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.

翻译：大型语言模型与医疗领域的融合有望革新医学诊断、研究和患者护理。然而，医学大语言模型的发展面临诸多障碍，包括复杂的训练需求、严苛的评估要求，以及限制学术探索的专有模型主导地位。透明、全面地获取大语言模型资源对于推动该领域发展、促进可重复性研究以及鼓励医疗AI创新至关重要。我们提出Hippocrates——一个专为医疗领域开发的开源大语言模型框架。与以往工作形成鲜明对比的是，该框架提供对其训练数据集、代码库、检查点和评估协议的无限制访问。这种开放方式旨在激发协作研究，使学界能够在透明生态系统中基于医疗大语言模型进行构建、优化和严格评估。此外，我们引入Hippo——一个面向医疗领域定制的70亿参数模型家族，该模型通过持续预训练、指令微调，以及基于人类与AI反馈的强化学习，从Mistral和LLaMA2微调而来。我们的模型以显著优势超越现有开源医疗大语言模型，甚至优于700亿参数模型。通过Hippocrates，我们不仅致力于释放大语言模型在推动医学知识和患者护理方面的全部潜力，更希望将AI研究在医疗领域的成果普惠至全球，实现其民主化。