26 citations · 80 across the 37 of their papers we have counts for
8 papers · 1 filter
Beyond Outliers: A Study of Optimizers Under Quantization
Georgios Vlassis, Saleh Ashkboos, Alexandra Volkova +2
As new optimizers gain traction and model quantization becomes standard for efficient deployment, a key question arises: how does the choice of optimizer affect model performance i…
DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing
Afif Boudaoud, Alexandru Calotoiu, Marcin Copik +1
Automatic differentiation (AD) is a set of techniques that systematically applies the chain rule to compute the gradients of functions without requiring human intervention. Althoug…
EfQAT: An Efficient Framework for Quantization-Aware Training
Saleh Ashkboos, Bram Verhoef, Torsten Hoefler +2
Quantization-aware training (QAT) schemes have been shown to achieve near-full precision accuracy. They accomplish this by training a quantized model for multiple epochs. This is c…
MARLIN: Mixed-Precision Auto-Regressive Parallel Inference on Large Language Models
Elias Frantar, Roberto L. Castro, Jiale Chen +2
As inference on Large Language Models (LLMs) emerges as an important workload in machine learning applications, weight quantization has become a standard technique for efficient GP…
SliceGPT: Compress Large Language Models by Deleting Rows and Columns
Saleh Ashkboos, Maximilian L. Croci, Marcelo Gennari do Nascimento +2
Large language models have become the cornerstone of natural language processing, but their use comes with substantial costs in terms of compute and memory resources. Sparsificatio…
QUIK: Towards End-to-End 4-Bit Inference on Generative Large Language Models
Saleh Ashkboos, Ilia Markov, Elias Frantar +5
Large Language Models (LLMs) from the GPT family have become extremely popular, leading to a race towards reducing their inference costs to allow for efficient local computation. Y…