LoRA & QLoRA Fine-tuning Explained In-Depth

Рет қаралды 30,185

7 ай бұрын

👉 Start fine-tuning at www.entrypointai.com
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

Пікірлер: 56

@DanielTompkinsGuitar 5 ай бұрын

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

@naevan1 2 ай бұрын

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 !

@user-os2rb3lx7h 6 ай бұрын

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

@drstrangeluv1680 3 ай бұрын

I loved the explanation! Please make more such videos!

@VerdonTrigance 5 ай бұрын

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

@thelitbit 25 күн бұрын

great video! referring to the paper and explaining each thing in detail really helps understand the concept to the fullest. Kudos!

@anujlahoty8022 2 ай бұрын

Loved the contnt! Simply explained no BS.

@user-wr4yl7tx3w 4 ай бұрын

This is really well presented

@varun_skywalker 6 ай бұрын

This is really helpful, Thank you!!

@SanjaySingh-gj2kq 7 ай бұрын

Good explanation of LoRA and QLoRA

@aashwinsharma8194 3 күн бұрын

Great explanation...

@SantoshGupta-jn1wn 5 ай бұрын

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.

@Sonic2kDBS Ай бұрын

Some nice details here. Keep on.

@steve_wk 6 ай бұрын

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

@louisrose7823 3 ай бұрын

Great video!

@nachiketkathoke8281 Ай бұрын

really grate explanation

@stutters3772 2 ай бұрын

This video deserves more likes

@markironmonger223 6 ай бұрын

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 6 ай бұрын

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

@YLprime 3 ай бұрын

Dude u look like the lich king with those blue eyes

@practicemail3227 2 ай бұрын

True. 😅 He should be in acting career ig.

@EntryPointAI 2 ай бұрын

You mean Lich King looks like me I think 🤪

@chrisanderson1513 22 күн бұрын

Saving me somr embarrassment in future work meetings. :) thanks for sharing.

@SergieArizandieta 3 ай бұрын

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

@titusfx 5 ай бұрын

🎯 Key Takeaways for quick navigation: <a href="#" class="seekto" data-time="0">00:00</a> 🤖 *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. <a href="#" class="seekto" data-time="149">02:29</a> 🛡️ *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. <a href="#" class="seekto" data-time="259">04:19</a> 💼 *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. <a href="#" class="seekto" data-time="371">06:11</a> 🎯 *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. <a href="#" class="seekto" data-time="492">08:12</a> 🔍 *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. <a href="#" class="seekto" data-time="646">10:46</a> 📈 *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. <a href="#" class="seekto" data-time="810">13:30</a> 🧩 *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.

@RafaelPierre-vo2rq 3 ай бұрын

Awesome explanation! Which camera you use?

@EntryPointAI 3 ай бұрын

Thanks, it’s a Canon 6d Mk II

@nafassaadat8326 Ай бұрын

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

@TheBojda 3 ай бұрын

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 3 ай бұрын

I think you mean distillation with quantisation?

@EntryPointAI 3 ай бұрын

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 3 ай бұрын

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

@rishiktiwari 3 ай бұрын

@@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.

@Ian-fo9vh 6 ай бұрын

Bright eyes

@kunalnikam9112 2 ай бұрын

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 2 ай бұрын

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 2 ай бұрын

@@EntryPointAI got it!! Thank you

@ArunkumarMTamil 2 ай бұрын

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

@EntryPointAI 2 ай бұрын

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 2 ай бұрын

@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 2 ай бұрын

@@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.

@egonkirchof 29 күн бұрын

Why do we call training a model pre-training it ?

@EntryPointAI 29 күн бұрын

Not sure if that's a rhetorical question, but I'll give it a go. You can call it just "training," but that might imply that it's ready to do something useful when you're done. If you call it "pre-training" it implies that you'll train it more afterward, which is generally true. So it may be useful in being a little more specific.

@vediodiary1754 3 ай бұрын

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

@ecotts 3 ай бұрын

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 3 ай бұрын

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

@nabereon 4 ай бұрын

Are you trying to hypnotize us with those eyes 😜

@619vijay Күн бұрын

Eyes!

@DrJaneLuciferian 5 ай бұрын

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

@EntryPointAI 5 ай бұрын

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

@DrJaneLuciferian 5 ай бұрын

@@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 :^)