baichuan-inc

Baichuan-M2-32B

README

License: apache-2.0

🌟 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

Table with columns: Model Name, HealthBench, HealthBench-Hard, HealthBench-Consensus
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

General Performance

Table with columns: Benchmark, Baichuan-M2-32B, Qwen3-32B (Thinking)
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

python

# 1. load modelfrom transformers import AutoTokenizer, AutoModelForCausalLMmodel = AutoModelForCausalLM.from_pretrained("baichuan-inc/Baichuan-M2-32B", trust_remote_code=True)tokenizer = AutoTokenizer.from_pretrained("baichuan-inc/Baichuan-M2-32B")# 2. Input prompt textprompt = "Got a big swelling after a bug bite. Need help reducing it."# 3. Encode the input text for the modelmessages = [    {"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 textgenerated_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 contenttry:    # rindex finding 151668 (</think>)    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

Replace the qwen2.py file in the sglang installation directory with draft/qwen2.py.
Launch sglang:

text

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

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

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Model Details

Model Provider

baichuan-inc

Model Tree

Base

Qwen/Qwen2.5-32B

Fine-tuned

this model

Input Modalities

Text

Output Modalities

Text

Supported Functionality

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README

License: apache-2.0

🌟 Model Overview

Model Features:

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

Table with columns: Model Name, HealthBench, HealthBench-Hard, HealthBench-Consensus
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

General Performance

Table with columns: Benchmark, Baichuan-M2-32B, Qwen3-32B (Thinking)
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

python

# 1. load modelfrom transformers import AutoTokenizer, AutoModelForCausalLMmodel = AutoModelForCausalLM.from_pretrained("baichuan-inc/Baichuan-M2-32B", trust_remote_code=True)tokenizer = AutoTokenizer.from_pretrained("baichuan-inc/Baichuan-M2-32B")# 2. Input prompt textprompt = "Got a big swelling after a bug bite. Need help reducing it."# 3. Encode the input text for the modelmessages = [    {"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 textgenerated_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 contenttry:    # rindex finding 151668 (</think>)    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

Replace the qwen2.py file in the sglang installation directory with draft/qwen2.py.
Launch sglang:

text

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

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