In customized scenarios， it is urgent to enhance the understanding and generation capabilities of large language models （LLMs） in specific vertical domains. We propose a paradigm for developing vertical-domain LLM system named “Wuxin”， which covers a series of development methods for LLM systems， including architecture， data， model， and training. Wuxin utilizes human-in-the-loop data augmentation to improve the quality of military training injury question and answer datasets， and employs the GaLore strategy to perform efficient full-parameter fine-tuning on small LLMs. Experimental results show that the adopted full-parameter fine-tuning method outperforms LoRA fine-tuning in terms of convergence and accuracy. Furthermore，Wuxin demonstrates significant advantages in understanding professional military training injury knowledge， as well as overcoming hallucinations. Our achievements can provide references for the design and application of question-answering LLM systems in vertical domains.