Low-rank Adaption of Large Language Models: Explaining the Key Concepts Behind LoRA

In this video, I go over how LoRA works and why it's crucial for affordable Transformer fine-tuning. LoRA learns low-rank matrix decompositions to slash the costs of training huge language models. It adapts only low-rank factors instead of entire weight matrices, achieving major memory and performance wins. 🔗 LoRA Paper: https://arxiv.org/pdf/2106.09685.pdf 🔗 Intrinsic Dimensionality Paper: https://arxiv.org/abs/2012.13255 About me: Follow me on LinkedIn: https://www.linkedin.com/in/csalexiuk/ Check out what I'm working on: https://getox.ai/