The recent progress of large language models (LLMs), including ChatGPT and GPT-4, in comprehending and responding to human instructions has been remarkable. Nevertheless, these models typically perform better in English and have not been explicitly trained for the medical domain, resulting in suboptimal precision in diagnoses, drug recommendations, and other medical advice. Additionally, training and deploying a dialogue model is still believed to be impossible for hospitals, hindering the promotion of LLMs. To tackle these challenges, we have collected databases of medical dialogues in Chinese with ChatGPT's help and adopted several techniques to train an easy-deploy LLM. Remarkably, we were able to fine-tune the ChatGLM-6B on a single A100 80G in 13 hours, which means having a healthcare-purpose LLM can be very affordable. DoctorGLM is currently an early-stage engineering attempt and contain various mistakes. We are sharing it with the broader community to invite feedback and suggestions to improve its healthcare-focused capabilities: https://github.com/xionghonglin/DoctorGLM.

翻译：大型语言模型（LLMs），包括ChatGPT和GPT-4，在理解与响应人类指令方面近期取得了显著进展。然而，这些模型通常在英文环境中表现更佳，且未针对医学领域进行专门训练，导致其在诊断、药物推荐及其他医疗建议方面精度欠佳。此外，医院端仍普遍认为训练和部署对话模型不可实现，这阻碍了LLMs的推广。为应对这些挑战，我们借助ChatGPT收集了中文医学对话数据库，并采用多种技术训练了一个易于部署的LLM。值得注意的是，我们能够在单个A100 80G上于13小时内微调ChatGLM-6B，这意味着拥有医疗用途的LLM可以非常经济实惠。DoctorGLM目前仍处于早期工程尝试阶段，包含各种错误。我们将其分享给更广泛的社区，以邀请反馈和建议改进其医疗保健能力：https://github.com/xionghonglin/DoctorGLM。