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.

本研究旨在为大型模型赋予中医理论特色的专业知识，以构建和组织一个中医领域的专业语料库，并成功基于LLaMA开发出首个经历从预训练到监督微调完整训练流程的中医领域大型模型Qibo。此外，我们还开发了用于评估中医领域大型模型性能的专用工具Qibo-benchmark，为对不同模型的理解和应用能力在传统中医领域进行量化和比较提供重要依据，并为中医智能助手未来研究方向和实际应用提供指导。最终，我们进行了充分的实验证明Qibo在中医领域具有良好的性能。

綺柏: 一個針對中醫藥的大型語言模型