commit fb83a80c251058c896ac1242b26dea2300ff5ff0 Author: ModelHub XC Date: Tue May 5 14:31:42 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: hongzhouyu/FineMedLM-o1 Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..51a5b10 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,5 @@ +model-00001-of-00004.safetensors filter=lfs diff=lfs merge=lfs -text +model-00002-of-00004.safetensors filter=lfs diff=lfs merge=lfs -text +model-00003-of-00004.safetensors filter=lfs diff=lfs merge=lfs -text +model-00004-of-00004.safetensors filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..09fbccb --- /dev/null +++ b/README.md @@ -0,0 +1,107 @@ +--- +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 +--- + +
+

+ FineMedLM-o1 +

+
+ +
+GitHub | Paper +
+ +# Introduction +**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. + +# Usage +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] +``` + +# Citation +``` +@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}, +} +``` \ No newline at end of file diff --git a/config.json b/config.json new file mode 100644 index 0000000..a153282 --- /dev/null +++ b/config.json @@ -0,0 +1,35 @@ +{ + "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" 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tools_in_user_message is defined %}\n {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n {%- set date_string = \"26 Jul 2024\" %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n{%- else %}\n {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message + builtin tools #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if builtin_tools is defined or tools is not none %}\n {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{%- if builtin_tools is defined %}\n {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\\n\\n\"}}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n {{- \"Given the following functions, please respond with a JSON for a function call \" 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