LoRA Tutorial : Low-Rank Adaptation of Large Language Models

Published: 24 October 2023
on channel: ChrisTech

In this video, I will explain the main idea and benefits of LoRA. LoRA reduces the number of trainable parameters by injecting low-rank matrices into each layer of the Transformer architecture, while freezing the original weights. This way, LoRA can achieve comparable or superior performance to full fine-tuning, while saving storage space, memory usage, and inference latency. LoRA also outperforms other adaptation methods such as adapters and prefix-tuning. If you are interested in learning more about LoRA, you can check out the paper [here](^1^) or the GitHub repository [here](^2^). You can also find more information and tutorials on the Hugging Face website [here](^3^). I hope you enjoy this video and find it useful. Please like, share, and subscribe for more content on natural language processing and machine learning. Thank you for watching!

(1) [2106.09685] LoRA: Low-Rank Adaptation of Large Language Models - arXiv.org. https://arxiv.org/abs/2106.09685.
(2) LoRA: Low-Rank Adaptation of Large Language Models. https://github.com/microsoft/LoRA.
(3) Low-Rank Adaptation of Large Language Models (LoRA) - Hugging Face. https://huggingface.co/docs/diffusers/main....
(4) Low-rank Adaptation of Large Language Model Rescoring for Parameter .... https://arxiv.org/abs/2309.15223.
(5) undefined. https://doi.org/10.48550/arXiv.2106.09685.

Watch video LoRA Tutorial : Low-Rank Adaptation of Large Language Models online, duration hours minute second in high quality that is uploaded to the channel ChrisTech 24 October 2023. Share the link to the video on social media so that your subscribers and friends will also watch this video. This video clip has been viewed 90 times and liked it 2 visitors.

489