SeedLM: Compressing LLM Weights into Seeds of Pseudo-Random Generators - Apple Machine Learning Research

Large Language Models (LLMs) have transformed natural language processing, but face significant challenges in widespread deployment due to their high runtime cost. In this paper, we introduce SeedLM, a novel post-training compression method that uses seeds of a pseudo-random generator to encode and compress model weights.