In the field of Artificial Intelligence, Large Language Models (LLMs) have demonstrated significant advances in user intent understanding and response in a number of specialized domains, including medicine, law, and finance. However, in the unique domain of traditional Chinese medicine (TCM), the performance enhancement of LLMs is challenged by the essential differences between its theories and modern medicine, as well as the lack of specialized corpus resources. In this paper, we aim to construct and organize a professional corpus in the field of TCM, to endow the large model with professional knowledge that is characteristic of TCM theory, and to successfully develop the Qibo model based on LLaMA, which is the first LLM in the field of TCM to undergo a complete training process from pre-training to Supervised Fine-Tuning (SFT). Furthermore, we develop the Qibo-benchmark, a specialized tool for evaluating the performance of LLMs, which is a specialized tool for evaluating the performance of LLMs in the TCM domain. This tool will provide an important basis for quantifying and comparing the understanding and application capabilities of different models in the field of traditional Chinese medicine, and provide guidance for future research directions and practical applications of intelligent assistants for traditional Chinese medicine. Finally, we conducted sufficient experiments to prove that Qibo has good performance in the field of traditional Chinese medicine.

翻译：在人工智能领域，大型语言模型（LLMs）在多个专业领域（包括医学、法律和金融）的用户意图理解与响应方面已展现出显著进展。然而，在中医这一独特领域，其理论与现代医学的本质差异以及专业语料资源的匮乏，对LLMs的性能提升构成了挑战。本文旨在构建和组织中医领域的专业语料库，赋予大型模型体现中医理论特色的专业知识，并成功开发了基于LLaMA的Qibo模型——这是中医领域首个经历从预训练到监督微调（SFT）完整训练流程的大型语言模型。此外，我们构建了Qibo-benchmark这一专用于评估LLMs在中医领域性能的工具。该工具将为量化比较不同模型在中医领域的理解与应用能力提供重要依据，并为未来中医智能助手的研究方向与实际应用提供指引。最后，我们通过充分实验证明，Qibo在中医领域具有良好的表现。