Large models represent a groundbreaking advancement in multiple application fields,
enabling remarkable achievements across various tasks. However, their unprecedented …

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

Although it is known that transformer language models (LMs) pass features from early layers
to later layers, it is not well understood how this information is represented and routed by the …

Diffusion models have been proven highly effective at generating high-quality images.
However, as these models grow larger, they require significantly more memory and suffer …

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

The prohibitive sizes of Large Language Models (LLMs) today make it difficult to deploy
them on memory-constrained edge devices. This work introduces $\rm CALDERA $--a new …

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

Existing methods for adapting large language models (LLMs) to new tasks are not suited to
multi-task adaptation because they modify all the model weights--causing destructive …

This study presents an innovative enhancement to retrieval-augmented generation (RAG)
systems by seamlessly integrating fine-tuned large language models (LLMs) with vector …

The deployment of large language models (LLMs) is often constrained by memory
bandwidth, where the primary bottleneck is the cost of transferring model parameters from …