MiniCPM4-Survey

<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> | <a href="https://huggingface.co/papers/2506.07900" target="_blank">Paper</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>

This repository contains the model described in the paper MiniCPM4: Ultra-Efficient LLMs on End Devices.

What's New

• [2025-06-05] 🚀🚀🚀 We have open-sourced MiniCPM4-Survey, a model built upon MiniCPM4-8B that is capable of generating trustworthy, long-form survey papers while maintaining competitive performance relative to significantly larger models.

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: The flagship of MiniCPM4, with 8B parameters, trained on 8T tokens.
• MiniCPM4-0.5B: The small version of MiniCPM4, with 0.5B parameters, trained on 1T tokens.
• MiniCPM4-8B-Eagle-FRSpec: Eagle head for FRSpec, accelerating speculative inference for MiniCPM4-8B.
• 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: Eagle head in vLLM format, accelerating speculative inference for MiniCPM4-8B.
• MiniCPM4-8B-marlin-Eagle-vLLM: Quantized Eagle head for vLLM format, accelerating speculative inference for MiniCPM4-8B.
• 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: Extreme ternary quantization applied to MiniCPM3-1B compresses model parameters into ternary values, achieving a 90% reduction in bit width.
• MiniCPM4-Survey: Based on MiniCPM4-8B, accepts users' quiries as input and autonomously generate trustworthy, long-form survey papers. (<-- you are here)
• 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.

Overview

MiniCPM4-Survey is an open-source LLM agent model jointly developed by THUNLP, Renmin University of China and ModelBest. Built on MiniCPM4 with 8 billion parameters, it accepts users' quiries as input and autonomously generate trustworthy, long-form survey papers.

Key features include:

• Plan-Retrieve-Write Survey Generation Framework — We propose a multi-agent generation framework, which operates through three core stages: planning (defining the overall structure of the survey), retrieval (generating appropriate retrieval keywords), and writing (synthesizing the retrieved information to generate coherent section-level content).

• High-Quality Dataset Construction — We gather and process lots of expert-written survey papers to construct a high-quality training dataset. Meanwhile, we collect a large number of research papers to build a retrieval database.

• Multi-Aspect Reward Design — We carefully design a reward system with three aspects (structure, content, and citations) to evaluate the quality of the surveys, which is used as the reward function in the RL training stage.

• Multi-Step RL Training Strategy — We propose a Context Manager to ensure retention of essential information while facilitating efficient reasoning, and we construct Parallel Environment to maintain efficient RL training cycles.

Quick Start

Download the model

Download MiniCPM4-Survey from Hugging Face and place it in model/MiniCPM4-Survey. We recommend using MiniCPM-Embedding-Light as the embedding model, which can be downloaded from Hugging Face and placed in model/MiniCPM-Embedding-Light.

Prepare the environment

You can download the paper data from Kaggle, then extract it. You can run python data_process.py to process the data and generate the retrieval database. Then you can run python build_index.py to build the retrieval database.

cd ./code
curl -L -o ~/Downloads/arxiv.zip\
   https://www.kaggle.com/api/v1/datasets/download/Cornell-University/arxiv
unzip ~/Downloads/arxiv.zip -d .
mkdir data
python ./src/preprocess/data_process.py
mkdir index
python ./src/preprocess/build_index.py

Model Inference

You can run the following command to build the retrieval environment and start the inference:

cd ./code
python ./src/retriever.py
bash ./scripts/run.sh

If you want to run with the frontend, you can run the following command:

cd ./code
python ./src/retriever.py
bash ./scripts/run_with_frontend.sh
cd frontend/minicpm4-survey
npm install
npm run dev

Then you can visit http://localhost:5173 in your browser to use the model.

Performance Evaluation

Performance comparison of the survey generation systems. "G2FT" stands for Gemini-2.0-Flash-Thinking, and "WTR1-7B" denotes Webthinker-R1-7B. FactScore evaluation was omitted for Webthinker, as it does not include citation functionality, and for OpenAI Deep Research, which does not provide citations when exporting the results.

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.

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}
}

中文

News

• [2025-06-05] 🚀🚀🚀我们开源了基于MiniCPM4-8B构建的MiniCPM4-Survey，能够生成可信的长篇调查报告，性能比肩更大模型。

概览

MiniCPM4-Survey是由THUNLP、中国人民大学和ModelBest联合开发的开源大语言模型智能体。它基于MiniCPM4 80亿参数基座模型，接受用户质量作为输入，自主生成可信的长篇综述论文。

主要特性包括：

• 计划-检索-写作生成框架 — 我们提出了一个多智能体生成框架，包含三个核心阶段：计划（定义综述的整体结构）、检索（生成合适的检索关键词）和写作（利用检索到的信息，生成连贯的段落）。
• 高质量数据集构建——我们收集并处理大量人类专家写作的综述论文，构建高质量训练集。同时，我们收集大量研究论文，构建检索数据库。
• 多方面奖励设计 — 我们精心设计了包含结构、内容和引用的奖励，用于评估综述的质量，在强化学习训练阶段作奖励函数。
• 多步强化学习训练策略 — 我们提出了一个上下文管理器，以确保在促进有效推理的同时保留必要的信息，并构建了并行环境，维持强化学习训练高效。

使用

下载模型

从 Hugging Face 下载MiniCPM4-Survey并将其放在model/MiniCPM4-Survey中。 我们建议使用MiniCPM-Embedding-Light作为表征模型，放在model/MiniCPM-Embedding-Light中。

准备环境

从 Kaggle 下载论文数据，然后解压。运行python data_process.py，处理数据并生成检索数据库。然后运行python build_index.py，构建检索数据库。

cd ./code
curl -L -o ~/Downloads/arxiv.zip\
   https://www.kaggle.com/api/v1/datasets/download/Cornell-University/arxiv
unzip ~/Downloads/arxiv.zip -d .
mkdir data
python ./src/preprocess/data_process.py
mkdir index
python ./src/preprocess/build_index.py

模型推理

运行以下命令来构建检索环境并开始推理：

cd ./code
python ./src/retriever.py
bash ./scripts/run.sh

如果您想使用前端运行，可以运行以下命令：

cd ./code
python ./src/retriever.py
bash ./scripts/run_with_frontend.sh
cd frontend/minicpm4-survey
npm install
npm run dev

然后你可以在浏览器中访问http://localhost:5173使用。

性能

GPT-4o对综述生成系统的性能比较。“G2FT”代表Gemini-2.0-Flash-Thinking，“WTR1-7B”代表Webthinker-R1-7B。由于Webthinker不包括引用功能，OpenAI Deep Research在导出结果时不提供引用，因此省略了对它们的FactScore评估。我们的技术报告中包含评测的详细信息。