908 citations · 2.4k across the 149 of their papers we have counts for
17 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…
"Give Me BF16 or Give Me Death"? Accuracy-Performance Trade-Offs in LLM Quantization
Eldar Kurtic, Alexandre Marques, Shubhra Pandit +2
Quantization is a powerful tool for accelerating large language model (LLM) inference, but the accuracy-performance trade-offs across different formats remain unclear. In this pape…
LDAdam: Adaptive Optimization from Low-Dimensional Gradient Statistics
Thomas Robert, Mher Safaryan, Ionut-Vlad Modoranu +1
We introduce LDAdam, a memory-efficient optimizer for training large models, that performs adaptive optimization steps within lower dimensional subspaces, while consistently explor…
EvoPress: Accurate Dynamic Model Compression via Evolutionary Search
Oliver Sieberling, Denis Kuznedelev, Eldar Kurtic +1
The high computational costs of large language models (LLMs) have led to a flurry of research on LLM compression, via methods such as quantization, sparsification, or structured pr…
Scalable Mechanistic Neural Networks for Differential Equations and Machine Learning
Jiale Chen, Dingling Yao, Adeel Pervez +2
We propose Scalable Mechanistic Neural Network (S-MNN), an enhanced neural network framework designed for scientific machine learning applications involving long temporal sequences…
Accurate Compression of Text-to-Image Diffusion Models via Vector Quantization
Vage Egiazarian, Denis Kuznedelev, Anton Voronov +5
Text-to-image diffusion models have emerged as a powerful framework for high-quality image generation given textual prompts. Their success has driven the rapid development of produ…