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Model: FreedomIntelligence/HuatuoGPT-o1-8B
Source: Original Platform

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README.md

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+---
+license: apache-2.0
+datasets:
+- FreedomIntelligence/medical-o1-reasoning-SFT
+- FreedomIntelligence/medical-o1-verifiable-problem
+language:
+- en
+base_model:
+- meta-llama/Llama-3.1-8B-Instruct
+pipeline_tag: text-generation
+tags:
+- medical
+---
+
+<div align="center">
+<h1>
+  HuatuoGPT-o1-8B
+</h1>
+</div>
+
+<div align="center">
+<a href="https://github.com/FreedomIntelligence/HuatuoGPT-o1" target="_blank">GitHub</a> | <a href="https://arxiv.org/pdf/2412.18925" target="_blank">Paper</a>
+</div>
+
+# <span>Introduction</span>
+**HuatuoGPT-o1** is a medical LLM designed for advanced medical reasoning.  It generates a complex thought process, reflecting and refining its reasoning, before providing a final response. 
+
+For more information, visit our GitHub repository: 
+[https://github.com/FreedomIntelligence/HuatuoGPT-o1](https://github.com/FreedomIntelligence/HuatuoGPT-o1).
+
+# <span>Model Info</span>
+|                      | Backbone     | Supported Languages | Link                                                                  |
+| -------------------- | ------------ | ----- | --------------------------------------------------------------------- |
+| **HuatuoGPT-o1-8B**  | LLaMA-3.1-8B  | English    | [HF Link](https://huggingface.co/FreedomIntelligence/HuatuoGPT-o1-8B) |
+| **HuatuoGPT-o1-70B** | LLaMA-3.1-70B | English    | [HF Link](https://huggingface.co/FreedomIntelligence/HuatuoGPT-o1-70B) |
+| **HuatuoGPT-o1-7B**  | Qwen2.5-7B   | English & Chinese | [HF Link](https://huggingface.co/FreedomIntelligence/HuatuoGPT-o1-7B) |
+| **HuatuoGPT-o1-72B** | Qwen2.5-72B  | English & Chinese | [HF Link](https://huggingface.co/FreedomIntelligence/HuatuoGPT-o1-72B) |
+
+
+
+# <span>Usage</span>
+You can use HuatuoGPT-o1 in the same way as `Llama-3.1-8B-Instruct`. 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 = AutoModelForCausalLM.from_pretrained("FreedomIntelligence/HuatuoGPT-o1-8B",torch_dtype="auto",device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained("FreedomIntelligence/HuatuoGPT-o1-8B")
+
+input_text = "How to stop a cough?"
+messages = [{"role": "user", "content": input_text}]
+
+inputs = tokenizer(tokenizer.apply_chat_template(messages, tokenize=False,add_generation_prompt=True
+), return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=2048)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+HuatuoGPT-o1 adopts a *thinks-before-it-answers* approach, with outputs formatted as:
+
+```
+## Thinking
+[Reasoning process]
+
+## Final Response
+[Output]
+```
+
+# <span>📖 Citation</span>
+```
+@misc{chen2024huatuogpto1medicalcomplexreasoning,
+      title={HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs}, 
+      author={Junying Chen and Zhenyang Cai and Ke Ji and Xidong Wang and Wanlong Liu and Rongsheng Wang and Jianye Hou and Benyou Wang},
+      year={2024},
+      eprint={2412.18925},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2412.18925}, 
+}
+```
+
+
+

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