LoRA (Low-rank Adaption of AI Large Language Models) for fine-tuning LLM models

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What is LoRA? How does LoRA work?
Low-Rank Adaptation (LoRA) for Parameter-Efficient LLM Finetuning explained right from Rank Decomposition to how LoRA is suitable for transformers. LoRA is fast becoming (already is?) the go to approach to fine-tuning transformers based models in budget!

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Paper Title: LoRA: Low-Rank Adaptation of Large Language Models

⌚️ ⌚️ ⌚️ TIMESTAMPS ⌚️ ⌚️ ⌚️
0:00 - Intro
0:58 - Adapters
2:13 - What is LoRA
3:17 - Rank Decomposition
4:28 - Motivation Paper
5:02 - LoRA Training
6:53 - LoRA Inference
8:24 - LoRA in Transformers
9:20 - Choosing the rank
9:50 - Implementations

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Underrated channel, keep making videos and itll eventually blow up

gelly

Thanks for the video!
I loved that you added some libraries we can use for this.

talmaimon

this is better explained than what the inventor of Lora itself explained in his video.

dileepvijayakumar

Super in depth and specific, thank you!!!

benkim

Good job on the clear explanation of the method and simplification. At 3:40, when you showed the matrix decomposition, the result on the left side does not match the result on the right side. Is this a mistake in the video editing, or is there a point to this? [1 2 3] x [2 20 30[ should be [[2. 4 6], [20 40 60], [30 60 90]]

unclecode

Very Well Explained! If ΔW's dimensions is 10 x 10, A and B dimensions are 10x2 and 2x10 respectively. So, instead of training 100 params we only train 40 params (10x2 + 2x10). Am I correct ?

pshivaramakrishna

I wish I was good at math to understand this stuff.

ccidral

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