7 citations · 25 across the 24 of their papers we have counts for
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cs.CL2023
How to Prune Your Language Model: Recovering Accuracy on the "Sparsity May Cry'' Benchmark
Eldar Kurtic, Torsten Hoefler, Dan Alistarh
Pruning large language models (LLMs) from the BERT family has emerged as a standard compression benchmark, and several pruning methods have been proposed for this task. The recent…
cs.CL2023★ 4 cited
Sparse Fine-tuning for Inference Acceleration of Large Language Models
Eldar Kurtic, Denis Kuznedelev, Elias Frantar +2
We consider the problem of accurate sparse fine-tuning of large language models (LLMs), that is, fine-tuning pretrained LLMs on specialized tasks, while inducing sparsity in their…