SparseGPT
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LLM-Pruning Collection: JAX Repo for LLM Compression
Read Full Article: LLM-Pruning Collection: JAX Repo for LLM CompressionZlab Princeton researchers have developed the LLM-Pruning Collection, a JAX-based repository that consolidates major pruning algorithms for large language models into a single, reproducible framework. This collection aims to simplify the comparison of block level, layer level, and weight level pruning methods under a consistent training and evaluation setup on both GPUs and TPUs. It includes implementations of various pruning methods such as Minitron, ShortGPT, Wanda, SparseGPT, Magnitude, Sheared LLaMA, and LLM-Pruner, each designed to optimize model performance by removing redundant or less important components. The repository also integrates advanced training and evaluation tools, providing a platform for engineers to verify results against established baselines. This matters because it streamlines the process of enhancing large language models, making them more efficient and accessible for practical applications.