3 papers
cs.LG2025
Motivating Next-Gen Accelerators with Flexible (N:M) Activation Sparsity via Benchmarking Lightweight Post-Training Sparsification Approaches
Shirin Alanova, Kristina Kazistova, Ekaterina Galaeva +7
The demand for efficient large language model (LLM) inference has intensified the focus on sparsification techniques. While semi-structured (N:M) pruning is well-established for we…
cs.LG2025
From 2:4 to 8:16 sparsity patterns in LLMs for Outliers and Weights with Variance Correction
Egor Maximov, Yulia Kuzkina, Azamat Kanametov +4
As large language models (LLMs) grow in size, efficient compression techniques like quantization and sparsification are critical. While quantization maintains performance with redu…
cs.LG2024
GIFT-SW: Gaussian noise Injected Fine-Tuning of Salient Weights for LLMs
Maxim Zhelnin, Viktor Moskvoretskii, Egor Shvetsov +4
Parameter Efficient Fine-Tuning (PEFT) methods have gained popularity and democratized the usage of Large Language Models (LLMs). Recent studies have shown that a small subset of w…