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

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

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

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

Large language models (LLMs) have shown impressive performance on language tasks but
face challenges when deployed on resource-constrained devices due to their extensive …

Large language models (LLMs) are omnipresent, however their practical deployment is
challenging due to their ever increasing computational and memory demands. Quantization …