Compressing LLMs: The Truth is Rarely Pure and Never Simple Apple Machine Learning Research
Despite their remarkable achievements, modern Large Language Models (LLMs) encounter exorbitant computational and memory footprints. Recently, several works have shown significant success in training-free and data-free compression (pruning and quantization) of LLMs achieving 50-60% sparsity and reducing the bit-width down to 3 or 4 bits… Read More »Compressing LLMs: The Truth is Rarely Pure and Never Simple Apple Machine Learning Research