Baichuan-M2-32b

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

General Performance

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.

⚠️ 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