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*This model was published in HF papers on 2024-09-05 and contributed to Hugging Face Transformers on 2026-06-22.*

# MiniCPM3

## Overview

MiniCPM3 is the third-generation MiniCPM dense language model from OpenBMB. The 4B variant
([`openbmb/MiniCPM3-4B`](https://huggingface.co/openbmb/MiniCPM3-4B)) outperforms many 7B–9B open
models on standard benchmarks while remaining lightweight enough for on-device usage.

MiniCPM3 combines several architectural ideas:

- **Multi-head Latent Attention (MLA)** from DeepSeek-V2, which compresses the key/value cache
  into a low-rank latent representation while still using rotary embeddings on a portion of the
  query/key heads.
- A standard SwiGLU MLP (no MoE).
- Three scalar scaling factors that govern signal flow:
  - `scale_emb` — scales input embeddings.
  - `scale_depth / sqrt(num_hidden_layers)` — scales residual connections.
  - `hidden_size / dim_model_base` — scales hidden states before the language model head.

## Usage tips

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("openbmb/MiniCPM3-4B")
model = AutoModelForCausalLM.from_pretrained("openbmb/MiniCPM3-4B", device_map="auto")

inputs = tokenizer("Hello, my name is", return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=32, do_sample=False)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## MiniCPM3Config

[[autodoc]] MiniCPM3Config

## MiniCPM3Model

[[autodoc]] MiniCPM3Model
    - forward

## MiniCPM3ForCausalLM

[[autodoc]] MiniCPM3ForCausalLM
    - forward

## MiniCPM3ForSequenceClassification

[[autodoc]] MiniCPM3ForSequenceClassification
    - forward
