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：近期，包括ChatGPT和GPT-4在内的大型语言模型（LLMs）在理解和回应人类指令方面取得了显著进展。然而，这些模型通常对英文表现更佳，且未经过医疗领域的专门训练，因此在诊断、药物推荐及其他医疗建议方面的精确度欠佳。此外，训练和部署对话模型对医院而言仍被认为不可行，这阻碍了LLMs的推广。为应对这些挑战，我们借助ChatGPT收集了中文医疗对话数据库，并采用了多项技术来训练一个易于部署的LLM。值得注意的是，我们仅用13小时在一块A100 80G上便成功微调了ChatGLM-6B，这意味着拥有一个面向医疗保健的LLM可能非常经济实惠。DoctorGLM目前仍处于早期工程尝试阶段，存在各种错误。我们将其与更广泛的社区共享，以征求反馈和建议，从而提升其医疗保健能力：https://github.com/xionghonglin/DoctorGLM。