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Verified claim · AI-ML · 100% confidence

QLoRA introduced in paper: QLoRA: Efficient Finetuning of Quantized LLMs (Dettmers et al., 2023).

Last verified 2026-05-16 · Methodology veritas-v0.1 · 767cbe41c961be1a

Structured fields

Subject
QLoRA
Predicate
introduced_in_paper
Object
QLoRA: Efficient Finetuning of Quantized LLMs (Dettmers et al., 2023)
Confidence
100%
Tags
qlora · quantization · peft · fine-tuning · foundational · 2023

Sources (2)

  1. [1] preprint · arXiv (Dettmers, Pagnoni, Holtzman, Zettlemoyer) · 2023-05-23

    QLoRA: Efficient Finetuning of Quantized LLMs
    We present QLoRA, an efficient finetuning approach that reduces memory usage enough to finetune a 65B parameter model on a single 48GB GPU while preserving full 16-bit finetuning task performance.
  2. [2] github release · Artidoro Pagnoni / University of Washington · 2023-05-23

    artidoro/qlora — official implementation

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QLoRA introduced in paper: QLoRA: Efficient Finetuning of Quantized LLMs (Dettmers et al., 2023). — SourceScore Claim 767cbe41c961be1a (verified 2026-05-16). https://sourcescore.org/api/v1/claims/767cbe41c961be1a.json

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