On-device large language models (LLMs), referring to running LLMs on edge devices, have
raised considerable interest since they are more cost-effective, latency-efficient, and privacy …

The recent surge of Multimodal Large Language Models (MLLMs) has fundamentally
reshaped the landscape of AI research and industry, shedding light on a promising path …

The advent of large language models (LLMs) revolutionized natural language processing
applications, and running LLMs on edge devices has become increasingly attractive for …

The burgeoning interest in developing Large Language Models (LLMs) with up to trillion
parameters has been met with concerns regarding resource efficiency and practical …

Transformers have recently revolutionized the machine learning (ML) landscape, gradually
making their way into everyday tasks and equipping our computers with" sparks of …

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

Training Large Language Models (LLMs) is memory-intensive due to the large number of
parameters and associated optimization states. GaLore, a recent method, reduces memory …

The rapid advancement of large language models (LLMs) has revolutionized artificial
intelligence, introducing unprecedented capabilities in natural language processing and …

With the rise of embodied foundation models (EFMs), most notably small language models
(SLMs), adapting Transformers for the edge applications has become a very active field of …

When mobile meets LLMs, mobile app users deserve to have more intelligent usage
experiences. For this to happen, we argue that there is a strong need to apply LLMs for the …