FreedomIntelligence releases HuatuoGPT-3-9B medical LLM with open usage guides
FreedomIntelligence has made its new HuatuoGPT-3-9B model publicly available on Hugging Face. The 9‑billion‑parameter model is built on Qwen3.5‑9B and uses a One‑stage Policy Optimization (OnePO) technique that adapts a language model to medical tasks without a separate supervised fine‑tuning stage. The release includes the model weights, training code, a medical reinforcement‑learning dataset,…
Key points
- HuatuoGPT-3-9B is a 9‑billion‑parameter medical LLM built on Qwen3.5‑9B using One‑stage Policy Optimization.
- The model, training code, medical RL dataset, and an 8‑billion‑parameter rubric grader are released on Hugging Face.
- Usage guides cover Transformers pipelines, vLLM, SGLang, and Docker Model Runner for image‑text and pure text queries.
The post provides step‑by‑step instructions for deploying HuatuoGPT‑3‑9B with several popular inference stacks. Users can run the model via the Transformers library, set up an OpenAI‑compatible API with vLLM or SGLang, or launch a Docker Model Runner container. Example snippets show how to send image‑text prompts—such as asking “What animal is on the candy?”—and retrieve concise answers. The guide also notes that the model supports a “thinking mode” where it generates reasoning before the final answer, and that the model can handle up to 4,096 new tokens per request.
A citation to the underlying OnePO paper (Chen et al., 2026) is included, encouraging academic reuse and further research into domain‑specific policy optimization for large language models.
The story so far
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FreedomIntelligence/HuatuoGPT-3-9B · Hugging Face
huggingface.co · 17 September 2026
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