Ekaterina Grishina

ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations

Large language models (LLMs) demonstrate impressive results in natural language processing tasks but require a significant amount of computational and memory resources. Structured matrix representations are a promising way for reducing the number of parameters of these models. However, it seems unre…