What is LoRA? Low-Rank Adaptation for finetuning LLMs EXPLAINED

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How does LoRA work? Low-Rank Adaptation for Parameter-Efficient LLM Finetuning explained. Works for any other neural network as well, not just for LLMs.

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Outline:
00:00 LoRA explained
00:59 Why finetuning LLMs is costly
01:44 How LoRA works
03:45 Low-rank adaptation
06:14 LoRA vs other approaches

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Music 🎵 : Meadows - Ramzoid

Video editing: Nils Trost

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Insightful : Especially the comparison from LORA to prefix tuning and adapters at the end!

MikeTon

Perfect. This exactly what I wanted to know. "Bite-sized" is right!

rockapedra

I’ve been using LoRAs for a while now but didn’t have a great understanding of how they work. Thank you for the explainer!

wholenutsanddonuts

By far the clearest explanation on youtube

michelcusteau

Very clear and straightforward. The explanation of matrix rank was especially helpful. Thank you for the video.

SoulessGinge

Thanks Letitia. Your explanation was very clear and helpful to understand the paper.

minkijung

So simple explanation, thank you soo much!!

keshavsingh

Thank you much for this video. I started reading the paper, was very terrified by it, then I thought I should watch some YouTube video, watch one video, was asleep half-way through the video. Woke up again and stumbled across your video, your coffee woke me up and now I got the LoRA. Thanks for your efforts.

thecodest

Thanks again for amazing video. I would also request a detailed video on Flash Attention. Thanks

karndeepsingh

Very comprehensive explanation! Thank you

Lanc

LoRA is awesome! It also helps with overfitting in protein language models as well. Cool video!

amelieschreiber

Thank you for clearing my concepts regarding LoRA

m.rr.c.

Firstly thanks for the amazing video. Can you also make a video about QLoRA.

igrpfkc

If we knew what abstractions were handled layer by layer, we could make sure that the individual layers were trained to completely learn those abstractions. Let's hope Max Tegmark's work on introspection get us there.

dr.mikeybee

Thanks for the simple and educating video!
If I'm not mistaken, prefix tuning is pretty much the same as embedding vectors in diffusion models! How cool is that? 😀

alirezafarzaneh

Fantastic video as always. QLora is even better if you are GPU poor like me.

bdennyw

You are just amazing >>> so beautiful so elegant just wow😇😇

deepak_kori

Concept of rank of a matrix, tauught in such an effective way

soulfuljourney

how is Lora fine-tuning track changes from creating two decomposition matrix? How the ΔW is determined?

ArunkumarMTamil

Great explanation, thanks for the video!
I have a lingering question about LoRA: Is it necessary to approximate the low-rank matrices of the difference weights (the Delta W in the video). Or can we reduce the size of the original weight matrices? If I understood the video correctly, at the end of LoRA training, I have the full parameters of the roginal model + the difference weights (in reduced size). My question is why can't I learn low rank matrices for the original weights as well?

yacinegaci

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