139 lines
6.1 KiB
Markdown
139 lines
6.1 KiB
Markdown
---
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license: apache-2.0
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tags:
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- chat
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library_name: transformers
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language:
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- en
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- zh
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base_model:
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- Qwen/Qwen2.5-32B
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---
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# Baichuan-M2-32B-GPTQ-Int4
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[](https://opensource.org/licenses/Apache-2.0)
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[](https://huggingface.co/baichuan-inc/Baichuan-M2-32B)
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[](https://huggingface.co/baichuan-inc/Baichuan-M2-32B-GPTQ-Int4)
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[](https://modelers.cn/models/Baichuan/Baichuan-M2-32B-W8A8)
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## 🌟 Model Overview
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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.
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**Model Features:**
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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.
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**Core Highlights:**
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- 🏆 **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
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- 🧠 **Doctor-Thinking Alignment**: Trained on real clinical cases and patient simulators, with clinical diagnostic thinking and robust patient interaction capabilities
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- ⚡ **Efficient Deployment**: Supports 4-bit quantization for single-RTX4090 deployment, with 58.5% higher token throughput in MTP version for single-user scenarios
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## 📊 Performance Metrics
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### HealthBench Scores
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| Model Name | HealthBench | HealthBench-Hard | HealthBench-Consensus |
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|------------|-------------|------------------|-----------------------|
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| Baichuan-M2 | 60.1 | 34.7 | 91.5 |
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| gpt-oss-120b | 57.6 | 30 | 90 |
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| Qwen3-235B-A22B-Thinking-2507 | 55.2 | 25.9 | 90.6 |
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| Deepseek-R1-0528 | 53.6 | 22.6 | 91.5 |
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| GLM-4.5 | 47.8 | 18.7 | 85.3 |
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| Kimi-K2 | 43 | 10.7 | 90.9 |
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| gpt-oss-20b | 42.5 | 10.8 | 82.6 |
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### General Performance
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| Benchmark | Baichuan-M2-32B | Qwen3-32B (Thinking) |
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|-----------|-----------------|-----------|
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| AIME24 | 83.4 | 81.4 |
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| AIME25 | 72.9 | 72.9 |
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| Arena-Hard-v2.0 | 45.8 | 44.5 |
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| CFBench | 77.6 | 75.7 |
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| WritingBench | 8.56 | 7.90 |
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*Note: AIME uses max_tokens=64k, others use 32k; temperature=0.6 for all tests.*
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## 🔧 Technical Features
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📗 **Technical Blog**: [Blog - Baichuan-M2](https://www.baichuan-ai.com/blog/baichuan-M2)
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📑 **Technical Report**: [Arxiv - Baichuan-M2](https://arxiv.org/abs/2509.02208)
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### Large Verifier System
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- **Patient Simulator**: Virtual patient system based on real clinical cases
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- **Multi-Dimensional Verification**: 8 dimensions including medical accuracy, response completeness, and follow-up awareness
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- **Dynamic Scoring**: Real-time generation of adaptive evaluation criteria for complex clinical scenarios
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### Medical Domain Adaptation
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- **Mid-Training**: Medical knowledge injection while preserving general capabilities
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- **Reinforcement Learning**: Multi-stage RL strategy optimization
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- **General-Specialized Balance**: Carefully balanced medical, general, and mathematical composite training data
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## ⚙️ Quick Start
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For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.9.0` or to create an OpenAI-compatible API endpoint:
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- SGLang:
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```shell
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python -m sglang.launch_server --model-path baichuan-inc/Baichuan-M2-32B-GPTQ-Int4 --reasoning-parser qwen3
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```
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To turn on kv cache FP8 quantization:
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```shell
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python -m sglang.launch_server --model-path baichuan-inc/Baichuan-M2-32B-GPTQ-Int4 --reasoning-parser qwen3 --kv-cache-dtype fp8_e4m3 --attention-backend flashinfer
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```
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- vLLM:
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```shell
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vllm serve baichuan-inc/Baichuan-M2-32B-GPTQ-Int4 --reasoning-parser qwen3
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```
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To turn on kv cache FP8 quantization:
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```shell
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vllm serve baichuan-inc/Baichuan-M2-32B-GPTQ-Int4 --reasoning-parser qwen3 --kv_cache_dtype fp8_e4m3
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```
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## MTP inference with SGLang
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1. Replace the qwen2.py file in the sglang installation directory with draft/qwen2.py.
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2. Launch sglang:
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```
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python3 -m sglang.launch_server \
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--model Baichuan-M2-32B-GPTQ-Int4 \
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--speculative-algorithm EAGLE3 \
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--speculative-draft-model-path Baichuan-M2-32B-GPTQ-Int4/draft \
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--speculative-num-steps 6 \
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--speculative-eagle-topk 10 \
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--speculative-num-draft-tokens 32 \
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--mem-fraction 0.9 \
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--cuda-graph-max-bs 2 \
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--reasoning-parser qwen3 \
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--dtype bfloat16
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```
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## ⚠️ Usage Notices
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1. **Medical Disclaimer**: For research and reference only; cannot replace professional medical diagnosis or treatment
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2. **Intended Use Cases**: Medical education, health consultation, clinical decision support
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3. **Safe Use**: Recommended under guidance of medical professionals
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## 📄 License
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Licensed under the [Apache License 2.0](LICENSE). Research and commercial use permitted.
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## 🤝 Acknowledgements
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- Base Model: Qwen2.5-32B
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- Training Framework: verl
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- Inference Engines: vLLM, SGLang
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- Quantization: AutoRound, GPTQ
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Thank you to the open-source community. We commit to continuous contribution and advancement of healthcare AI.
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## 📞 Contact Us
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- Resources: [Baichuan AI Website](https://www.baichuan-ai.com)
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- Technical Support: [GitHub](https://github.com/baichuan-inc)
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---
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<div align="center">
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**Empowering Healthcare with AI, Making Health Accessible to All**
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</div>
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