46 lines
1.4 KiB
Markdown
46 lines
1.4 KiB
Markdown
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---
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license: afl-3.0
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base_model:
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- Qwen/Qwen2.5-7B-Instruct
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pipeline_tag: text-generation
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tags:
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- medical
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---
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# Instruction
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For more information, visit our GitHub repository: https://github.com/medfound/medfound
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# Quickstart
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``` python
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import pandas as pd
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from transformers import AutoTokenizer, AutoModelForCausalLM
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model_path = "medicalai/ClinicalGPT-R1-Qwen-7B-EN-preview"
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto")
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data = pd.read_json('data/test.zip', lines=True).iloc[1]
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prompt = f"{data["context"]}\n\nPlease provide a detailed and comprehensive diagnostic analysis of this medical record, and give the diagnostic results.\n"
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messages = [
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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input_ids = tokenizer([text], return_tensors="pt").to(model.device)
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output_ids = model.generate(**input_ids, max_new_tokens=2048, temperature=0.7, do_sample=True).to(model.device)
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generated_text = tokenizer.decode(output_ids[0,len(input_ids[0]):], skip_special_tokens=True)
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print("Generated Output:\n", generated_text)
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```
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# Citation
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If you find our work helpful, feel free to give us a cite.
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```
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Wang, G., Liu, X., Liu, H., Yang, G. et al. A Generalist Medical Language Model for Disease Diagnosis Assistance. Nat Med (2025). https://doi.org/10.1038/s41591-024-03416-6
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```
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