
A novel pruning technique called Blockwise Parameter-Efficient Sparsity Allocation (BESA) that optimizes pruning rates across different layers of large language models to minimize performance degradation.


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blockwise-parameter-efficient-sparsity-allocation-besa-an-efficient-technique-for-pruning-large-language-models


Blockwise Parameter-Efficient Sparsity Allocation (BESA): An Efficient Technique for Pruning Large Language Models


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A simple and effective pruning method, Wanda, can induce high sparsity in pretrained large language models without any retraining or weight update.


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Effective Pruning of Large Language Models without Retraining



Outlier-Weighed Layerwise Sparsity (OWL) is a novel pruning methodology that leverages the non-uniform distribution of outlier features across different layers of Large Language Models (LLMs) to achieve superior performance at high sparsity levels compared to existing pruning techniques.


outlier-weighed-layerwise-sparsity-owl-a-novel-approach-to-efficiently-prune-large-language-models-to-high-sparsity


Outlier-Weighed Layerwise Sparsity (OWL): A Novel Approach to Efficiently Prune Large Language Models to High Sparsity
