SQFT: Low-cost Model Adaptation in Low-precision Sparse Foundation Models

• URL: http://arxiv.org/abs/2410.03750v1
• Date: Tue, 1 Oct 2024 19:49:35 GMT
• Title: SQFT: Low-cost Model Adaptation in Low-precision Sparse Foundation Models
• Authors: Juan Pablo Muñoz, Jinjie Yuan, Nilesh Jain,
• Abstract summary: SQFT is an end-to-end solution for low-precision sparse parameter-efficient fine-tuning of large pre-trained models. SQFT allows for effective model manipulation in resource-constrained environments. SQFT also addresses the challenge of having quantized weights and adapters with different numerical precisions.
• Score: 2.867517731896504
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Large pre-trained models (LPMs), such as large language models, have become ubiquitous and are employed in many applications. These models are often adapted to a desired domain or downstream task through a fine-tuning stage. This paper proposes SQFT, an end-to-end solution for low-precision sparse parameter-efficient fine-tuning of LPMs, allowing for effective model manipulation in resource-constrained environments. Additionally, an innovative strategy enables the merging of sparse weights with low-rank adapters without losing sparsity and accuracy, overcoming the limitations of previous approaches. SQFT also addresses the challenge of having quantized weights and adapters with different numerical precisions, enabling merging in the desired numerical format without sacrificing accuracy. Multiple adaptation scenarios, models, and comprehensive sparsity levels demonstrate the effectiveness of SQFT. Models and code are available at https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning.

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