SparseGPT: One-shot Pruning of Large Language Models

SparseGPT is a novel one-shot pruning technique designed to compress large language models, particularly those from the Generative Pre-trained Transformer (GPT) family. The method efficiently reduces model size without sacrificing accuracy, offering a practical way to deploy massive models in resource-constrained environments.

SparseGPT offers a one-shot pruning approach that avoids costly retraining, making it significantly more efficient for compressing large language models like GPT variants. The method can achieve high sparsity levels while maintaining minimal accuracy loss, providing a promising solution for improving the deployment of powerful language models.

Read full paper: https://arxiv.org/abs/2301.00774

Tags: Artificial Intelligence, Natural Language Processing, Model Compression