LoRA & QLoRA Fine-tuning Explained In-Depth

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In this video, I dive into how LoRA works vs full-parameter fine-tuning, explain why QLoRA is a step up, and provide an in-depth look at the LoRA-specific hyperparameters: Rank, Alpha, and Dropout.
0:26 - Why We Need Parameter-efficient Fine-tuning
1:32 - Full-parameter Fine-tuning
2:19 - LoRA Explanation
6:29 - What should Rank be?
8:04 - QLoRA and Rank Continued
11:17 - Alpha Hyperparameter
13:20 - Dropout Hyperparameter
Ready to put it into practice? Try LoRA fine-tuning at www.entrypointai.com

[@DanielTompkinsGuitar]

Thanks! This is among the clearest and most concise explanations of LoRA and QLoRA. Really great job.

[@steve_wk]

I've watched a couple other of your videos - you're a very good teacher - thanks for doing this.

[@user-os2rb3lx7h]

I have been using thiese techniques for a while now without having a good understanding of each of the prameters. Thanks for giving a good overview of both the techniques and the papers

[@naevan1]

I love this video man. watched it at least 3 times and came back to it before a job interview also. Please do more tutorials /explanations !

[@VerdonTrigance]

It was incredible and very helpful video. Thank you man!

[@drstrangeluv1680]

I loved the explanation! Please make more such videos!

[@Sonic2kDBS]

Some nice details here. Keep on.

[@SantoshGupta-jn1wn]

great video, i think the best explanation i've seen on this, i'm also really confused about why they picked the rank and alpha that they did.

[@anujlahoty8022]

Loved the contnt! Simply explained no BS.

[@varun_skywalker]

This is really helpful, Thank you!!

[@SanjaySingh-gj2kq]

Good explanation of LoRA and QLoRA

[@user-wr4yl7tx3w]

This is really well presented

[@titusfx]

🎯 Key Takeaways for quick navigation: 00:00 🤖 *Introduction to Low Rank Adaptation (LoRA) and QLoRA* - LoRA is a parameter-efficient fine-tuning method for large language models. - Explains the need for efficient fine-tuning in the training process of large language models. 02:29 🛡️ *Challenges of Full Parameter Fine-Tuning* - Full parameter fine-tuning updates all model weights, requiring massive memory. - Limits fine-tuning to very large GPUs or GPU clusters due to memory constraints. 04:19 💼 *How LoRA Solves the Memory Problem* - LoRA tracks changes to model weights instead of directly updating all parameters. - It uses rank-one matrices to efficiently calculate weight changes. 06:11 🎯 *Choosing the Right Rank for LoRA* - Rank determines the precision of the final output table in LoRA fine-tuning. - For most tasks, rank can be set lower without sacrificing performance. 08:12 🔍 *Introduction to Quantized LoRA (QLoRA)* - QLoRA is a quantized version of LoRA that reduces model size without losing precision. - It exploits the normal distribution of parameters to achieve compression and recovery. 10:46 📈 *Hyperparameters in LoRA and QLoRA* - Discusses hyperparameters like rank, alpha, and dropout in LoRA and QLoRA. - The importance of training all layers and the relationship between alpha and rank. 13:30 🧩 *Fine-Tuning with LoRA and QLoRA in Practice* - Emphasizes the need to experiment with hyperparameters based on your specific data. - Highlights the ease of using LoRA with integrations like Replicate and Gradient.

[@louisrose7823]

Great video!

[@SergieArizandieta]

wow I'm noobie in this field n I been testing fine-tunen my own chatbot with differents techniques, n I found a lot of stuff, but It's not commonly find a some explanation to understand the main reason of the use of it, ty a lot < 3

[@stutters3772]

This video deserves more likes

[@nafassaadat8326]

can we use QLoRA in a simple ML model like CNN for image classification ?

[@markironmonger223]

This was wonderfully educational and very easy to follow. That either it makes you a great educator or me an idiot :P Regardless, thank you.

[@EntryPointAI]

let's both say it's the former and call it good! 🤣

[@YLprime]

Dude u look like the lich king with those blue eyes

[@practicemail3227]

True. 😅 He should be in acting career ig.

[@EntryPointAI]

You mean Lich King looks like me I think 🤪

[@RafaelPierre-vo2rq]

Awesome explanation! Which camera you use?

[@EntryPointAI]

Thanks, it’s a Canon 6d Mk II

[@kunalnikam9112]

In LoRA, Wupdated = Wo + BA, where B and A are decomposed matrices with low ranks, so i wanted to ask you that what does the parameters of B and A represent like are they both the parameters of pre trained model, or both are the parameters of target dataset, or else one (B) represents pre-trained model parameters and the other (A) represents target dataset parameters, please answer as soon as possible

[@EntryPointAI]

Wo would be the original model parameters. A and B multiplied together represent the changes to the original parameters learned from your fine-tuning. So together they represent the difference between your final fine-tuned model parameters and the original model parameters. Individually A and B don't represent anything, they are just intermediate stores of data that save memory.

[@kunalnikam9112]

@@EntryPointAI got it!! Thank you

[@TheBojda]

Nice video, congrats! LoRA is about fine-tuning, but is it possible to use it to compress the original matrices to speed up inference? I mean decompose the original model's original weight matrices to products of low-rank matrices to reduce the number of weights.

[@rishiktiwari]

I think you mean distillation with quantisation?

[@EntryPointAI]

Seems worth looking into, but I couldn't give you a definitive answer on what the pros/cons would be. Intuitively I would expect it could reduce the memory footprint but that it wouldn't be any faster.

[@TheBojda]

@@rishiktiwari Ty. I learned something new. :) If I understand well, this is a form of distillation.

[@rishiktiwari]

​@@TheBojdaCheers mate! Yes, in distillation there is student-teacher configuration and the student tries to be like teacher with less parameters (aka. weights). This can also be combined with quantisation to reduce memory footprint.

[@ArunkumarMTamil]

how is Lora fine-tuning track changes from creating two decomposition matrix?

[@EntryPointAI]

The matrices are multiplied together and the result is the changes to the LLM's weights. It should be explained clearly in the video, it may help to rewatch.

[@ArunkumarMTamil]

@EntryPointAI My understanding: Orignal weight = 10 * 10 to form a two decomposed matrices A and B let's take the rank as 1 so, The A is 10 * 1 and B is 1 * 10 total trainable parameters is A + B = 20 In Lora even without any dataset training if we simply add the A and B matrices with original matric we can improve the accuracy slighty And if we use custom dataset in Lora the custom dataset matrices will captured by A and B matrices Am I right @EntryPointAI?

[@EntryPointAI]

@@ArunkumarMTamil Trainable parameters math looks right. But these decomposed matrices will be initialized as all zeroes so adding them without any custom training dataset will have no effect.

[@ecotts]

LoRa (Long Range) is a physical proprietary radio communication technique that uses a spread spectrum modulation technique derived from chirp spread spectrum. It's a low powered wireless platform that has become the de facto wireless platform of Internet of Things (IoT). Get your own acronym! 😂

[@EntryPointAI]

Fair - didn’t create it, just explaining it 😂

[@Ian-fo9vh]

Bright eyes

[@vediodiary1754]

Oh my god your eyes 😍😍😍😍everybody deserves hot teacher😂❤

[@nabereon]

Are you trying to hypnotize us with those eyes 😜

[@DrJaneLuciferian]

I wish people would actually share links to papers they reference...

[@EntryPointAI]

LoRA: arxiv.org/abs/2106.09685 QLoRA: arxiv.org/abs/2305.14314 Click "Download PDF" in top right to view the actual papers.

[@DrJaneLuciferian]

@@EntryPointAI Thank you, that's kind. I did already go look it up. Sorry I was frustrated. It's very common for people to forget to putlikes to papers in show note :^)

[@TR-707]

Ahh very interesting thank you! *goes to fine tune pictures of anime girls*

[@coco-ge4xg]

omg I always distracted by his blue eyes😆and ignoring what his talking