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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…
math.OC2024
Shadowheart SGD: Distributed Asynchronous SGD with Optimal Time Complexity Under Arbitrary Computation and Communication Heterogeneity
Alexander Tyurin, Marta Pozzi, Ivan Ilin +1
We consider nonconvex stochastic optimization problems in the asynchronous centralized distributed setup where the communication times from workers to a server can not be ignored,…