初始化项目，由ModelHub XC社区提供模型

Model: hongzhouyu/FineMedLM
Source: Original Platform

README.md

@@ -0,0 +1,92 @@
+---
+license: mit
+datasets:
+- hongzhouyu/FineMed-SFT
+language:
+- en
+- zh
+base_model:
+- meta-llama/Llama-3.1-8B
+library_name: transformers
+tags:
+- medical
+---
+
+<div align="center">
+<h1>
+  FineMedLM
+</h1>
+</div>
+
+<div align="center">
+<a href="https://github.com/hongzhouyu/FineMed" target="_blank">GitHub</a> | <a href="https://arxiv.org/abs/2501.09213" target="_blank">Paper</a>
+</div>
+
+# <span>Introduction</span>
+**FineMedLM** is a medical chat LLM trained via SFT on meticulously crafted synthetic data. By further applying DPO, the model acquires enhanced deep reasoning capabilities, culminating in the development of [FineMedLM-o1](https://huggingface.co/hongzhouyu/FineMedLM-o1).
+
+For more information, visit our GitHub repository.
+
+# <span>Usage</span>
+You can use FineMedLM in the same way as `Llama-3.1-8B-Instruct`:
+
+（⚠️**Note**: Please use the system prompt we provide to achieve better inference results）
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+main_model_name = "hongzhouyu/FineMedLM"  
+model = AutoModelForCausalLM.from_pretrained(main_model_name, device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained(main_model_name)
+
+prompt = (
+    """The following are multiple choice questions (with answers) about health. Think step by step and then finish your answer with "the answer is (X)" where X is the correct letter choice.
+
+
+Question:
+Polio can be eradicated by which of the following?
+Options:
+A. Herbal remedies
+B. Use of antibiotics
+C. Regular intake of vitamins
+D. Administration of tetanus vaccine
+E. Attention to sewage control and hygiene
+F. Natural immunity acquired through exposure
+G. Use of antiviral drugs
+Answer: Let's think step by step.
+"""
+)
+
+
+messages = [
+    {"role": "system", "content": "You are a helpful professional doctor. The user will give you a medical question, and you should answer it in a professional way."},
+    {"role": "user", "content": prompt}
+]
+
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+print(text)
+
+model_inputs = tokenizer(text, return_tensors="pt").to(model.device)
+
+print("-----start generate-----")
+generated_ids = model.generate(
+    model_inputs.input_ids,
+    max_new_tokens=2048,
+    eos_token_id=tokenizer.eos_token_id
+)
+
+answer = tokenizer.decode(generated_ids[0], skip_special_tokens=False)
+print(answer)
+```
+
+# <span>Citation</span>
+```
+@misc{yu2025finemedlmo1enhancingmedicalreasoning,
+    title={FineMedLM-o1: Enhancing the Medical Reasoning Ability of LLM from Supervised Fine-Tuning to Test-Time Training}, 
+    author={Hongzhou Yu and Tianhao Cheng and Ying Cheng and Rui Feng},
+    year={2025},
+    eprint={2501.09213},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL},
+    url={https://arxiv.org/abs/2501.09213}, 
+}
+```