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LoRA: The Original Paper on LoRA Fine-Tuning
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This is a discussion of the original LoRA paper, which proposed a novel approach called Low-Rank Adaptation (LoRA) to make large language models (LLMs) more efficient for downstream tasks. LoRA avoids the computational and storage burden of traditional fine-tuning by freezing the pre-trained model weights and instead injects trainable low-rank matrices into each layer of the Transformer architecture. This technique results in a significant reduction in trainable parameters and memory requirements without compromising model quality. The authors provide a comprehensive evaluation of LoRA across various NLP tasks, showcasing its effectiveness on models like RoBERTa, DeBERTa, GPT-2, and GPT-3. They also investigate the underlying principles of low-rank updates, offering insights into the effectiveness of LoRA.
Read the paper: https://arxiv.org/abs/2106.09685
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