The recent emergence of various large language models, which can be fine-tuned with minimal instruction data, has demonstrated impressive performance across various tasks. However, a phenomenon of forgetting occurs during life- long fine-tuning because training on new tasks interferes with the previously acquired knowl- edge. To mitigate catastrophic forgetting, con- ventional data replay methods achieve high per- formance, but at the cost of compromising data privacy and security. This paper introduces a dataless distribution replay approach for life- long fine-tuning. Concretely, the distribution distillation is applied to replay the output dis- tribution of the linear layers at previous task stages. The optimal solution for this distri- bution replay can be directly computed using the retained inner product matrix of the input data, thereby eliminating the need for previ- ous data. Additionally, Singular Value Decom- position (SVD) and module accumulation are employed to further enhance the performance of dataless distribution replay method. Finally, the evaluation is conducted in a lifelong fine- tuning scenario involving multiple tasks. The experimental results and analysis show that the proposed method achieves significant improve- ments compared to several strong lifelong fine- tuning methods.