Recently, the instruction-tuning of large language models is a crucial area of research in the field of natural language processing. Due to resource and cost limitations, several researchers have employed parameter-efficient tuning techniques, such as LoRA, for instruction tuning, and have obtained encouraging results In comparison to full-parameter fine-tuning, LoRA-based tuning demonstrates salient benefits in terms of training costs. In this study, we undertook experimental comparisons between full-parameter fine-tuning and LoRA-based tuning methods, utilizing LLaMA as the base model. The experimental results show that the selection of the foundational model, training dataset scale, learnable parameter quantity, and model training cost are all important factors. We hope that the experimental conclusions of this paper can provide inspiration for training large language models, especially in the field of Chinese, and help researchers find a better trade-off strategy between training cost and model performance. To facilitate the reproduction of the paper's results, the dataset, model and code will be released.

本研究通过实验比较全参数微调和 LoRA 微调方法，使用 LLaMA 作为基础模型，发现基础模型选择、训练数据集规模、可训练参数数量和模型训练成本都是重要因素。该实验结论可以为训练大型语言模型提供启示，特别是在中文领域，帮助研究人员找到更好的训练成本和模型性能的平衡策略。为了方便本文结果的复现，我们将公开数据集、模型和代码。

基于完整参数和LoRA的微调在指令遵循大型语言模型的比较研究