[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:
If you want to run with the frontend, you can run the following command:
bash
1cd ./code
2python ./src/retriever.py
3bash ./scripts/run_with_frontend.sh
4cd frontend/minicpm4-survey
5npminstall6npm run dev
Then you can visit http://localhost:5173 in your browser to use the model.
Performance Evaluation
Method
Relevance
Coverage
Depth
Novelty
Avg.
Fact Score
Naive RAG (driven by G2FT)
3.25
2.95
3.35
2.60
3.04
43.68
AutoSurvey (driven by G2FT)
3.10
3.25
3.15
3.15
3.16
46.56
Webthinker (driven by WTR1-7B)
3.30
3.00
2.75
2.50
2.89
--
Webthinker (driven by QwQ-32B)
3.40
3.30
3.30
2.50
3.13
--
OpenAI Deep Research (driven by GPT-4o)
3.50
3.95
3.55
3.00
3.50
--
MiniCPM4-Survey
3.45
3.70
3.85
3.00
3.50
68.73
w/o RL
3.55
3.35
3.30
2.25
3.11
50.24
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.
bibtex
1@article{minicpm4,
2 title={{MiniCPM4}: Ultra-Efficient LLMs on End Devices},
3 author={MiniCPM Team},
4 year={2025}
5}
1cd ./code
2python ./src/retriever.py
3bash ./scripts/run_with_frontend.sh
4cd frontend/minicpm4-survey
5npminstall6npm run dev
然后你可以在浏览器中访问http://localhost:5173使用。
性能
Method
Relevance
Coverage
Depth
Novelty
Avg.
Fact Score
Naive RAG (driven by G2FT)
3.25
2.95
3.35
2.60
3.04
43.68
AutoSurvey (driven by G2FT)
3.10
3.25
3.15
3.15
3.16
46.56
Webthinker (driven by WTR1-7B)
3.30
3.00
2.75
2.50
2.89
--
Webthinker (driven by QwQ-32B)
3.40
3.30
3.30
2.50
3.13
--
OpenAI Deep Research (driven by GPT-4o)
3.50
3.95
3.55
3.00
3.50
--
MiniCPM4-Survey
3.45
3.70
3.85
3.00
3.50
68.73
w/o RL
3.55
3.35
3.30
2.25
3.11
50.24
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。
