The integration of Large Language Models (LLMs) into healthcare promises to
transform medical diagnostics, research, and patient care. Yet, the progression
of medical LLMs faces obstacles such as complex training requirements, rigorous
evaluation demands, and the dominance of proprietary models that restrict
academic exploration. Transparent, comprehensive access to LLM resources is
essential for advancing the field, fostering reproducibility, and encouraging
innovation in healthcare AI. We present Hippocrates, an open-source LLM
framework specifically developed for the medical domain. In stark contrast to
previous efforts, it offers unrestricted access to its training datasets,
codebase, checkpoints, and evaluation protocols. This open approach is designed
to stimulate collaborative research, allowing the community to build upon,
refine, and rigorously evaluate medical LLMs within a transparent ecosystem.
Also, we introduce Hippo, a family of 7B models tailored for the medical
domain, fine-tuned from Mistral and LLaMA2 through continual pre-training,
instruction tuning, and reinforcement learning from human and AI feedback. Our
models outperform existing open medical LLMs models by a large-margin, even
surpassing models with 70B parameters. Through Hippocrates, we aspire to unlock
the full potential of LLMs not just to advance medical knowledge and patient
care but also to democratize the benefits of AI research in healthcare, making
them available across the globe.

医疗领域的大型语言模型的集成将转变医学诊断、研究和患者护理，Hippocrates 是一个开源 LLM 框架，通过无限制地提供训练数据集、代码、检查点和评估协议来促进协作研究，解锁 LLM 的全部潜力，并使其在全球范围内享有人工智能研究的好处。