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Integrating large language models (LLMs) into healthcare presents potentialbut faces challenges. Directly pre-training LLMs for domains like medicine isresource-heavy and sometimes unfeasible. Sole reliance on SupervisedFine-tuning (SFT) can result in overconfident predictions and may not tap intodomain specific insights. Addressing these challenges, we present a multi-stagetraining method combining Domain-specific Continued Pre-training (DCPT), SFT,and Direct Preference Optimization (DPO). A notable contribution of our studyis the introduction of a 3Gb Chinese Medicine (ChiMed) dataset, encompassingmedical question answering, plain texts, knowledge graphs, and dialogues,segmented into three training stages. The medical LLM trained with ourpipeline, Qilin-Med, exhibits significant performance boosts. In the CPT andSFT phases, it achieves 38.4% and 40.0% accuracy on the CMExam, surpassingBaichuan-7B's 33.5%. In the DPO phase, on the Huatuo-26M test set, it scores16.66 in BLEU-1 and 27.44 in ROUGE1, outperforming the SFT's 12.69 and 24.21.This highlights the strength of our training approach in refining LLMs formedical applications.

Qilin-Med: Multi-stage Knowledge Injection Advanced Medical Large Language Model