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Model: BAAI/CareBot_Medical_multi-llama3-8b-instruct Source: Original Platform
2026-05-16 15:15:37 +08:00
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+---
+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.
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/642f6c64f945a8a5c9ee5b5d/kqvLfcFtkw6lHcHtCySLr.png)
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/642f6c64f945a8a5c9ee5b5d/UiokfV8qcYEyCWEa__820.png)
+
+
+gsb result with other medical LLMS
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/642f6c64f945a8a5c9ee5b5d/rOnnIoY9MaXPTFD_R10r1.png)
+
+
+
+
+# Acknowledgements
+
+This work is supported by the National Science and Technology Major Project (No. 2022ZD0116314).
+
+本项目受新一代人工智能国家科技重大专项（No. 2022ZD0116314）支持。