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

Model merging is an efficient empowerment technique in the machine learning community
that does not require the collection of raw training data and does not require expensive …

Low-Rank Adaptation (LoRA) offers an efficient way to fine-tune large language models
(LLMs). Its modular and plug-and-play nature allows the integration of various domain …

Deep model training on extensive datasets is increasingly becoming cost-prohibitive,
prompting the widespread adoption of deep model fusion techniques to leverage knowledge …

The rapid advancement of foundation modelslarge-scale neural networks trained on
diverse, extensive datasetshas revolutionized artificial intelligence, enabling unprecedented …

The cultivation of expertise for large language models (LLMs) to solve tasks of specific areas
often requires special-purpose tuning with calibrated behaviors on the expected stable …

Large language models (LLMs) have achieved remarkable success in natural language
processing tasks but suffer from high computational costs during inference, limiting their …

Parameter-efficient fine-tuning techniques like Low-Rank Adaptation (LoRA) have
revolutionized the adaptation of large language models (LLMs) to diverse tasks. Recent …

Efficiency, specialization, and adaptability to new data distributions are qualities that are
hard to combine in current Large Language Models. The Mixture of Experts (MoE) …

While Foundation Models provide a general tool for rapid content creation, they regularly
require task-specific adaptation. Traditionally, this exercise involves careful curation of …