Baichuan-M2-32B-Q4_K_M-GGUF
This repository contains the model presented in Baichuan-M2: Scaling Medical Capability with Large Verifier System.
🌟 Model Overview
Baichuan-M2-32B is Baichuan AI's medical-enhanced reasoning model, the second medical model released by Baichuan. Designed for real-world medical reasoning tasks, this model builds upon Qwen2.5-32B with an innovative Large Verifier System. Through domain-specific fine-tuning on real-world medical questions, it achieves breakthrough medical performance while maintaining strong general capabilities.
Model Features:
Baichuan-M2 incorporates three core technical innovations: First, through the Large Verifier System, it combines medical scenario characteristics to design a comprehensive medical verification framework, including patient simulators and multi-dimensional verification mechanisms; second, through medical domain adaptation enhancement via Mid-Training, it achieves lightweight and efficient medical domain adaptation while preserving general capabilities; finally, it employs a multi-stage reinforcement learning strategy, decomposing complex RL tasks into hierarchical training stages to progressively enhance the model's medical knowledge, reasoning, and patient interaction capabilities.
Core Highlights:
- 🏆 World's Leading Open-Source Medical Model: Outperforms all open-source models and many proprietary models on HealthBench, achieving medical capabilities closest to GPT-5
- 🧠 Doctor-Thinking Alignment: Trained on real clinical cases and patient simulators, with clinical diagnostic thinking and robust patient interaction capabilities
- ⚡ Efficient Deployment: Supports 4-bit quantization for single-RTX4090 deployment, with 58.5% higher token throughput in MTP version for single-user scenarios
📊 Performance Metrics
HealthBench Scores
| Model Name | HealthBench | HealthBench-Hard | HealthBench-Consensus |
|---|---|---|---|
| Baichuan-M2 | 60.1 | 34.7 | 91.5 |
| gpt-oss-120b | 57.6 | 30 | 90 |
| Qwen3-235B-A22B-Thinking-2507 | 55.2 | 25.9 | 90.6 |
| Deepseek-R1-0528 | 53.6 | 22.6 | 91.5 |
| GLM-4.5 | 47.8 | 18.7 | 85.3 |
| Kimi-K2 | 43 | 10.7 | 90.9 |
| gpt-oss-20b | 42.5 | 10.8 | 82.6 |
General Performance
| Benchmark | Baichuan-M2-32B | Qwen3-32B (Thinking) |
|---|---|---|
| AIME24 | 83.4 | 81.4 |
| AIME25 | 72.9 | 72.9 |
| Arena-Hard-v2.0 | 45.8 | 44.5 |
| CFBench | 77.6 | 75.7 |
| WritingBench | 8.56 | 7.90 |
Note: AIME uses max_tokens=64k, others use 32k; temperature=0.6 for all tests.
🔧 Technical Features
📗 Technical Blog: Blog - Baichuan-M2
📑 Technical Report: Arxiv - Baichuan-M2
Large Verifier System
- Patient Simulator: Virtual patient system based on real clinical cases
- Multi-Dimensional Verification: 8 dimensions including medical accuracy, response completeness, and follow-up awareness
- Dynamic Scoring: Real-time generation of adaptive evaluation criteria for complex clinical scenarios
Medical Domain Adaptation
- Mid-Training: Medical knowledge injection while preserving general capabilities
- Reinforcement Learning: Multi-stage RL strategy optimization
- General-Specialized Balance: Carefully balanced medical, general, and mathematical composite training data
⚙️ Quick Start
For deploying the Q4_K_M quantized model, you can use llama.cpp or ollama, please visit their website to get the specific operational steps for deploying the model. Taking ollama as an example.
- Ensure that Ollama is already installed
- Download the model: baichuan-m2-32b-q4_k_m.gguf
- Create and edit the Modelfile
FROM /path/to/baichuan-m2-32b-q4_k_m.gguf
TEMPLATE """{{- if .System -}}<<|im_start|>>system
{{ .System }}<<|im_end|>>
{{- end -}}
{{- range .Messages -}}
<<|im_start|>>{{ .Role }}
{{ .Content }}<<|im_end|>>
{{- end -}}
<<|im_start|>>assistant
"""
PARAMETER stop "<<|im_end|>>"
PARAMETER stop "<<|im_start|>>"
PARAMETER temperature 0.6
PARAMETER top_p 0.9
- Create the model in Ollama
ollama create baichuan-m2-q4km -f Modelfile
- Launch the model, and you can begin chatting with it
ollama run baichuan-m2-q4km
⚠️ Usage Notices
- Medical Disclaimer: For research and reference only; cannot replace professional medical diagnosis or treatment
- Intended Use Cases: Medical education, health consultation, clinical decision support
- Safe Use: Recommended under guidance of medical professionals
📄 License
Licensed under the Apache License 2.0. Research and commercial use permitted.
🤝 Acknowledgements
- Base Model: Qwen2.5-32B
- Training Framework: verl
- Inference Engines: vLLM, SGLang
- Quantization: AutoRound, GPTQ Thank you to the open-source community. We commit to continuous contribution and advancement of healthcare AI.
📞 Contact Us
- Resources: Baichuan AI Website
- Technical Support: GitHub
Empowering Healthcare with AI, Making Health Accessible to All