Deploying Baichuan-M2-32B is divided into three main steps:
- Environment Configuration: need to install transformers>=4.42.0 and accelerate library, it is recommended to use the CUDA version of PyTorch and make sure the NVIDIA driver is working.
- API service building: OpenAI-compatible API endpoints can be created with inference engines such as sglang or vllm. For example when using vLLM execute
vllm serve baichuan-inc/Baichuan-M2-32B --reasoning-parser qwen3 - application matching: After starting the service, the healthcare system can interact with the model via HTTP requests, supporting batch processing of clinical problems or real-time doctor-patient dialog scenarios
Note that thinking_mode should be turned on during deployment to facilitate tracking of the model's diagnostic reasoning process.
This answer comes from the articleBaichuan-M2: A Large Language Model for Augmented Reasoning in HealthcareThe
May not be reproduced without permission:AI productivity tools " How to deploy Baichuan-M2-32B model in real projects?
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