Large Language Models (LLMs) have recently demonstrated remarkable capabilities in
natural language processing tasks and beyond. This success of LLMs has led to a large …

The high computational and memory requirements of large language model (LLM) inference
make it feasible only with multiple high-end accelerators. Motivated by the emerging …

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 …

The recent advancements in self-supervised learning, combined with the Transformer
architecture, have enabled natural language processing (NLP) to achieve remarkably low …

We propose a simple approach for memory-efficient adaptation of pretrained language
models. Our approach uses an iterative algorithm to decompose each pretrained matrix into …

How can we compress language models without sacrificing accuracy? The number of
compression algorithms for language models is rapidly growing to benefit from remarkable …

Large Language Models (LLMs) have recently gained popularity due to their impressive few-
shot performance across various downstream tasks. However, fine-tuning all parameters …

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

Serving foundation model inference is a pivotal component of contemporary AI applications,
where this service is usually hosted in a centralized data center on a group of homogeneous …

Large Language Models (LLMs) are proficient in natural language processing tasks, but
their deployment is often restricted by extensive parameter sizes and computational …