MiniCPM4-MCP Open-source Edge-side Large Language Model - Solving Real-world Tasks through Interaction with Tools

Minicpm4 MCP

Developed by openbmb

MiniCPM4-MCP is an open-source edge-side large language model agent model, built on MiniCPM-4 with 8 billion parameters. It can interact with various tools and data resources through MCP to solve a wide range of real-world tasks.

Large Language Model

Transformers

Supports Multiple LanguagesOpen Source License:Apache-2.0 #Edge-side large model #Multi-tool interaction #Efficient inference

Downloads 367

Release Time : 6/5/2025

Model Overview

MiniCPM4-MCP is an agent model based on MiniCPM4-8B. It supports invoking multiple MCP tools for task processing and is suitable for multiple fields such as office work, daily life, and communication.

Model Features

Utilization of multiple MCP server tools

Supports utilizing tools on 16 MCP servers, covering multiple categories such as office work, daily life, communication, information, and work management.

Single-tool invocation ability

Can use a single MCP-compliant tool for single-step or multi-step tool invocation.

Cross-tool invocation ability

Can use different MCP-compliant tools for single-step or multi-step tool invocation.

Efficient inference

Achieves more than 5 times generation acceleration on edge-side chips.

Model Capabilities

Tool invocation

Automated task processing

Multi-step task execution

Cross-tool collaboration

Use Cases

Office automation

Document processing

Automatically invokes the MCP-Docx tool to process Word documents

Reaches a functional accuracy of 95.1% in the evaluation

PPT production

Automatically generates and edits PPT presentations

Reaches a functional accuracy of 91.2% in the evaluation

Life services

Map navigation

Invokes the Amap-Maps tool to provide navigation services

Reaches a functional accuracy of 89.3% in the evaluation

Accommodation reservation

Queries and reserves accommodation through the Airbnb tool

Reaches a functional accuracy of 96.4% in the evaluation

Development tools

Code execution

Uses the MCP-Code-Executor to run and test code

Reaches a functional accuracy of 90% in the evaluation

GitHub operations

Automates GitHub repository management

Reaches a functional accuracy of 62.8% in the evaluation

🚀 MiniCPM4-MCP

MiniCPM4-MCP is an open - source on - device LLM agent model. Built on MiniCPM - 4, it can solve a wide range of real - world tasks by interacting with various tool and data resources through MCP.

🚀 Quick Start

You can start exploring MiniCPM4-MCP by referring to the GitHub Repo. Also, check out the Technical Report for in - depth information.

GitHub Repo | Technical Report

👋 Join us on Discord and WeChat

✨ Features

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.🔥🔥🔥

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, Renmin University of China and ModelBest, 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 License.

📚 Documentation

Citation

Please cite our paper if you find our work valuable.

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

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