In ancient Chinese books, punctuation marks are typically absent in engraved texts. Sentence segmentation and punctuation heavily rely on the meticulous efforts of experts and scholars. Therefore, the work of automatic punctuation and sentence segmentation plays a very important role in promoting ancient books, as well as the inheritance of Chinese culture. In this paper, we present a method for fine-tuning downstream tasks for large language model using the LoRA approach, leveraging the EvaHan2024 dataset. This method ensures robust output and high accuracy while inheriting the knowledge from the large pre-trained language model Xunzi.