4 citations · 5 across the 10 of their papers we have counts for
4 papers · 1 filter
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…
Panza: Design and Analysis of a Fully-Local Personalized Text Writing Assistant
Armand Nicolicioiu, Eugenia Iofinova, Andrej Jovanovic +6
The availability of powerful open-source large language models (LLMs) opens exciting use-cases, such as using personal data to fine-tune these models to imitate a user's unique wri…
Correlated Quantization for Faster Nonconvex Distributed Optimization
Andrei Panferov, Yury Demidovich, Ahmad Rammal +1
Quantization (Alistarh et al., 2017) is an important (stochastic) compression technique that reduces the volume of transmitted bits during each communication round in distributed m…
Extreme Compression of Large Language Models via Additive Quantization
Vage Egiazarian, Andrei Panferov, Denis Kuznedelev +3
The emergence of accurate open large language models (LLMs) has led to a race towards performant quantization techniques which can enable their execution on end-user devices. In th…