Low-Rank Adapters Meet Neural Architecture Search for LLM Compression
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.
67999c3dc1e34886f90320ee
J. Pablo Muñoz
admin_assigned
2025-01-29T17:27:08.761Z
63dece97e742e86dc92169b9
/avatars/55e66d1b65e73b0ad1a16ee0457d6af6.svg
J. Pablo Munoz
user
jpablomch
67999c3dc1e34886f90320ef
Jinjie Yuan
admin_assigned
2025-01-29T17:27:14.404Z
6541d41ae2e170b6a8de2f78
/avatars/f7b524d17910b0e93548d08089d24f60.svg
Jinjie Yuan
user
jinjieyuan
67999c3dc1e34886f90320f0
Nilesh Jain
admin_assigned
2025-01-29T17:27:20.569Z
62d28f0c8d8206fd4d84ceca
https://cdn-avatars.huggingface.co/v1/production/uploads/62d28f0c8d8206fd4d84ceca/E9k4-RUCnoAgYinh6Ax2W.png
Nilesh Jain
user
dalle2
Low-Rank Adapters Meet Neural Architecture Search for LLM Compression
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.
Low-rank adapters and NAS techniques are combined to create efficient, parameter-reduced Large Language Models suitable for resource-limited environments.
Large Language Models (LLMs) low-rank adapters parameter-efficient fine-tuning (PEFT) Neural Architecture Search (NAS) weight-sharing super-networks memory footprints inference times