LORA WITHOUT REGRET

Summary from the TLDR Dev newsletter

The Thinking Machines Lab published research on Low-Rank Adaptation (LoRA), a more efficient fine-tuning method for LLMs. Their findings show that LoRA can achieve similar performance to full fine-tuning when applied to all layers, especially MLP layers, and when not constrained by capacity limitations relative to dataset size. The study also found that optimal learning rates for LoRA are consistently 10x higher than those for full fine-tuning, and offers insights into setting LoRA hyperparameters.