Low-Rank Adapters Meet Neural Architecture Search for LLM Compression
J. Pablo Muñoz, Jinjie Yuan, Nilesh Jain
2025-01-29
Summary
This paper talks about new ways to make large AI language models (LLMs) smaller and faster without losing their smarts. It combines two clever techniques: low-rank adapters and Neural Architecture Search (NAS), to shrink these big AI models so they can work on smaller computers.
What's the problem?
Large Language Models are super smart AI systems, but they're also huge and need a lot of computer power to run and improve. This makes it hard for people without access to powerful computers to use or work with these AIs. It's like having a really smart robot that's too big to fit in most homes.
What's the solution?
The researchers found a way to combine two methods to shrink these AI models. First, they use 'low-rank adapters', which are like efficient add-ons that help the AI learn new things without changing its whole structure. Then, they use 'Neural Architecture Search' to find the best way to arrange the AI's parts, kind of like rearranging furniture to make more space in a room. By putting these methods together, they can make the AI models much smaller and faster without making them less smart.
Why it matters?
This matters because it could make powerful AI accessible to more people and businesses. Smaller, faster AI models can run on regular computers or even phones, not just big data centers. This could lead to more people using AI for all sorts of things, from writing help to solving complex problems, without needing super expensive equipment. It's like shrinking a supercomputer so it can fit in your pocket, making advanced AI technology available to everyone, not just big tech companies.
Abstract
The rapid expansion of Large Language Models (LLMs) has posed significant challenges regarding the computational resources required for fine-tuning and deployment. Recent advancements in low-rank adapters have demonstrated their efficacy in parameter-efficient fine-tuning (PEFT) of these models. This retrospective paper comprehensively discusses innovative approaches that synergize low-rank representations with Neural Architecture Search (NAS) techniques, particularly weight-sharing super-networks. Robust solutions for compressing and fine-tuning large pre-trained models are developed by integrating these methodologies. Our analysis highlights the potential of these combined strategies to democratize the use of LLMs, making them more accessible for deployment in resource-constrained environments. The resulting models exhibit reduced memory footprints and faster inference times, paving the way for more practical and scalable applications of LLMs. Models and code are available at https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning.