3 papers
cs.LG2025
Cache Me If You Must: Adaptive Key-Value Quantization for Large Language Models
Alina Shutova, Vladimir Malinovskii, Vage Egiazarian +5
Efficient real-world deployments of large language models (LLMs) rely on Key-Value (KV) caching for processing and generating long outputs, reducing the need for repetitive computa…
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…
cs.LG2024
PV-Tuning: Beyond Straight-Through Estimation for Extreme LLM Compression
Vladimir Malinovskii, Denis Mazur, Ivan Ilin +5
There has been significant interest in "extreme" compression of large language models (LLMs), i.e., to 1-2 bits per parameter, which allows such models to be executed efficiently o…