PublishedCurated

QLoRA: Efficient Finetuning of Quantized LLMs

Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, Luke Zettlemoyer

Advances in Neural Information Processing Systems 36 (NeurIPS 2023)23 May 2023

Abstract

Backpropagates through a frozen 4-bit quantized base model into a small set of low-rank adapters. The approach brings fine-tuning of large models down to a single consumer GPU while holding quality close to full 16-bit tuning.

arXiv Code

BibTeX

@inproceedings{dettmers2023qlora,
  author    = {Tim Dettmers and Artidoro Pagnoni and Ari Holtzman and Luke Zettlemoyer},
  title     = {QLoRA: Efficient Finetuning of Quantized LLMs},
  booktitle = {Advances in Neural Information Processing Systems 36 (NeurIPS 2023)},
  year      = {2023},
  eprint    = {2305.14314},
  archivePrefix = {arXiv},
  url       = {https://arxiv.org/abs/2305.14314}
}

Ejentic did not author this paper. Credit belongs to the authors named above; the note is ours.

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