Update README.md

README.md

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----
-license: apache-2.0
----
+---
+library_name: transformers
+license: apache-2.0
+pipeline_tag: text-generation
+language:
+- en
+- zh
+base_model:
+- Qwen/Qwen3-32B
+tags:
+- medical
+- reasoning
+---
+
+<div align="center">
+  <h1>🩺 HuatuoGPT-3-32B</h1>
+</div>
+
+<div align="center">
+  <a href="https://github.com/FreedomIntelligence/HuatuoGPT-3" target="_blank">🏠 GitHub</a>
+  |
+  <a href="" target="_blank">📄 Paper</a>
+</div>
+
+# <span>Introduction</span>
+
+**HuatuoGPT-3** is an open-source medical LLM trained with **SeedRL**, an RL-only domain adaptation paradigm that transforms a base model into a medical expert in a single RL stage.
+
+For more information, visit our GitHub repository:
+[https://github.com/FreedomIntelligence/HuatuoGPT-3](https://github.com/FreedomIntelligence/HuatuoGPT-3)
+
+> [!IMPORTANT]
+> **HuatuoGPT-3-32B is set to thinking mode by default.** The output contains a `<think>...</think>` reasoning block followed by the final response after `</think>`. 
+
+# <span>Model Info</span>
+
+| Model | Description | Backbone | Link |
+| --- | --- | --- | --- |
+| **HuatuoGPT-3-32B** | 32B medical LLM trained with SeedRL | Qwen3-32B | [HF Link](https://huggingface.co/FreedomIntelligence/HuatuoGPT-3-32B) |
+| **HuatuoGPT-3-8B** | 8B medical LLM trained with SeedRL | Qwen3-8B-Base | [HF Link](https://huggingface.co/FreedomIntelligence/HuatuoGPT-3-8B) |
+| **HuatuoGPT-3-7B-Pangu** | 7B medical LLM trained with SeedRL | openPangu-Embedded-7B | [HF Link](https://huggingface.co/FreedomIntelligence/HuatuoGPT-3-7B-Pangu) |
+
+
+# <span>Usage</span>
+
+You can use HuatuoGPT-3-8B in the same way as `Qwen3-32B`. You can deploy it with tools like [vLLM](https://github.com/vllm-project/vllm) or [SGLang](https://github.com/sgl-project/sglang), or perform direct inference:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "FreedomIntelligence/HuatuoGPT-3-32B"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+
+messages = [
+    {"role": "user", "content": "A patient has fever, cough, and shortness of breath. What should be considered first?"}
+]
+
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+inputs = tokenizer([text], return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=4096)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+# <span>📖 Citation</span>
+
+```bibtex
+@article{huatuogpt3,
+  title={HuatuoGPT-3: RL-Only Domain Adaptation from Base Models via Off-Policy Seeding},
+  author={Coming soon},
+  journal={arXiv preprint},
+  year={2026}
+}
+```