README.md

---
license: apache-2.0
language:
- zh
- en
pipeline_tag: text-generation
library_name: transformers
---
<div align="center">
<img src="https://github.com/OpenBMB/MiniCPM/blob/main/assets/minicpm_logo.png?raw=true" width="500em" ></img> 
</div>

<p align="center">
<a href="https://github.com/OpenBMB/MiniCPM/" target="_blank">GitHub Repo</a> |
<a href="https://github.com/OpenBMB/MiniCPM/tree/main/report/MiniCPM_4_Technical_Report.pdf" target="_blank">Technical Report</a> 
</p>
<p align="center">
👋 Join us on <a href="https://discord.gg/3cGQn9b3YM" target="_blank">Discord</a> and <a href="https://github.com/OpenBMB/MiniCPM/blob/main/assets/wechat.jpg" target="_blank">WeChat</a>
</p>

## What's New
- [2025.06.06] **MiniCPM4** series are released! This model achieves ultimate efficiency improvements while maintaining optimal performance at the same scale! It can achieve over 5x generation acceleration on typical end-side chips! You can find technical report [here](https://github.com/OpenBMB/MiniCPM/tree/main/report/MiniCPM_4_Technical_Report.pdf).🔥🔥🔥

## MiniCPM4 Series
MiniCPM4 series are highly efficient large language models (LLMs) designed explicitly for end-side devices, which achieves this efficiency through systematic innovation in four key dimensions: model architecture, training data, training algorithms, and inference systems.
- [MiniCPM4-8B](https://huggingface.co/openbmb/MiniCPM4-8B): The flagship of MiniCPM4, with 8B parameters, trained on 8T tokens.
- [MiniCPM4-0.5B](https://huggingface.co/openbmb/MiniCPM4-0.5B): The small version of MiniCPM4, with 0.5B parameters, trained on 1T tokens.
- [MiniCPM4-8B-Eagle-FRSpec](https://huggingface.co/openbmb/MiniCPM4-8B-Eagle-FRSpec): Eagle head for FRSpec, accelerating speculative inference for MiniCPM4-8B.
- [MiniCPM4-8B-Eagle-FRSpec-QAT-cpmcu](https://huggingface.co/openbmb/MiniCPM4-8B-Eagle-FRSpec-QAT-cpmcu): Eagle head trained with QAT for FRSpec, efficiently integrate speculation and quantization to achieve ultra acceleration for MiniCPM4-8B. (**<-- you are here**)
- [MiniCPM4-8B-Eagle-vLLM](https://huggingface.co/openbmb/MiniCPM4-8B-Eagle-vLLM): Eagle head in vLLM format, accelerating speculative inference for MiniCPM4-8B.
- [MiniCPM4-8B-marlin-Eagle-vLLM](https://huggingface.co/openbmb/MiniCPM4-8B-marlin-Eagle-vLLM): Quantized Eagle head for vLLM format, accelerating speculative inference for MiniCPM4-8B.
- [BitCPM4-0.5B](https://huggingface.co/openbmb/BitCPM4-0.5B): Extreme ternary quantization applied to MiniCPM4-0.5B compresses model parameters into ternary values, achieving a 90% reduction in bit width.
- [BitCPM4-1B](https://huggingface.co/openbmb/BitCPM4-1B): Extreme ternary quantization applied to MiniCPM3-1B compresses model parameters into ternary values, achieving a 90% reduction in bit width.
- [MiniCPM4-Survey](https://huggingface.co/openbmb/MiniCPM4-Survey): Based on MiniCPM4-8B, accepts users' quiries as input and autonomously generate trustworthy, long-form survey papers.
- [MiniCPM4-MCP](https://huggingface.co/openbmb/MiniCPM4-MCP): Based on MiniCPM4-8B, accepts users' queries and available MCP tools as input and autonomously calls relevant MCP tools to satisfy users' requirements.

## Introduction
MiniCPM4-8B-Eagle-FRSpec-QAT is a quantization-friendly Eagle model trained with MiniCPM4-8B in QAT. It clould be apply on our inference framework [cpm.cu](https://github.com/OpenBMB/cpm.cu) with FRSpec,  accelerating the generation speed by 7 times compared to Qwen3-8B.

## Usage
### Inference with cpm.cu
```
# case 1: verify model is fp16 or bf16
cd cpm.cu/tests
python3 test_generate.py \
    --no-apply-quant \
    --apply-eagle-quant

# case 2: verify model is quanted with Marlin (W4A16, group size = 128)
cd cpm.cu/tests
python3 test_generate.py \
    --apply-quant \
    --apply-eagle-quant
```

## Evaluation

Tested on two representative edge devices, the Jetson AGX Orin and RTX 4090, MiniCPM4 with MiniCPM4-8B-Eagle-FRSpec-QAT demonstrates significantly superior processing speed over models of comparable size for long-text processing tasks. Its performance advantage becomes increasingly pronounced as the text length increases. On the Jetson AGX Orin platform, MiniCPM4 achieves approximately a 7x improvement in generation speed compared to Qwen3-8B.

![speed test](https://raw.githubusercontent.com/OpenBMB/MiniCPM/refs/heads/minicpm-4/assets/minicpm4/efficiency.png)


## Statement
- As a language model, MiniCPM generates content by learning from a vast amount of text. 
- However, it does not possess the ability to comprehend or express personal opinions or value judgments. 
- Any content generated by MiniCPM does not represent the viewpoints or positions of the model developers. 
- Therefore, when using content generated by MiniCPM, users should take full responsibility for evaluating and verifying it on their own.

## LICENSE
- This repository and MiniCPM models are released under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) License. 

## Citation
- Please cite our [paper](https://github.com/OpenBMB/MiniCPM/tree/main/report/MiniCPM_4_Technical_Report.pdf) if you find our work valuable.

```bibtex
@article{minicpm4,
  title={{MiniCPM4}: Ultra-Efficient LLMs on End Devices},
  author={MiniCPM Team},
  year={2025}
}
```