LoRA Tutorial : Low-Rank Adaptation of Large Language Models
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.
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