3 papers
cs.LG2025
Thanos: A Block-wise Pruning Algorithm for Efficient Large Language Model Compression
Ivan Ilin, Peter Richtarik
This paper presents Thanos, a novel weight-pruning algorithm designed to reduce the memory footprint and enhance the computational efficiency of large language models (LLMs) by rem…
cs.LG2025
Hessian of Perplexity for Large Language Models by PyTorch autograd (Open Source)
Ivan Ilin
Computing the full Hessian matrix -- the matrix of second-order derivatives for an entire Large Language Model (LLM) is infeasible due to its sheer size. In this technical report,…
cs.LG2024
Pushing the Limits of Large Language Model Quantization via the Linearity Theorem
Vladimir Malinovskii, Andrei Panferov, Ivan Ilin +3
Quantizing large language models has become a standard way to reduce their memory and computational costs. Typically, existing methods focus on breaking down the problem into indiv…