# LoRA: Low-Rank Adaptation of Large Language Models

> Low-Rank Adaptation, or LoRA, is proposed, which freezes the pre-trained model weights and injects trainable rank decomposition matrices into each layer of the Transformer architecture, greatly reducing the number of trainable parameters for downstream tasks.

## Metadata
- Authors: J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Weizhu Chen
- Journal: ArXiv
- Published: 2021
- Citations: 19,201
- Source: Semantic Scholar

## Technology Hub
- Hub: Large Language Models
- Discipline: Computer Science / AI
- Hub URL: https://science-database.com/technology/large-language-models
- Hub llms.txt: https://science-database.com/technology/large-language-models/llms.txt

## Abstract
An important paradigm of natural language processing consists of large-scale pre-training on general domain data and adaptation to particular tasks or domains. As we pre-train larger models, full fine-tuning, which retrains all model parameters, becomes less feasible. Using GPT-3 175B as an example -- deploying independent instances of fine-tuned models, each with 175B parameters, is prohibitively expensive. We propose Low-Rank Adaptation, or LoRA, which freezes the pre-trained model weights and injects trainable rank decomposition matrices into each layer of the Transformer architecture, greatly reducing the number of trainable parameters for downstream tasks. Compared to GPT-3 175B fine-tuned with Adam, LoRA can reduce the number of trainable parameters by 10,000 times and the GPU memory requirement by 3 times. LoRA performs on-par or better than fine-tuning in model quality on RoBERTa, DeBERTa, GPT-2, and GPT-3, despite having fewer trainable parameters, a higher training throughput, and, unlike adapters, no additional inference latency. We also provide an empirical investigation into rank-deficiency in language model adaptation, which sheds light on the efficacy of LoRA. We release a package that facilitates the integration of LoRA with PyTorch models and provide our implementations and model checkpoints for RoBERTa, DeBERTa, and GPT-2 at https://github.com/microsoft/LoRA.

## Links
- Semantic Scholar: https://www.semanticscholar.org/paper/a8ca46b171467ceb2d7652fbfb67fe701ad86092
- JSON API: https://science-database.com/api/v1/technology/large-language-models

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Paper ID: s2-a8ca46b171467ceb2d7652fbfb67fe701ad86092 | Hub: large-language-models