Modern deep neural networks, particularly recent large language models, come with
massive model sizes that require significant computational and storage resources. To …

Abstract Low-Rank Adaptation (LoRA), which updates the dense neural network layers with
pluggable low-rank matrices, is one of the best performed parameter efficient fine-tuning …

Large language models (LLMs) show inherent brittleness in their safety mechanisms, as
evidenced by their susceptibility to jailbreaking and even non-malicious fine-tuning. This …

Large Language Models (LLMs) have attracted extensive attention due to their remarkable
performance across various tasks. However, the substantial computational and memory …

Large Language Models (LLMs) represent a class of deep learning models adept at
understanding natural language and generating coherent text in response to prompts or …

The rapid growth of Large Language Models (LLMs) has been a driving force in
transforming various domains, reshaping the artificial general intelligence landscape …

The considerable size of Large Language Models (LLMs) presents notable deployment
challenges, particularly on resource-constrained hardware. Structured pruning, offers an …

Transformer based large language models have achieved tremendous success. However,
the significant memory and computational costs incurred during the inference process make …

Large language models (LLM) have been extensively applied in various natural language
tasks and domains, but their applicability is constrained by the large number of parameters …

Deep neural networks (DNNs) have recently achieved impressive success across a wide
range of real-world vision and language processing tasks, spanning from image …