What question did this study set out to answer?

This research aims to evaluate different parameter-efficient fine-tuning techniques for large language models.

July 28, 2026Open Access

Parameter-Efficient Fine-Tuning: LoRA, QLoRA, and DoRA

DSDheiver Francisco SantosCognizant (United States)

Key Points

This research aims to evaluate different parameter-efficient fine-tuning techniques for large language models.
Compared Low-Rank Adaptation (LoRA) with its quantized variant QLoRA and Weight-Decomposed Low-Rank Adaptation (DoRA).
Analyzed performance metrics and computational efficiency of each technique.
LoRA showed significant efficiency improvements over traditional fine-tuning methods.
QLoRA demonstrated reduced memory usage with minimal impact on performance.
DoRA provided a novel approach that optimally balances parameter efficiency and model performance.

Abstract

A technical note comparing parameter-efficient fine-tuning techniques for large language models, including Low-Rank Adaptation (LoRA), its quantized variant QLoRA, and Weight-Decomposed Low-Rank Adaptation (DoRA).

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