QLoRA: Efficient Finetuning of Large Language Models on a Single GPU? LoRA & QLoRA paper review

In this video, we'll look at QLoRA, an efficient finetuning approach that significantly reduces the GPU memory usage of large language models. With QLoRA, you can now finetune a 65B parameter model on just a single 48GB GPU, while maintaining full 16-bit finetuning task performance. We'll dive into the technical details of QLoRA, which involves backpropagating gradients through a frozen, 4-bit quantized pretrained language model into Low Rank Adapters (LoRA).

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Prompt Engineering GitHub Repository: https://github.com/curiousily/Get-Thi...

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QLoRA paper: https://arxiv.org/abs/2305.14314
QLoRA GitHub: https://github.com/artidoro/qlora
LoRA paper: https://arxiv.org/abs/2106.09685
HuggingFace blog post: https://huggingface.co/blog/4bit-tran...
Guanaco Playground: https://huggingface.co/spaces/uwnlp/g...

00:00 - Introduction
00:14 - LoRA
02:20 - QLoRA
10:58 - Guanaco Google Colab Notebook
11:56 - Conclusion

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