Large language models (LLMs) face the challenges in fine-tuning and deployment due to
their high memory demands and computational costs. While parameter-efficient fine-tuning …

Large language models (LLMs) are crucial in modern natural language processing and
artificial intelligence. However, they face challenges in managing their significant memory …

Abstract Model quantization is widely used to compress and accelerate deep neural
networks. However recent studies have revealed the feasibility of weaponizing model …

The burgeoning field of Large Language Models (LLMs), exemplified by sophisticated
models like OpenAI's ChatGPT, represents a significant advancement in artificial …

Large foundation models, including large language models (LLMs), vision transformers
(ViTs), diffusion, and LLM-based multimodal models, are revolutionizing the entire machine …

Large language models (LLMs) have achieved remarkable advancements in natural
language processing, showcasing exceptional performance across various tasks. However …

Large foundation models, including large language models, vision transformers, diffusion,
and large language model based multimodal models, are revolutionizing the entire machine …

Abstract Segment Anything Model (SAM) has achieved impressive performance in many
computer vision tasks. However as a large-scale model the immense memory and …

Large Language Models (LLMs) have proven their exceptional capabilities in performing
language-related tasks. However, their deployment poses significant challenges due to their …

In the rapidly evolving field of artificial intelligence (AI), a paradigm shift is underway. We are
witnessing the transition from specialized, fragmented deep learning models to versatile …