# MiniCPM4-MCP **Repository Path**: hf-models/MiniCPM4-MCP ## Basic Information - **Project Name**: MiniCPM4-MCP - **Description**: Mirror of https://huggingface.co/openbmb/MiniCPM4-MCP - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2025-06-07 - **Last Updated**: 2025-06-07 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README --- license: apache-2.0 language: - zh - en pipeline_tag: text-generation library_name: transformers ---

GitHub Repo | Technical Report

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## 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. - [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. (**<-- 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](https://github.com/OpenBMB/MiniCPM/tree/minicpm-4/demo/minicpm4/MCP) 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} } ```