AirLLM

1. Install package

First, install the airllm pip package.

pip install airllm

Quickstart

Example Python Notebook

Example colabs here:

<a target="_blank" href="https://colab.research.google.com/github/lyogavin/airllm/blob/main/air_llm/examples/run_all_types_of_models.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>

example of other models (ChatGLM, QWen, Baichuan, Mistral, etc):

<details>

• ChatGLM:

from airllm import AutoModel
MAX_LENGTH = 128
model = AutoModel.from_pretrained("THUDM/chatglm3-6b-base")
input_text = ['What is the capital of China?',]
input_tokens = model.tokenizer(input_text,
    return_tensors="pt", 
    return_attention_mask=False, 
    truncation=True, 
    max_length=MAX_LENGTH, 
    padding=True)
generation_output = model.generate(
    input_tokens['input_ids'].cuda(), 
    max_new_tokens=5,
    use_cache= True,
    return_dict_in_generate=True)
model.tokenizer.decode(generation_output.sequences[0])

• QWen:

from airllm import AutoModel
MAX_LENGTH = 128
model = AutoModel.from_pretrained("Qwen/Qwen-7B")
input_text = ['What is the capital of China?',]
input_tokens = model.tokenizer(input_text,
    return_tensors="pt", 
    return_attention_mask=False, 
    truncation=True, 
    max_length=MAX_LENGTH)
generation_output = model.generate(
    input_tokens['input_ids'].cuda(), 
    max_new_tokens=5,
    use_cache=True,
    return_dict_in_generate=True)
model.tokenizer.decode(generation_output.sequences[0])

• Baichuan, InternLM, Mistral, etc:

from airllm import AutoModel
MAX_LENGTH = 128
model = AutoModel.from_pretrained("baichuan-inc/Baichuan2-7B-Base")
#model = AutoModel.from_pretrained("internlm/internlm-20b")
#model = AutoModel.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
input_text = ['What is the capital of China?',]
input_tokens = model.tokenizer(input_text,
    return_tensors="pt", 
    return_attention_mask=False, 
    truncation=True, 
    max_length=MAX_LENGTH)
generation_output = model.generate(
    input_tokens['input_ids'].cuda(), 
    max_new_tokens=5,
    use_cache=True,
    return_dict_in_generate=True)
model.tokenizer.decode(generation_output.sequences[0])

</details>

To request other model support: here

Configurations

When initialize the model, we support the following configurations:

• compression: supported options: 4bit, 8bit for 4-bit or 8-bit block-wise quantization, or by default None for no compression
• profiling_mode: supported options: True to output time consumptions or by default False
• layer_shards_saving_path: optionally another path to save the splitted model
• hf_token: huggingface token can be provided here if downloading gated models like: meta-llama/Llama-2-7b-hf
• prefetching: prefetching to overlap the model loading and compute. By default, turned on. For now, only AirLLMLlama2 supports this.
• delete_original: if you don't have too much disk space, you can set delete_original to true to delete the original downloaded hugging face model, only keep the transformed one to save half of the disk space.

Python API

from airllm import AirLLMLoRAQwen4Exp

trainer = AirLLMLoRAQwen4Exp(
    "Qwen/Qwen3.8-Flash-Next",
    max_seq_len=512,
    lora_r=16,
    delete_original=True,
)

tok = trainer.tokenizer
if tok.pad_token_id is None:
    tok.pad_token = tok.eos_token

encoded = tok(
    "Your training text here.",
    return_tensors="pt",
    truncation=True,
    max_length=512,
)
loss = trainer.train_step(
    encoded["input_ids"].cuda(),
    attention_mask=encoded.get("attention_mask"),
)
print(loss)
trainer.save_adapter("qwen38-flash-next-lora.pt")

AirLLMLoRA is the same API for Qwen/Qwen3.8-27B.

Supported Models

AirLLM works out of the box with virtually every popular open LLM — just pass its Hugging Face ID to AutoModel.from_pretrained(...). That covers all the major families:

Llama (2 / 3 / 3.1 / 3.3 / 4) · Qwen (1 / 2 / 2.5 / 3 / 3.5 / 3.8, including MoE, Flash-Next, FP8, and native VL) · DeepSeek (V2 / V3 / R1) · Mistral & Mixtral · Phi · Gemma · ChatGLM · Baichuan · InternLM · Yi · Kimi K3 — and most new models the day they're released.

FAQ