11 citations · 23 across the 9 of their papers we have counts for
4 papers · 1 filter
WUSH: Near-Optimal Adaptive Transforms for LLM Quantization
Jiale Chen, Vage Egiazarian, Roberto L. Castro +2
Quantizing LLM weights and activations is a standard approach for efficient deployment, but a few extreme outliers can stretch the dynamic range and amplify low-bit quantization er…
CAGE: Curvature-Aware Gradient Estimation For Accurate Quantization-Aware Training
Soroush Tabesh, Mher Safaryan, Andrei Panferov +2
Despite significant work on low-bit quantization-aware training (QAT), there is still an accuracy gap between such techniques and native training. To address this, we introduce CAG…
The Geometry of LLM Quantization: GPTQ as Babai's Nearest Plane Algorithm
Jiale Chen, Yalda Shabanzadeh, Elvir Crnčević +2
Quantizing the weights of large language models (LLMs) from 16-bit to lower bitwidth is the de facto approach to deploy massive transformers onto more affordable accelerators. Whil…
FFT-based Dynamic Subspace Selection for Low-Rank Adaptive Optimization of Large Language Models
Ionut-Vlad Modoranu, Mher Safaryan, Erik Schultheis +3
Low-rank optimization has emerged as a promising direction in training large language models (LLMs) to improve running time and reduce the memory usage of adaptive optimizers by co…