Optimizing Large Language Models with Multi-Degree Low-Rank Approximations

Abstract The increasing computational demands and resource requirements of advanced neural network models have created a growing need for efficient methods to enhance their scalability and deployment, particularly in environments with limited hardware capabilities. Addressing this challenge, the novel application of multi-degree low-rank approximations provides a significant breakthrough, enabling substantial reductions in memory usage and computational costs while preserving high levels of performance. Experiments conducted on the Mistral model demonstrated that this approach can effectively balance the trade-offs between model complexity and accuracy, achieving reduced perplexity and improved classification performance across a range of tasks. The use of varying degrees of rank reduction allowed for tailored optimization, enhancing the model's adaptability to different task requirements and operational environments. The findings suggest that multi-degree low-rank approximations are not only a viable solution for optimizing large-scale neural networks but also a versatile tool for extending the applicability of sophisticated language models to resource-constrained settings. This approach opens up new possibilities for the deployment of advanced language processing capabilities in real-time applications, mobile devices, and other platforms where computational efficiency is critical.