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="" 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 on [arXiv]().🔥🔥🔥

## MiniCPM4 Series
- [MiniCPM4-0.5B](https://huggingface.co/openbmb/MiniCPM4-0.5B): TODO
- [MiniCPM4-8B](https://huggingface.co/openbmb/MiniCPM4-8B): TODO **<-- you are here**
- [MiniCPM4-8B-Eagle-FRSpec](https://huggingface.co/openbmb/MiniCPM4-8B-Eagle-FRSpec)
- [MiniCPM4-8B-Eagle-FRSpec-QAT](https://huggingface.co/openbmb/MiniCPM4-8B-Eagle-FRSpec-QAT)
- [BitCPM4-0.5B](https://huggingface.co/openbmb/BitCPM4-0.5B): TODO
- [BitCPM4-1B](https://huggingface.co/openbmb/BitCPM4-1B): TODO
- [MiniCPM4-Survey](https://huggingface.co/openbmb/MiniCPM4-Survey): TODO
- [MiniCPM4-MCP](https://huggingface.co/openbmb/MiniCPM4-MCP): TODO

## Introduction
MiniCPM 4 is an extremely efficient edge-side large model that has undergone efficient optimization across four dimensions: model architecture, learning algorithms, training data, and inference systems, achieving ultimate efficiency improvements.

- 🏗️ **Efficient Model Architecture:**
  - InfLLM v2 -- Trainable Sparse Attention Mechanism: Adopts a trainable sparse attention mechanism architecture where each token only needs to compute relevance with less than 5% of tokens in 128K long text processing, significantly reducing computational overhead for long texts

- 🧠 **Efficient Learning Algorithms:**
  - Model Wind Tunnel 2.0 -- Efficient Predictable Scaling: Introduces scaling prediction methods for performance of downstream tasks, enabling more precise model training configuration search
  - BitCPM -- Ultimate Ternary Quantization: Compresses model parameter bit-width to 3 values, achieving 90% extreme model bit-width reduction
  - Efficient Training Engineering Optimization: Adopts FP8 low-precision computing technology combined with Multi-token Prediction training strategy

- 📚 **High-Quality Training Data:**
  - UltraClean -- High-quality Pre-training Data Filtering and Generation: Builds iterative data cleaning strategies based on efficient data verification, open-sourcing high-quality Chinese and English pre-training dataset [UltraFinweb](https://huggingface.co/datasets/openbmb/Ultra-FineWeb)
  - UltraChat v2 -- High-quality Supervised Fine-tuning Data Generation: Constructs large-scale high-quality supervised fine-tuning datasets covering multiple dimensions including knowledge-intensive data, reasoning-intensive data, instruction-following data, long text understanding data, and tool calling data

- ⚡ **Efficient Inference System:**
  - FRSpec -- Lightweight Speculative Sampling: Achieves draft model acceleration through vocabulary pruning of draft model
  - ArkInfer -- Cross-platform Deployment System: Supports efficient deployment across multiple backend environments, providing flexible cross-platform adaptation capabilities

## Usage
### Inference with Transformers

### Inference with [vLLM](https://github.com/vllm-project/vllm)

## Evaluation Results
On two typical end-side chips, Jetson AGX Orin and RTX 4090, MiniCPM4 demonstrates significantly faster processing speed compared to similar-size models in long text processing tasks. As text length increases, MiniCPM4's efficiency advantage becomes more pronounced. On the Jetson AGX Orin platform, compared to Qwen3-8B, MiniCPM4 achieves approximately 7x decoding speed improvement.

![benchmark](https://github.com/OpenBMB/MiniCPM/blob/main/assets/minicpm4/efficiency.png?raw=true)

#### Comprehensive Evaluation
MiniCPM4 launches end-side versions with 8B and 0.5B parameter scales, both achieving best-in-class performance in their respective categories.

![benchmark](https://github.com/OpenBMB/MiniCPM/blob/main/assets/minicpm4/benchmark.png?raw=true)

#### Long Text Evaluation
MiniCPM4 is pre-trained on 32K long texts and achieves length extension through YaRN technology. In the 128K long text needle-in-a-haystack task, MiniCPM4 demonstrates outstanding performance.

![long-niah](https://github.com/OpenBMB/MiniCPM/blob/main/assets/minicpm4/128k-niah.png?raw=true)

## 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 is released under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) License. 
- The usage of MiniCPM model weights must strictly follow [MiniCPM Model License](https://github.com/OpenBMB/MiniCPM/blob/main/MiniCPM%20Model%20License.md).
- The models and weights of MiniCPM are completely free for academic research. after filling out a [questionnaire](https://modelbest.feishu.cn/share/base/form/shrcnpV5ZT9EJ6xYjh3Kx0J6v8g) for registration, are also available for free commercial use.

## Citation

- Please cite our [paper](TODO) if you find our work valuable.

```bibtex

TODO

```