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.

1. Ensure that Ollama is already installed
2. Download the model: baichuan-m2-32b-q4_k_m.gguf
3. 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

4. Create the model in Ollama

ollama create baichuan-m2-q4km -f Modelfile

5. Launch the model, and you can begin chatting with it

ollama run baichuan-m2-q4km

⚠️ Usage Notices

1. Medical Disclaimer: For research and reference only; cannot replace professional medical diagnosis or treatment
2. Intended Use Cases: Medical education, health consultation, clinical decision support
3. 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

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