908 citations · 2.4k across the 149 of their papers we have counts for
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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…
ELSA: Partial Weight Freezing for Overhead-Free Sparse Network Deployment
Paniz Halvachi, Alexandra Peste, Dan Alistarh +1
We present ELSA, a practical solution for creating deep networks that can easily be deployed at different levels of sparsity. The core idea is to embed one or more sparse networks…
QUIK: Towards End-to-End 4-Bit Inference on Generative Large Language Models
Saleh Ashkboos, Ilia Markov, Elias Frantar +5
Large Language Models (LLMs) from the GPT family have become extremely popular, leading to a race towards reducing their inference costs to allow for efficient local computation. Y…
AsGrad: A Sharp Unified Analysis of Asynchronous-SGD Algorithms
Rustem Islamov, Mher Safaryan, Dan Alistarh
We analyze asynchronous-type algorithms for distributed SGD in the heterogeneous setting, where each worker has its own computation and communication speeds, as well as data distri…
QMoE: Practical Sub-1-Bit Compression of Trillion-Parameter Models
Elias Frantar, Dan Alistarh
Mixture-of-Experts (MoE) architectures offer a general solution to the high inference costs of large language models (LLMs) via sparse routing, bringing faster and more accurate mo…
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…