most citedSparse Fine-tuning for Inference Acceleration of Large Language Models

4 citations · 4 across the 3 of their papers we have counts for

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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.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…