openbmb
/

MiniCPM4-MCP

@@ -1,60 +1,94 @@
----
-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](https://huggingface.co/openbmb/MiniCPM4-8B-Eagle-FRSpec-QAT): Eagle head trained with QAT for FRSpec, efficiently integrate speculation and quantization to achieve ultra acceleration 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. (**<-- you are here**)
-## Introduction
-## Usage
-## Evaluation
-## 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}
-}
-```

+---
+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](https://huggingface.co/openbmb/MiniCPM4-8B-Eagle-FRSpec-QAT): Eagle head trained with QAT for FRSpec, efficiently integrate speculation and quantization to achieve ultra acceleration 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. (**<-- you are here**)
+## Introduction
+**MiniCPM4-MCP** is an open-source on-device LLM agent model jointly developed by [THUNLP](https://nlp.csai.tsinghua.edu.cn), Renmin University of China and [ModelBest](https://modelbest.cn/en), built on [MiniCPM-4](https://huggingface.co/openbmb/MiniCPM4-8B) with 8 billion parameters. It is capable of solving a wide range of real-world tasks by interacting with various tool and data resources through MCP.
+## Usage
+As of now, MiniCPM4-MCP supports the following:
+- Utilization of tools across 16 MCP servers: These servers span various categories, including office, lifestyle, communication, information, and work management.
+- Single-tool-calling capability: It can perform single- or multi-step tool calls using a single tool that complies with the MCP.
+- Cross-tool-calling capability: It can perform single- or multi-step tool calls using different tools that complies with the MCP.
+## Evaluation
+The detailed evaluation script can be found on the GitHub page. The evaluation results are presented below.
+| MCP Server                  |          | gpt-4o             |              |          | qwen3             |              |      |      minicpm4         |              |
+|-----------------------|----------------|--------------|--------------|---------------|--------------|--------------|----------------|--------------|--------------|
+|                       | func           | param        | value        | func          | param        | value        | func           | param        | value        |
+| Airbnb                | 89.3           | 67.9         | 53.6         | 92.8          | 60.7         | 50.0         | 96.4           | 67.9         | 50.0         |
+| Amap-Maps             | 79.8           | 77.5         | 50.0         | 74.4          | 72.0         | 41.0         | 89.3           | 85.7         | 39.9         |
+| Arxiv-MCP-Server      | 85.7           | 85.7         | 85.7         | 81.8          | 54.5         | 50.0         | 57.1           | 57.1         | 52.4         |
+| Calculator            | 100.0          | 100.0        | 20.0         | 80.0          | 80.0         | 13.3         | 100.0          | 100.0        | 6.67         |
+| Computor-Control-MCP  | 90.0           | 90.0         | 90.0         | 90.0          | 90.0         | 90.0         | 90.0           | 90.0         | 86.7         |
+| Desktop-Commander     | 100.0          | 100.0        | 100.0        | 100.0         | 100.0        | 100.0        | 100.0          | 100.0        | 100.0        |
+| Filesystem            | 63.5           | 63.5         | 31.3         | 69.7          | 69.7         | 26.0         | 83.3           | 83.3         | 42.7         |
+|Github | 92.0 | 80.0 | 58.0 | 80.5 | 50.0 | 27.7 | 62.8 | 25.7 | 17.1 |
+| Gaode                 | 71.1           | 55.6         | 17.8         | 68.8          | 46.6         | 24.4         | 68.9           | 46.7         | 15.6         |
+| MCP-Code-Executor     | 85.0           | 80.0         | 70.0         | 80.0          | 80.0         | 70.0         | 90.0           | 90.0         | 65.0         |
+| MCP-Docx              | 95.8           | 86.7         | 67.1         | 94.9          | 81.6         | 60.1         | 95.1           | 86.6         | 76.1         |
+| PPT                   | 72.6           | 49.8         | 40.9         | 85.9          | 50.7         | 37.5         | 91.2           | 72.1         | 56.7         |
+| PPTx                  | 64.2           | 53.7         | 13.4         | 91.0          | 68.6         | 20.9         | 91.0           | 58.2         | 26.9         |
+| Simple-Time-Server    | 90.0           | 70.0         | 70.0         | 90.0          | 90.0         | 90.0         | 90.0           | 60.0         | 60.0         |
+| Slack                 | 100.0          | 90.0         | 70.0         | 100.0         | 100.0        | 65.0         | 100.0          | 100.0        | 100.0        |
+| Whisper               | 90.0           | 90.0         | 90.0         | 90.0          | 90.0         | 90.0         | 90.0           | 90.0         | 30.0         |
+| **Average**              | **80.2**       | **70.2**     | **49.1**     | **83.5**      | **67.7**     | **43.8**     | **88.3**       | **76.1**     | **51.2**     |
+## 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}
+}
+```