Recently, both closed-source LLMs and open-source communities have made
significant strides, outperforming humans in various general domains. However,
their performance in specific professional fields such as medicine, especially
within the open-source community, remains suboptimal due to the complexity of
medical knowledge. We propose Aquila-Med, a bilingual medical LLM based on
Aquila, addressing these challenges through continue pre-training, supervised
fine-tuning (SFT), and reinforcement learning from human feedback (RLHF). We
construct a large-scale Chinese and English medical dataset for continue
pre-training and a high-quality SFT dataset, covering extensive medical
specialties. Additionally, we develop a high-quality Direct Preference
Optimization (DPO) dataset for further alignment. Aquila-Med achieves notable
results across single-turn, multi-turn dialogues, and medical multiple-choice
questions, demonstrating the effectiveness of our approach. We open-source the
datasets and the entire training process, contributing valuable resources to
the research community. Our models and datasets will released at
this https URL

提出 Aquila-Med：一种基于 Aquila 的双语医学 LLM，通过持续预训练、监督微调以及强化学习从人类反馈中进行训练，解决医学领域中开源社区性能的问题，并在单轮对话、多轮对话和医学多项选择题方面取得显著成果。