most citedGMP*: Well-Tuned Gradual Magnitude Pruning Can Outperform Most BERT-Pruning Methods

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cs.CL2024

Enabling High-Sparsity Foundational Llama Models with Efficient Pretraining and Deployment

Abhinav Agarwalla, Abhay Gupta, Alexandre Marques +9

Large language models (LLMs) have revolutionized Natural Language Processing (NLP), but their size creates computational bottlenecks. We introduce a novel approach to create accura…

cs.CL2022★ 1 cited

GMP*: Well-Tuned Gradual Magnitude Pruning Can Outperform Most BERT-Pruning Methods

Eldar Kurtic, Dan Alistarh

We revisit the performance of the classic gradual magnitude pruning (GMP) baseline for large language models, focusing on the classic BERT benchmark on various popular tasks. Despi…