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：最近，包括ChatGPT和GPT-4在内的大型语言模型（LLM）在理解和响应人类指令方面取得了显着进展。然而，这些模型通常在英语中表现更好，并且尚未明确为医疗领域进行培训，导致诊断、药物推荐和其他医学建议的精度不佳。此外，训练和部署对话模型仍被认为对于医院来说是不可能的事情，阻碍了LLM的推广。为了解决这些挑战，我们使用ChatGPT的帮助收集了中文医学对话的数据库，并采用了几种技术来训练易于部署的医疗LLM。惊人的是，我们能够在单个A100 80G上在13个小时内对ChatGLM-6B进行微调，这意味着拥有面向医疗用途的LLM可能非常实惠。DoctorGLM目前还处于早期工程尝试阶段，存在各种错误。我们正在与广泛的社区分享它，以邀请反馈和建议以改进其医疗重点能力：https://github.com/xionghonglin/DoctorGLM。
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