初始化项目，由ModelHub XC社区提供模型

Model: hongzhouyu/FineMedLM-o1
Source: Original Platform

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README.md

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+---
+license: mit
+datasets:
+- hongzhouyu/FineMed-SFT
+- hongzhouyu/FineMed-DPO
+language:
+- en
+- zh
+base_model:
+- meta-llama/Llama-3.1-8B
+- hongzhouyu/FineMedLM
+library_name: transformers
+tags:
+- medical
+---
+
+<div align="center">
+<h1>
+  FineMedLM-o1
+</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-o1** is a specialized medical LLM engineered for advanced medical reasoning. It employs a multi-step reasoning process, iteratively reflecting on and refining its thought process before delivering a final response.
+
+For more information, visit our GitHub repository.
+
+# <span>Usage</span>
+You can use FineMedLM-o1 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 = "yuhongzhou/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. You need to generate an answer based on the given problem and thoroughly explore the problem through a systematic and long-term thinking process to provide a final and accurate solution. This requires a comprehensive cycle of analysis, summary, exploration, re-evaluation, reflection, backtracking and iteration to form a thoughtful thinking process. Use the background information provided in the text to assist in formulating the answer. Follow these answer guidelines:
+1. Please structure your response into two main sections: **Thought** and **Summarization**.
+2. During the **Thought** phase, think step by step based on the given text content. If the text content is used, it must be expressed.
+3. During the **Summarization** phase, based on the thinking process in the thinking phase, give the final answer to the question.
+Here is the question: """},
+    {"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)
+```
+
+FineMedLM-o1 adopts a *slow-thinking* approach, with outputs formatted as:
+
+```
+**Thought**
+[Reasoning process]
+
+**Summarization**
+[Output]
+```
+
+# <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}, 
+}
+```

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": 128001,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 8.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.50.0",
+  "use_cache": true,
+  "vocab_size": 128256
+}

generation_config.json

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+  "do_sample": true,
+  "eos_token_id": 128001,
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.50.0"
+}

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