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LoRA and PEFT/Evidence
Method evidence record

LoRA and PEFT

LoRA (Low-Rank Adaptation), introduced by Hu et al. in 2022, and the broader family of parameter-efficient fine-tuning (PEFT) methods adapt large pretrained language models to new tasks by training only a small number of extra parameters instead of every weight in the model. This makes fine-tuning possible with far less GPU memory and compute while leaving the original model largely untouched.

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Source record

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Low-Rank Adaptation and Parameter-Efficient Fine-Tuning
Taxonomic method record · ml-model / deep-learning
  • Hu, E. J. et al. (2022). LoRA: Low-Rank Adaptation of Large Language Models. ICLR. · URL
  • Lester, B. et al. (2021). The Power of Scale for Parameter-Efficient Prompt Tuning. EMNLP. · DOI 10.18653/v1/2021.emnlp-main.243
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Related methods

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Same method familyGenerative Adversarial Networkmachine-suggested · Relational suggestion, not evidence.Same method familyRandom Forestmachine-suggested · Relational suggestion, not evidence.Same method familyVariational Autoencodermachine-suggested · Relational suggestion, not evidence.Same method familyVision Transformermachine-suggested · Relational suggestion, not evidence.Same method familyXGBoostmachine-suggested · Relational suggestion, not evidence.

Evidence status

Sources recorded, not reviewed

Bibliographic sources are present. Claim-level evidence review has not been performed.

Sources

2 recorded citations, copied from the method source record.

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