Multi Codebook

Multi-codebook techniques aim to improve the efficiency and accuracy of various machine learning models by representing data using multiple smaller codebooks instead of a single large one. Current research focuses on applying this approach to vector quantization (VQ) within autoencoders and large language models (LLMs), often incorporating product quantization and addressing challenges like "index collapse" and communication overhead in federated learning settings. These advancements enable significant model compression, leading to faster inference, reduced memory footprint, and improved performance in applications ranging from speech synthesis and image processing to resource-constrained deployments of LLMs.