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

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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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-07-26
DOI
https://doi.org/10.5281/zenodo.21610033
Primary Topic
Speech Recognition and Synthesis
Type
article
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article

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

Dheiver Francisco Santos
Zenodo (CERN European Organization for Nuclear Research)
Speech Recognition and Synthesis
article

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

Dheiver Francisco Santos
article en

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).

Zenodo (CERN European Organization for Nuclear Research)
Oldham Council (GB)
Quality Education
Openalex Percentile: Top 7%
Speech Recognition and Synthesis
DOIOpen Access PDFOpenAlex
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