license: apache-2.0
datasets:
- BAAI/IndustryInstruction_Health-Medicine
- BAAI/IndustryInstruction
base_model:
- MonteXiaofeng/CareBot_Medical_multi-llama3-8b-base
tags:
- 医疗对话模型
- 中英文多语种医疗对话模型
- chatmodel
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.
Data Construction
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.
evaluation
evaluation on benchmark is bellow.
gsb result with other medical LLMS
