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Status: Unchecked ×Keyword: RoBERTa ×Clear all

1 claim from 1 paper

1. Computer Science › Topic Modeling

   LoRA Fine-Tuning of a 3B Code LLM for Algorithmic Efficiency

   Hu, Shen, Wallis et al. · arXiv (Cornell University) · 2021

   The paper proposes Low-Rank Adaptation (LoRA), which freezes pre-trained weights and trains small added matrices, sharply cutting trainable parameters and memory while keeping model quality.

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   1. UncheckedLoRA matches or beats full fine-tuning on RoBERTa, DeBERTa, GPT-2 and GPT-3 with fewer trainable parameters and no added inference delay.“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.”