39 lines
2.3 KiB
Markdown
39 lines
2.3 KiB
Markdown
---
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license: apache-2.0
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datasets:
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- BAAI/IndustryInstruction_Health-Medicine
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- BAAI/IndustryInstruction
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base_model:
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- MonteXiaofeng/CareBot_Medical_multi-llama3-8b-base
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tags:
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- 医疗对话模型
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- 中英文多语种医疗对话模型
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- chatmodel
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---
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This model is trained from the model: MonteXiaofeng/CareBot_Medical_multi-llama3-8b-base, training data is: BAAI/IndustryInstruction_Health-Medicine， To enhance the model's ability to follow medical instructions and better adapt to specific medical scenarios, we conduct the supervised fine-tuning. This process involves using conversational-style data (comprising both queries and responses) to finetune the pretrained LLM. In the following sections, we will explore the details of data construction and training methods.
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## Data Construction
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Our SFT dataset comprises a diverse array of question types, including multiple-choice questions from medical exams, single-turn disease diagnoses, and multi-turn health consultations. It integrates data from seven publicly available sources: Chinese Medical Dialogue Data\footnote{https://github.com/Toyhom/Chinese-medical-dialogue-data}, Huatuo26M , MedDialog , ChatMed Consult Dataset , ChatDoctor , CMB\footnote{https://github.com/FreedomIntelligence/CMB}, and MedQA . We preserve portions of authentic doctor-patient conversations and augment the dataset by rewriting the remaining content. For these rewrites, we use real-world medical scenarios as prompts and generate responses via GPT-4. We believe this ensures the diversity of the SFT dataset, which can help the CareBot better adapt to different types of medical problems and patient situations, thereby improving its performance in a variety of scenarios.
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## evaluation
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evaluation on benchmark is bellow.
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gsb result with other medical LLMS
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# Acknowledgements
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This work is supported by the National Science and Technology Major Project (No. 2022ZD0116314).
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本项目受新一代人工智能国家科技重大专项（No. 2022ZD0116314）支持。
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