LoRA: Low-Rank Adaptation of LLMs Explained

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best tutorial on lora if you are interested in in-depth knowledge. the way you present the paper is simple yet effective

blandatz

This video saves me a lot of time. Great work my friend. Appreciate it

JM-tumg

Thanks for the explanation. I was looking for an in-depth explanation and couldn't find anywhere that explains LoRA like you have here.

hiranhasanka

The diagram sketched out are so helpful.

wryltxw

Thanks for your helpful explanation <3

NockyLucky

Thanks Gabriel! it will be nice is you do a life coding session of your work it will be very good for others

fredrelec

Thank you do much for the video! It helped a ton! Do you have any plan on more related videos? such as Adam or stuff

thanosqin

Thanks for the great explaination! One question regarding the matrix 'B', when we initialize the weights to zero won't that cause the gradients of matrix B to be zero always and hence preventing it from learning?

pavanbuduguppa

Hi a question, can we use lora to just reduce the size of a model and run inference, or we have to train it always?

davidromero

Thanks for the explanation. What's the name of the notetaking app you are using here?

bryanw

Can you clarify that all the benefits of LORA are during the finetuning time, and no benefits accrue during inference time.

agdsam

ABx is different than BAx, isn't it? When you're writing ABx you are multiplying (r x d) with (d x r) to get (r x r) but you should instead get (d x d) with the reverse process. Actually, the confusion probably comes from you denoting A as (d x r) while in the paper it is (r x k) for k=d in your specific context.

RadiCho

Hello, Anyone can help me in answering my question: in section 7.3 “ HOW DOES THE ADAPTATION MATRIX ∆W COMPARE TO W ?“ : “ …..∆W only amplifies directions that are not emphasized in W. Third, the amplification factor is rather huge…….“. By one example: if we use a 1000-entry dataset to do LoRA fine-tune, we get a new weight W1. Based on this W1, if we do this Lora fine-tune again with the same dataset, will it be re-amplified (re-emphasized) once again ? or remain the same ? Thanks

jamesyang

It doesn't train the entire Lora model, so I came up with an idea to divide the model and train each part on Lora in each epoch. Wouldn't this approach, which requires less RAM like Lora and involves full fine-tuning, yield the same results?

talharuzgarakkus

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