tokenizer.version == TokenizerVersion.v13:
            template_file = "unsloth-mistral-Devstral-Small-2507.jinja"
        else:
            err_message = f"Unknown tokenizer type: {vocab.tokenizer_type} and version {vocab.tokenizer.version}"
            if is_mistral_format:
                err_message += (
                    " . Please pass --disable-mistral-community-chat-template argument to the CLI "
                    "if you want to skip this error and use the Mistral official `mistral-common` pre-processing library."
                )
            raise ValueError(err_message)

        template_path = templates_dir / template_file
        if not template_path.exists():
            raise FileNotFoundError(f"Template file not found: {template_path}")

        with open(template_path, "r", encoding="utf-8") as f:
            template = f.read()

        return template

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        MistralModel.set_mistral_config(self.gguf_writer, self.hparams)

    @staticmethod
    def set_mistral_config(gguf_writer: gguf.GGUFWriter, hparams: dict):
        if "yarn" in hparams:
            yarn_params = hparams["yarn"]
            mscale_all_dim = 1.0 if not yarn_params["apply_scale"] else 0.0
            gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
            gguf_writer.add_rope_scaling_factor(yarn_params["factor"])
            gguf_writer.add_rope_scaling_yarn_beta_fast(yarn_params["beta"])
            gguf_writer.add_rope_scaling_yarn_beta_slow(yarn_params["alpha"])
            gguf_writer.add_rope_scaling_yarn_log_mul(mscale_all_dim)
            gguf_writer.add_rope_scaling_orig_ctx_len(yarn_params["original_max_position_embeddings"])

        if "llama_4_scaling" in hparams:
            gguf_writer.add_attn_temperature_scale(hparams["llama_4_scaling"]["beta"])


class MistralMoeModel(DeepseekV2Model):
    model_arch = gguf.MODEL_ARCH.DEEPSEEK2
    model_name = "Mistral"
    hf_arch = ""
    is_mistral_format = True

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        logger.info("Using MistralMoeModel")
        # remap hparams from Mistral MoE format to DeepseekV2 format
        # we do this way to be able to reuse DeepseekV2Model set_gguf_parameters logic
        # ref: https://github.com/vllm-project/vllm/blob/b294e28db2c5dee61bc25157664edcada8b90b31/vllm/transformers_utils/configs/mistral.py
        config = self.hparams
        # Mistral key -> HF key
        config_mapping = {
            "dim": "hidden_size",
            "norm_eps": "rms_norm_eps",
            "n_kv_heads": "num_key_value_heads",
            "n_layers": "num_hidden_layers",
            "n_heads": "num_attention_heads",
            "hidden_dim": "intermediate_size",
        }
        # HF key -> (Mistral key, default value)
        top_level_mapping_with_default = {
            "model_type": ("model_type", "transformer"),
            "hidden_act": ("activation", "silu"),
            "tie_word_embeddings": ("tied_embeddings", False),
            "max_seq_len": ("max_seq_len", config.get("max_position_embeddings", 128_000)),
            "max_position_embeddings": ("max_position_embeddings", 128_000),
        }
        # mapping top-level keys
        for key, new_key in config_mapping.items():
            if key in config:
                config[new_key] = config[key]
        for new_key, (key, default_value) in top_level_mapping_with_default.items():
            config[new_key] = config.get(key, default_value)
        # mapping MoE-specific keys
        moe_config_map = {
            "route_every_n": "moe_layer_freq",
            "first_k_dense_replace": "first_k_dense_replace",
            "num_experts_per_tok": "num_experts_per_tok",
            "num_experts": "n_routed_experts",
            "expert_hidden_dim": "moe_intermediate_size",
            "routed_scale": "routed_scaling_factor",
            "num_shared_experts": "n_shared_experts",
            "num_expert_groups": "n_group",
            "num_expert_groups_per_tok": "topk_group",
        }
        moe = config["moe"]
        for key, new_key in moe_config_map.items():
            if key in moe:
                config[new_key] = moe[key]
        # provide missing values
        config["topk_method"] = None
        config["norm_topk_prob"] = True
        config["scoring_func"] = "softmax"

    def set_vocab(self):
        self._set_vocab_mistral()

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        MistralModel.set_mistral_config(self.gguf_writer, self.hparams)
        yarn_params = self.hparams["yarn"]
        self.gguf_writer.add_attn_temperature_length(yarn_params["original_max_position_embeddings"])

        # [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX]
        # note: for legacy reasons, this is not consistent with the other usages of self.gguf_writer.add_rope_scaling_yarn_log_mul
        # ref https://github.com/ggml-org/llama.cpp/pull/17945
        self.gguf_writer.add_rope_scaling_yarn_log_mul(0.1) # mscale_all_dim * 0.1

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        # rename certain tensors so that we can reuse DeepseekV2Model modify_tensors logic
        if name.endswith(".qscale_act"):
            name = name.replace(".qscale_act", ".input_scale")
        if name.endswith(".qscale_weight"):
            name = name.replace(".qscale_weight", ".weight_scale")
        if ".wkv_b." in name:
            name = name.replace(".wkv_b.", ".kv_b_proj.")
        if ".experts." in name:
            name = name.replace(".experts.", ".mlp.experts.")
            name = name.replace(".w1.", ".gate_proj.")
            name = name.replace(".w2.", ".down_proj.")
            name = name.replace(".w3.", ".up_proj.")
            name = "model." + name

        return super().filter_tensors((name, gen))


class PixtralModel(LlavaVisionModel):
    model_name = "Pixtral"
    hf_arch = ""
    is_mistral_format = True

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.PIXTRAL)

        self.gguf_writer.add_vision_attention_layernorm_eps(
            self.find_hparam(["norm_eps"])
        )
        self.gguf_writer.add_rope_freq_base(self.find_vparam(["rope_theta"]))

        self.gguf_writer.add_vision_use_silu(True)

        # spatial_merge_size
        if self.find_vparam(["mm_projector_id"], optional=True) == "patch_merge":
            self.gguf_writer.add_vision_spatial_merge_size(
                self.find_vparam(["spatial_merge_size"])
            )

    def map_tensor_name(self, name: str, try_suffixes: Sequence[str] = (".weight", ".bias")) -> str:
        if name == "vision_language_adapter.w_in.weight":
            return "mm.1.weight"
        elif name == "vision_language_adapter.w_in.bias":
            return "mm.1.bias"
        elif name == "vision_language_adapter.w_out.weight":
            return "mm.2.weight"
        elif name == "vision_language_adapter.w_out.bias":
            return "mm.2.bias"
        return super().map_tensor_name(name, try_suffixes)


@ModelBase.register("LightOnOCRForConditionalGeneration")
class LightOnOCRVisionModel(LlavaVisionModel):
    is_mistral_format = False
    use_break_tok = False

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.LIGHTONOCR)

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        name = name.replace("model.vision_encoder.", "vision_tower.")
        name = name.replace("model.vision_projection.", "multi_modal_projector.")

        return super().filter_tensors((name, gen))


@ModelBase.register("KimiVLForConditionalGeneration")
class KimiVLModel(MmprojModel):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        assert self.hparams_vision is not None
        self.hparams_vision["image_size"] = 64 * 14 # for compatibility

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.KIMIVL)
        self.gguf_writer.add_vision_use_gelu(True)
        self.gguf_writer.add_vision_projector_scale_factor(2)
        # eps is the same as pytorch's default value
        assert self.hparams_vision is not None
        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams_vision.get("layer_norm_eps", 1e-5))

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        is_vision_tensor = "vision_tower" in name or "multi_modal_projector" in name

        if not is_vision_tensor:
            return None

        return super().filter_tensors(item)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        if "pos_emb.weight" in name:
            data_torch = data_torch.view(data_torch.shape[0] * data_torch.shape[1], data_torch.shape[2])

        if "wqkv" in name:
            split_dim = 0 if "weight" in name else -1
            wq, wk, wv = data_torch.chunk(3, dim=split_dim)
            yield from super().modify_tensors(wq, name.replace("wqkv", "wq"), bid)
            yield from super().modify_tensors(wk, name.replace("wqkv", "wk"), bid)
            yield from super().modify_tensors(wv, name.replace("wqkv", "wv"), bid)
        else:
            yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("KimiK25ForConditionalGeneration")
class KimiK25Model(MmprojModel):
    """Kimi-K2.5 with MoonViT3d vision encoder"""

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

        assert self.hparams_vision is not None, "Kimi-K2.5 requires vision_config in model config"

        self.merge_kernel_size = tuple(self.hparams_vision.get("merge_kernel_size", [2, 2]))
        self.patch_size = self.hparams_vision.get("patch_size", 14)

        # Set image_size for compatibility with base class
        # Use position embedding dimensions as image_size reference
        pos_emb_h = self.hparams_vision.get("init_pos_emb_height", 64)
        self.hparams_vision["image_size"] = pos_emb_h * self.patch_size

    def set_gguf_parameters(self):
        # Base class MmprojModel.set_gguf_parameters() already writes:
        # - vision_block_count, vision_head_count, vision_embedding_length
        # - vision_feed_forward_length, vision_patch_size, image_mean, image_std
        # via find_vparam() which handles the vt_* prefixed keys in Kimi-K2.5's config
        super().set_gguf_parameters()
        assert self.hparams_vision is not None

        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.KIMIK25)

        # Position embedding parameters (for interpolation)
        self.gguf_writer.add_uint32("vision.pos_emb_height", self.hparams_vision.get("init_pos_emb_height", 64))
        self.gguf_writer.add_uint32("vision.pos_emb_width", self.hparams_vision.get("init_pos_emb_width", 64))
        self.gguf_writer.add_uint32("vision.pos_emb_time", self.hparams_vision.get("init_pos_emb_time", 4))

        # Projector parameters
        self.gguf_writer.add_vision_use_gelu(self.hparams_vision.get("projector_hidden_act", "gelu") == "gelu")
        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams_vision.get("projector_ln_eps", 1e-5))
        self.gguf_writer.add_vision_projector_scale_factor(self.merge_kernel_size[0])

        # Image size limits
        # Note: in_patch_limit is for images, in_patch_limit_each_frame is for video (not supported yet)
        in_patch_limit = self.preprocessor_config.get("in_patch_limit", 16384)
        min_patches = 8  # reasonable minimum
        pixels_per_patch = self.patch_size ** 2
        self.gguf_writer.add_vision_min_pixels(min_patches * pixels_per_patch)
        self.gguf_writer.add_vision_max_pixels(in_patch_limit * pixels_per_patch)

    @staticmethod
    def permute(weights: Tensor, n_head: int) -> Tensor:
        out_dim, in_dim = weights.shape
        head_dim = out_dim // n_head
        w = weights.reshape(n_head, head_dim // 4, 2, 2, in_dim)
        w = w.permute(0, 2, 1, 3, 4)
        return w.reshape(out_dim, in_dim)

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        # Only process vision and projector tensors
        is_vision = any(x in name for x in ["vision_tower", "mm_projector"])

        if not is_vision:
            return None

        return super().filter_tensors(item)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        assert self.hparams_vision is not None
        n_head = self.hparams_vision.get("num_attention_heads", 16)

        # Permute Q/K weights/biases from interleaved to split RoPE format
        # This allows using build_rope_2d at runtime without post-permutation.
        if "wqkv" in name:
            out_dim = data_torch.shape[0]
            qkv_dim = out_dim // 3
            head_dim = qkv_dim // n_head

            if "weight" in name:
                wq, wk, wv = data_torch[:qkv_dim, :], data_torch[qkv_dim:2 * qkv_dim, :], data_torch[2 * qkv_dim:, :]
                wq = self.permute(wq, n_head)
                wk = self.permute(wk, n_head)
                data_torch = torch.cat([wq, wk, wv], dim=0)
            elif "bias" in name:
                bq, bk, bv = data_torch[:qkv_dim], data_torch[qkv_dim:2 * qkv_dim], data_torch[2 * qkv_dim:]
                bq = bq.reshape(n_head, head_dim // 4, 2, 2).permute(0, 2, 1, 3).reshape(-1)
                bk = bk.reshape(n_head, head_dim // 4, 2, 2).permute(0, 2, 1, 3).reshape(-1)
                data_torch = torch.cat([bq, bk, bv], dim=0)

        # Temporal embeddings: (T, 1, C) → (T, C)
        if "pos_emb.time_weight" in name:
            T, _, C = data_torch.shape
            data_torch = data_torch.reshape(T, C)

        # PatchMergerMLP tensor name mapping
        # proj.0.weight → proj.linear_1.weight
        # proj.2.weight → proj.linear_2.weight
        if "mm_projector.proj.0." in name:
            name = name.replace(".proj.0.", ".proj.linear_1.")
        elif "mm_projector.proj.2." in name:
            name = name.replace(".proj.2.", ".proj.linear_2.")

        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("CogVLMForCausalLM")
class CogVLMVisionModel(MmprojModel):

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams.get("layer_norm_eps", 1e-6))
        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.COGVLM)

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        if not name.startswith("model.vision."):
            return None

        return super().filter_tensors(item)


@ModelBase.register("CogVLMForCausalLM")
class CogVLMModel(LlamaModel):
    model_arch = gguf.MODEL_ARCH.COGVLM


@ModelBase.register("JanusForConditionalGeneration")
class JanusProModel(LlamaModel):
    model_arch = gguf.MODEL_ARCH.LLAMA  # reuse Llama arch

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        # Skip vision, aligner, and generation tensors
        skip_prefixes = (
            'model.vision_model.',
            'model.aligner.',
            'model.vqmodel.',
            'model.generation_embeddings.',
            'model.generation_aligner.',
            'model.generation_head.',
        )
        if name.startswith(skip_prefixes):
            return None

        return super().filter_tensors(item)


@ModelBase.register("JanusForConditionalGeneration")
class JanusProVisionModel(MmprojModel):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        assert self.hparams_vision is not None
        if "intermediate_size" not in self.hparams_vision:
            mlp_ratio = self.hparams_vision.get("mlp_ratio")
            hidden_size = self.hparams_vision.get("hidden_size")
            if mlp_ratio is not None and hidden_size is not None:
                self.hparams_vision["intermediate_size"] = int(round(hidden_size * mlp_ratio))

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        assert self.hparams_vision is not None

        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.JANUS_PRO)

        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams_vision.get("layer_norm_eps", 1e-6))

        hidden_act = str(self.hparams_vision.get("hidden_act", "")).lower()
        if hidden_act == "gelu":
            self.gguf_writer.add_vision_use_gelu(True)
        elif hidden_act == "silu":
            self.gguf_writer.add_vision_use_silu(True)

    def _map_aligner_tensor(self, data_torch: Tensor, name: str) -> Iterable[tuple[str, Tensor]]:
        """Map aligner tensors to projector format"""
        suffix = ".bias" if name.endswith(".bias") else ".weight"

        if name.startswith("model.aligner."):
            local_name = name[len("model.aligner."):]
        elif name.startswith("aligner."):
            local_name = name[len("aligner."):]
        else:
            raise ValueError(f"Unsupported Janus aligner prefix: {name}")

        if local_name.startswith("fc1."):
            mm_index = 0
        elif local_name.startswith("hidden_layers."):
            parts = local_name.split(".", 2)
            if len(parts) < 3:
                raise ValueError(f"Unexpected Janus aligner tensor name: {name}")
            mm_index = int(parts[1]) + 1
        else:
            raise ValueError(f"Unsupported Janus aligner tensor: {name}")

        tensor_name = self.format_tensor_name(gguf.MODEL_TENSOR.V_MMPROJ, mm_index, suffix=suffix)
        return [(tensor_name, data_torch)]

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        # Skip generation-related components
        skip_generation_prefixes = (
            'model.vqmodel.',
            'vqmodel.',
            'model.generation_embeddings.',
            'generation_embeddings.',
            'model.generation_aligner.',
            'generation_aligner.',
            'model.generation_head.',
            'generation_head.',
        )
        if name.startswith(skip_generation_prefixes):
            return None

        return super().filter_tensors(item)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        # Handle aligner tensors
        if name.startswith(('model.aligner.', 'aligner.')):
            yield from self._map_aligner_tensor(data_torch, name)
            return

        # Handle vision tensors
        if name.startswith(('model.vision_model.', 'vision_model.')):
            yield from super().modify_tensors(data_torch, name, bid)
            return

        return


@ModelBase.register("YoutuVLForConditionalGeneration")
class YoutuVLVisionModel(MmprojModel):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        assert self.hparams_vision is not None
        self.hparams_vision["image_size"] = self.hparams_vision.get("image_size", 560)

    def set_gguf_parameters(self):
        super().set_gguf_parameters()

        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.YOUTUVL)
        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams.get("layer_norm_eps", 1e-6))

        # Handle activation function
        hidden_act = str(self.hparams.get("hidden_act", "gelu_pytorch_tanh")).lower()
        if hidden_act in ("gelu", "gelu_pytorch_tanh", "gelu_fast", "gelu_new", "gelu_accurate"):
            self.gguf_writer.add_vision_use_gelu(True)
        elif hidden_act == "silu":
            self.gguf_writer.add_vision_use_silu(True)
        else:
            raise ValueError(f"Unsupported activation function for YOUTUVL: {hidden_act}")

        self.gguf_writer.add_vision_spatial_merge_size(self.hparams.get("spatial_merge_size", 2))

        window_size = self.hparams.get("window_size")
        if window_size is not None:
            self.gguf_writer.add_vision_window_size(window_size)
        # fullatt_block_indexes contains explicit layer indices that use full attention
        # e.g., [2, 5, 8, 11] means layers 2, 5, 8, 11 use full attention
        # All other layers use window attention
        fullatt_block_indexes = self.hparams.get("fullatt_block_indexes")
        assert fullatt_block_indexes is not None, "fullatt_block_indexes is required for youtuvl"
        # Store the explicit layer indices for YoutuVL (irregular pattern approach)
        self.gguf_writer.add_vision_wa_layer_indexes(layers=fullatt_block_indexes)

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        # Skip language model tensors
        skip_prefixes = ('lm_head.', 'model.layers.', 'model.embed_tokens.', 'model.norm.')
        if name.startswith(skip_prefixes):
            return None

        return super().filter_tensors(item)

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        # Try to map the tensor using TensorNameMap (handles vision encoder and projector)
        try:
            yield from super().modify_tensors(data_torch, name, bid)
        except ValueError:
            # If mapping fails, log warning and skip
            logger.warning(f"Cannot map tensor: {name}")
            return


@ModelBase.register("SolarOpenForCausalLM")
class SolarOpenModel(Glm4MoeModel):
    model_arch = gguf.MODEL_ARCH.GLM4_MOE

    def set_vocab(self):
        from transformers import AutoTokenizer
        tokenizer = AutoTokenizer.from_pretrained(self.dir_model)
        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
        tokens, toktypes, tokpre = self.get_vocab_base()
        self.gguf_writer.add_tokenizer_model("gpt2")
        self.gguf_writer.add_tokenizer_pre(tokpre)
        self.gguf_writer.add_token_list(tokens)
        self.gguf_writer.add_token_types(toktypes)
        special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
        special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
        special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<unk>"])  # ty: ignore[unresolved-attribute]
        special_vocab._set_special_token("bos", tokenizer.get_added_vocab()["<|startoftext|>"])  # ty: ignore[unresolved-attribute]
        special_vocab.add_to_gguf(self.gguf_writer)


@ModelBase.register("DotsOCRForCausalLM")
class DotsOCRVisionModel(MmprojModel):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        assert self.hparams_vision is not None
        self.hparams_vision["image_size"] = 0 # dynamic resolution

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.DOTSOCR)
        self.gguf_writer.add_vision_min_pixels(self.preprocessor_config["min_pixels"])
        self.gguf_writer.add_vision_max_pixels(self.preprocessor_config["max_pixels"])
        self.gguf_writer.add_vision_attention_layernorm_eps(self.find_vparam(["rms_norm_eps"]))
        self.gguf_writer.add_vision_projector_scale_factor(self.find_vparam(["spatial_merge_size"]))
        self.gguf_writer.add_vision_use_silu(True)

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

        if not name.startswith("vision_tower."):
            return None

        if "vision_tower.blocks." in name and ".mlp." in name:
            # note: to avoid naming conflicts in tensor_mapping.py, we need to handle FFN renaming here
            # x = F.silu(self.fc1(x)) * self.fc3(x)
            # x = self.fc2(x)
            # fc1 -> gate, fc2 -> down, fc3 -> up
            # mapping original names to Qwen2.5 naming scheme
            name = name.replace("vision_tower.blocks.", "visual.blocks.")
            name = name.replace(".fc1", ".gate_proj")
            name = name.replace(".fc2", ".down_proj")
            name = name.replace(".fc3", ".up_proj")

        return super().filter_tensors((name, gen))

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        yield from super().modify_tensors(data_torch, name, bid)


@ModelBase.register("Sarashina2VisionForCausalLM")
class Sarashina2VLTextModel(LlamaModel):
    model_arch = gguf.MODEL_ARCH.LLAMA

    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item
        if name.startswith("llm."):
            name = name.replace("llm.", "", 1)
        elif name.startswith("norm."):
            return None
        return super().filter_tensors((name, gen))


@ModelBase.register("Sarashina2VisionForCausalLM")
class Sarashina2VLVisionModel(Qwen2VLVisionModel):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.global_config['model_type'] = "qwen2_vl"


###### CONVERSION LOGIC ######


# tree of lazy tensors
class LazyTorchTensor(gguf.LazyBase):
    _tensor_type = torch.Tensor
    # to keep the type-checker happy
    dtype: torch.dtype
    shape: torch.Size

    # only used when converting a torch.Tensor to a np.ndarray
    _dtype_map: dict[torch.dtype, type] = {
        torch.float16: np.float16,
        torch.float32: np.float32,
        torch.uint8: np.uint8,
    }

    # only used when byteswapping data. Only correct size is needed
    # TODO: uncomment uint64, uint32, and uint16, ref: https://github.com/pytorch/pytorch/issues/58734
    _dtype_byteswap_map: dict[torch.dtype, type] = {
        torch.float64: np.float64,
        torch.float32: np.float32,
        torch.bfloat16: np.float16,
        torch.float16: np.float16,
        torch.int64: np.int64,
        # torch.uint64: np.uint64,
        torch.int32: np.int32,
        # torch.uint32: np.uint32,
        torch.int16: np.int16,
        # torch.uint16: np.uint16,
        torch.int8: np.int8,
        torch.uint8: np.uint8,
        torch.bool: np.uint8,
        torch.float8_e4m3fn: np.uint8,
        torch.float8_e5m2: np.uint8,
    }

    # used for safetensors slices
    # ref: https://github.com/huggingface/safetensors/blob/079781fd0dc455ba0fe851e2b4507c33d0c0d407/bindings/python/src/lib.rs#L1046
    # TODO: uncomment U64, U32, and U16, ref: https://github.com/pytorch/pytorch/issues/58734
    _dtype_str_map: dict[str, torch.dtype] = {
        "F64": torch.float64,
        "F32": torch.float32,
        "BF16": torch.bfloat16,
        "F16": torch.float16,
        # "U64": torch.uint64,
        "I64": torch.int64,
        # "U32": torch.uint32,
        "I32": torch.int32,
        # "U16": torch.uint16,
        "I16": torch.int16,
        "U8": torch.uint8,
        "I8": torch.int8,
        "BOOL": torch.bool,
        "F8_E4M3": torch.float8_e4m3fn,
        "F8_E5M2": torch.float8_e5m2,
    }

    def numpy(self) -> gguf.LazyNumpyTensor:
        dtype = self._dtype_map[self.dtype]
        return gguf.LazyNumpyTensor(
            meta=gguf.LazyNumpyTensor.meta_with_dtype_and_shape(dtype, self.shape),
            args=(self,),
            func=(lambda s: s.numpy())
        )

    @classmethod
    def meta_with_dtype_and_shape(cls, dtype: torch.dtype, shape: tuple[int, ...]) -> Tensor:
        return torch.empty(size=shape, dtype=dtype, device="meta")

    @classmethod
    def from_safetensors_slice(cls, st_slice: Any) -> Tensor:
        dtype = cls._dtype_str_map[st_slice.get_dtype()]
        shape: tuple[int, ...] = tuple(st_slice.get_shape())
        lazy = cls(meta=cls.meta_with_dtype_and_shape(dtype, shape), args=(st_slice,), func=lambda s: s[...] if len(s.get_shape()) == 0 else s[:])
        return cast(torch.Tensor, lazy)

    @classmethod
    def from_local_tensor(cls, t: gguf.utility.LocalTensor) -> Tensor:
        def load_tensor(tensor: gguf.utility.LocalTensor) -> Tensor:
            def byteswap_tensor(tensor: np.ndarray, dtype: type) -> np.ndarray:
                if sys.byteorder == 'big':
                    # switch data back to big endian
                    tensor = tensor.view(dtype).byteswap(inplace=False)
                return tensor
            dtype = cls._dtype_str_map[tensor.dtype]
            numpy_dtype = cls._dtype_byteswap_map[dtype]
            return torch.from_numpy(byteswap_tensor(tensor.mmap_bytes(), numpy_dtype)).view(dtype).reshape(tensor.shape)
        dtype = cls._dtype_str_map[t.dtype]
        shape = t.shape
        lazy = cls(meta=cls.meta_with_dtype_and_shape(dtype, shape), args=(t,), func=lambda r: load_tensor(r))
        return cast(torch.Tensor, lazy)

    @classmethod
    def from_remote_tensor(cls, remote_tensor: gguf.utility.RemoteTensor):
        def byteswap_tensor(tensor: np.ndarray, dtype: type) -> np.ndarray:
            if sys.byteorder == 'big':
                # switch data back to big endian
                tensor = tensor.view(dtype).byteswap(inplace=False)
            return tensor
        dtype = cls._dtype_str_map[remote_tensor.dtype]
        numpy_dtype = cls._dtype_byteswap_map[dtype]
        shape = remote_tensor.shape
        meta = cls.meta_with_dtype_and_shape(dtype, shape)
        lazy = cls(meta=meta, args=(remote_tensor,), func=lambda r: torch.from_numpy(byteswap_tensor(np.frombuffer(r.data(), dtype=numpy_dtype), numpy_dtype)).view(dtype).reshape(shape))
        return cast(torch.Tensor, lazy)

    @classmethod
    def __torch_function__(cls, func, types, args=(), kwargs=None):
        del types  # unused

        if kwargs is None:
            kwargs = {}

        if func is torch.Tensor.numpy:
            assert len(args)
            return args[0].numpy()

        return cls._wrap_fn(func)(*args, **kwargs)


def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser(
        description="Convert a huggingface model to a GGML compatible file")
    parser.add_argument(
        "--vocab-only", action="store_true",
        help="extract only the vocab",
    )
    parser.add_argument(
        "--outfile", type=Path,
        help="path to write to; default: based on input. {ftype} will be replaced by the outtype.",
    )
    parser.add_argument(
        "--outtype", type=str, choices=["f32", "f16", "bf16", "q8_0", "tq1_0", "tq2_0", "auto"], default="auto",
        help="output format - use f32 for float32, f16 for float16, bf16 for bfloat16, q8_0 for Q8_0, tq1_0 or tq2_0 for ternary, and auto for the highest-fidelity 16-bit float type",
    )
    parser.add_argument(
        "--bigendian", action="store_true",
        help="model is executed on big endian machine",
    )
    parser.add_argument(
        "model", type=str,
        help="directory containing model file or huggingface repository ID (if --remote)",
        nargs="?",
    )
    parser.add_argument(
        "--use-temp-file", action="store_true",
        help="use the tempfile library while processing (helpful when running out of memory, process killed)",
    )
    parser.add_argument(
        "--no-lazy", action="store_true",
        help="use more RAM by computing all outputs before writing (use in case lazy evaluation is broken)",
    )
    parser.add_argument(
        "--model-name", type=str, default=None,
        help="name of the model",
    )
    parser.add_argument(
        "--verbose", action="store_true",
        help="increase output verbosity",
    )
    parser.add_argument(
        "--split-max-tensors", type=int, default=0,
        help="max tensors in each split",
    )
    parser.add_argument(
        "--split-max-size", type=str, default="0",
        help="max size per split N(M|G)",
    )
    parser.add_argument(
        "--dry-run", action="store_true",
        help="only print out a split plan and exit, without writing any new files",
    )
    parser.add_argument(
        "--no-tensor-first-split", action="store_true",
        help="do not add tensors to the first split (disabled by default)"
    )
    parser.add_argument(
        "--metadata", type=Path,
        help="Specify the path for an authorship metadata override file"
    )
    parser.add_argument(
        "--print-supported-models", action="store_true",
        help="Print the supported models"
    )
    parser.add_argument(
        "--remote", action="store_true",
        help="(Experimental) Read safetensors file remotely without downloading to disk. Config and tokenizer files will still be downloaded. To use this feature, you need to specify Hugging Face model repo name instead of a local directory. For example: 'HuggingFaceTB/SmolLM2-1.7B-Instruct'. Note: To access gated repo, set HF_TOKEN environment variable to your Hugging Face token.",
    )
    parser.add_argument(
        "--mmproj", action="store_true",
        help="(Experimental) Export multimodal projector (mmproj) for vision models. This will only work on some vision models. A prefix 'mmproj-' will be added to the output file name.",
    )
    parser.add_argument(
        "--mistral-format", action="store_true",
        help="Whether the model is stored following the Mistral format.",
    )
    parser.add_argument(
        "--disable-mistral-community-chat-template", action="store_true",
        help=(
            "Whether to disable usage of Mistral community chat templates. If set, use the Mistral official `mistral-common` library for tokenization and detokenization of Mistral models. "
            "Using `mistral-common` ensure correctness and zero-day support of tokenization for models converted from the Mistral format but requires to manually setup the tokenization server."
        )
    )

    parser.add_argument(
        "--sentence-transformers-dense-modules", action="store_true",
        help=("Whether to include sentence-transformers dense modules. "
              "It can be used for sentence-transformers models, like google/embeddinggemma-300m. "
              "Default these modules are not included.")
    )

    parser.add_argument(
        "--fuse-gate-up-exps", action="store_true",
        help="Fuse gate_exps and up_exps tensors into a single gate_up_exps tensor for MoE models.",
    )

    args = parser.parse_args()
    if not args.print_supported_models and args.model is None:
        parser.error("the following arguments are required: model")
    return args


def split_str_to_n_bytes(split_str: str) -> int:
    if split_str.endswith("K"):
        n = int(split_str[:-1]) * 1000
    elif split_str.endswith("M"):
        n = int(split_str[:-1]) * 1000 * 1000
    elif split_str.endswith("G"):
        n = int(split_str[:-1]) * 1000 * 1000 * 1000
    elif split_str.isnumeric():
        n = int(split_str)
    else:
        raise ValueError(f"Invalid split size: {split_str}, must be a number, optionally followed by K, M, or G")

    if n < 0:
        raise ValueError(f"Invalid split size: {split_str}, must be positive")

    return n


def get_model_architecture(hparams: dict[str, Any], model_type: ModelType) -> str:
    # TODO @ngxson : this won't work correctly if the model has both audio & vision encoders
    # maybe we should fallback to text model's arch in that case, since not many models have both
    text_config = hparams.get("text_config", {})
    vision_config = hparams.get("vision_config", {})
    arch = None
    if (arches := hparams.get("architectures")) is not None and len(arches) > 0:
        arch = arches[0]
    elif "ssm_cfg" in hparams:
        # For non-hf Mamba and Mamba2 models
        arch = hparams["ssm_cfg"].get("layer", "Mamba") + "ForCausalLM"

    # Step3-VL keeps text config under text_config but uses a custom top-level architecture.
    # For text conversion we route to a dedicated text-only class.
    # TODO: refactor this later to avoid adding exception here
    if model_type == ModelType.TEXT and arch in ("StepVLForConditionalGeneration", "Sarashina2VisionForCausalLM"):
        return arch

    # if "architectures" is found in the sub-config, use that instead
    if model_type == ModelType.TEXT and text_config.get("architectures") is not None:
        arch = text_config["architectures"][0]
    elif model_type == ModelType.MMPROJ and vision_config.get("architectures") is not None:
        arch = vision_config["architectures"][0]
    if arch is None:
        raise ValueError("Failed to detect model architecture")
    return arch


def main() -> None:
    args = parse_args()

    if args.print_supported_models:
        logger.error("Supported models:")
        ModelBase.print_registered_models()
        sys.exit(0)

    if args.verbose:
        logging.basicConfig(level=logging.DEBUG)
    else:
        logging.basicConfig(level=logging.INFO)

    if args.remote:
        hf_repo_id = args.model
        from huggingface_hub import snapshot_download
        allowed_patterns = ["LICENSE", "*.json", "*.md", "*.txt", "tokenizer.model"]
        if args.sentence_transformers_dense_modules:
            # include sentence-transformers dense modules safetensors files
            allowed_patterns.append("*.safetensors")
        local_dir = snapshot_download(
            repo_id=hf_repo_id,
            allow_patterns=allowed_patterns)
        dir_model = Path(local_dir)
        logger.info(f"Downloaded config and tokenizer to {local_dir}")
    else:
        hf_repo_id = None
        dir_model = Path(args.model)

    if not dir_model.is_dir():
        logger.error(f'Error: {dir_model} is not a directory')
        sys.exit(1)

    ftype_map: dict[str, gguf.LlamaFileType] = {
        "f32": gguf.LlamaFileType.ALL_F32,
        "f16": gguf.LlamaFileType.MOSTLY_F16,
        "bf16": gguf.LlamaFileType.MOSTLY_BF16,
        "q8_0": gguf.LlamaFileType.MOSTLY_Q8_0,
        "tq1_0": gguf.LlamaFileType.MOSTLY_TQ1_0,
        "tq2_0": gguf.LlamaFileType.MOSTLY_TQ2_0,
        "auto": gguf.LlamaFileType.GUESSED,
    }

    is_split = args.split_max_tensors > 0 or args.split_max_size != "0"
    if args.use_temp_file and is_split:
        logger.error("Error: Cannot use temp file when splitting")
        sys.exit(1)

    if args.outfile is not None:
        fname_out = args.outfile
    elif hf_repo_id:
        # if remote, use the model ID as the output file name
        fname_out = Path("./" + hf_repo_id.replace("/", "-") + "-{ftype}.gguf")
    else:
        fname_out = dir_model

    logger.info(f"Loading model: {dir_model.name}")

    is_mistral_format = args.mistral_format
    if is_mistral_format and not _mistral_common_installed:
        raise ImportError(_mistral_import_error_msg)
    disable_mistral_community_chat_template = args.disable_mistral_community_chat_template

    with torch.inference_mode():
        output_type = ftype_map[args.outtype]
        model_type = ModelType.MMPROJ if args.mmproj else ModelType.TEXT
        hparams = ModelBase.load_hparams(dir_model, is_mistral_format)
        if not is_mistral_format:
            model_architecture = get_model_architecture(hparams, model_type)
            logger.info(f"Model architecture: {model_architecture}")
            try:
                model_class = ModelBase.from_model_architecture(model_architecture, model_type=model_type)
            except NotImplementedError:
                logger.error(f"Model {model_architecture} is not supported")
                sys.exit(1)
        elif args.mmproj:
            assert hparams.get("vision_encoder") is not None, "This model does not support multimodal"
            model_class = PixtralModel
        elif "moe" in hparams:
            model_class = MistralMoeModel
        else:
            model_class = MistralModel

        model_instance = model_class(dir_model, output_type, fname_out,
                                     is_big_endian=args.bigendian, use_temp_file=args.use_temp_file,
                                     eager=args.no_lazy,
                                     metadata_override=args.metadata, model_name=args.model_name,
                                     split_max_tensors=args.split_max_tensors,
                                     split_max_size=split_str_to_n_bytes(args.split_max_size), dry_run=args.dry_run,
                                     small_first_shard=args.no_tensor_first_split,
                                     remote_hf_model_id=hf_repo_id, disable_mistral_community_chat_template=disable_mistral_community_chat_template,
                                     sentence_transformers_dense_modules=args.sentence_transformers_dense_modules,
                                     fuse_gate_up_exps=args.fuse_gate_up_exps
                                     )

        if args.vocab_only:
            logger.info("Exporting model vocab...")
            model_instance.write_vocab()
            logger.info(f"Model vocab successfully exported to {model_instance.fname_out}")
        else:
            logger.info("Exporting model...")
            model_instance.write()
            out_path = f"{model_instance.fname_out.parent}{os.sep}" if is_split else model_instance.fname_out
            logger.info(f"Model successfully exported to {out_path}")


if __name__ == '__main__':
    main()
