--- title: Baichuan-M2-32B canonical_url: "https://www.modelscope.cn/models/baichuan-inc/Baichuan-M2-32B" md_url: "https://www.modelscope.cn/models/baichuan-inc/Baichuan-M2-32B.md" repository: baichuan-inc/Baichuan-M2-32B last_updated: 2025-12-24 license: apache-2.0 pipeline_tag: text-generation tasks: - text-generation model_type: - qwen2 architectures: - Qwen2ForCausalLM base_model: - Qwen/Qwen2.5-32B base_model_relation: finetune parameters: 32.8B tensor_type: - BF16 library_name: - transformer - safetensors - pytorch frameworks: - pytorch language: - en - zh inference_backends: - "deploy_task text/emb" - "lmdeploy 0.9.1" - "lmdeploy_turbomind 0.9.1" - "sglang 0.5.2" - "vllm 0.9.2" downloads: 25894 stars: 36 tags: - chat --- # Baichuan-M2-32B > Baichuan-M2-32B - baichuan-inc 在 ModelScope 开源的模型。This repository contains the model presented in Baichuan-M2: Scaling Medical Capability with Large Verifier System. baichuan-inc/Baichuan-M2-32B 是 ModelScope 魔搭社区上的 32.8B 参数text-generation模型，采用 apache-2.0 许可，基于 Qwen/Qwen2.5-32B 构建，可用 deploy_task text/emb、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。 - **Repository**: baichuan-inc/Baichuan-M2-32B - **License**: apache-2.0 - **Tasks**: text-generation - **Parameters**: 32.8B - **Base model**: Qwen/Qwen2.5-32B - **Inference backends**: deploy_task text/emb, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2 - **Tags**: chat - **Downloads**: 25894 - **Stars**: 36 - **Last updated**: 2025-12-24 Source: https://www.modelscope.cn/models/baichuan-inc/Baichuan-M2-32B --- # Baichuan-M2-32B This repository contains the model presented in [Baichuan-M2: Scaling Medical Capability with Large Verifier System](https://huggingface.co/papers/2509.02208). [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) [![Hugging Face](https://img.shields.io/badge/🤗%20Hugging%20Face-Model-yellow)](https://huggingface.co/baichuan-inc/Baichuan-M2-32B) [![M2 GPTQ-4bit](https://img.shields.io/badge/🤗%20M2%20GPTQ--4bit-Model-orange)](https://huggingface.co/baichuan-inc/Baichuan-M2-32B-GPTQ-Int4) [![Huawei Ascend 8bit](https://img.shields.io/badge/✨%20Huawei%20Ascend%208bit-Model-green)](https://modelers.cn/models/Baichuan/Baichuan-M2-32B-W8A8) ## 🌟 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](https://www.baichuan-ai.com/blog/baichuan-M2) 📑 **Technical Report**: [Arxiv - Baichuan-M2](https://arxiv.org/abs/2509.02208) ### 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 ```python # 1. load model from transformers import AutoTokenizer, AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("baichuan-inc/Baichuan-M2-32B", trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained("baichuan-inc/Baichuan-M2-32B") # 2. Input prompt text prompt = "Got a big swelling after a bug bite. Need help reducing it." # 3. Encode the input text for the model messages = [ {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, thinking_mode='on' # on/off/auto ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # 4. Generate text generated_ids = model.generate( **model_inputs, max_new_tokens=4096 ) output_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ][0].tolist() # 5. parsing thinking content try: # rindex finding 151668 () index = len(output_ids) - output_ids[::-1].index(151668) except ValueError: index = 0 thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip(" ") content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip(" ") print("thinking content:", thinking_content) print("content:", content) ``` For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.9.0` or to create an OpenAI-compatible API endpoint: - SGLang: ```shell python -m sglang.launch_server --model-path baichuan-inc/Baichuan-M2-32B --reasoning-parser qwen3 ``` - vLLM: ```shell vllm serve baichuan-inc/Baichuan-M2-32B --reasoning-parser qwen3 ``` ## MTP inference with SGLang 1. Replace the qwen2.py file in the sglang installation directory with draft/qwen2.py. 2. Launch sglang: ``` python3 -m sglang.launch_server \ --model Baichuan-M2-32B \ --speculative-algorithm EAGLE3 \ --speculative-draft-model-path Baichuan-M2-32B/draft \ --speculative-num-steps 6 \ --speculative-eagle-topk 10 \ --speculative-num-draft-tokens 32 \ --mem-fraction 0.9 \ --cuda-graph-max-bs 2 \ --reasoning-parser qwen3 \ --dtype bfloat16 ``` ## ⚠️ 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](LICENSE). 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](https://www.baichuan-ai.com) - Technical Support: [GitHub](https://github.com/baichuan-inc) ---
**Empowering Healthcare with AI, Making Health Accessible to All**