3 citations · 6 across the 13 of their papers we have counts for
5 papers · 1 filter
FineGates: LLMs Finetuning with Compression using Stochastic Gates
Jonathan Svirsky, Yehonathan Refael, Ofir Lindenbaum
Large Language Models (LLMs), with billions of parameters, present significant challenges for full finetuning due to the high computational demands, memory requirements, and imprac…
TransformLLM: Adapting Large Language Models via LLM-Transformed Reading Comprehension Text
Iftach Arbel, Yehonathan Refael, Ofir Lindenbaum
Large Language Models (LLMs) have shown promise in highly-specialized domains, however challenges are still present in aspects of accuracy and costs. These limitations restrict the…
AdaRankGrad: Adaptive Gradient-Rank and Moments for Memory-Efficient LLMs Training and Fine-Tuning
Yehonathan Refael, Jonathan Svirsky, Boris Shustin +2
Training and fine-tuning large language models (LLMs) come with challenges related to memory and computational requirements due to the increasing size of the model weights and the…
SLIP: Securing LLMs IP Using Weights Decomposition
Yehonathan Refael, Adam Hakim, Lev Greenberg +6
Large language models (LLMs) have recently seen widespread adoption in both academia and industry. As these models grow, they become valuable intellectual property (IP), reflecting…
Unveiling Multiple Descents in Unsupervised Autoencoders
Kobi Rahimi, Yehonathan Refael, Tom Tirer +1
The phenomenon of double descent has challenged the traditional bias-variance trade-off in supervised learning but remains unexplored in unsupervised learning, with some studies ar…