LoRA & QLoRA Fine-tuning Explained In-Depth
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- Опубліковано 13 гру 2023
- 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 - Наука та технологія
Thanks! This is among the clearest and most concise explanations of LoRA and QLoRA. Really great job.
I've watched a couple other of your videos - you're a very good teacher - thanks for doing this.
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
It was incredible and very helpful video. Thank you man!
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 !
I loved the explanation! Please make more such videos!
Good explanation of LoRA and QLoRA
This is really well presented
This is really helpful, Thank you!!
Loved the contnt! Simply explained no BS.
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.
🎯 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.
Great video!
This video deserves more likes
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.
let's both say it's the former and call it good! 🤣
Awesome explanation! Which camera you use?
Thanks, it’s a Canon 6d Mk II
can we use QLoRA in a simple ML model like CNN for image classification ?
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
Dude u look like the lich king with those blue eyes
True. 😅 He should be in acting career ig.
You mean Lich King looks like me I think 🤪
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.
I think you mean distillation with quantisation?
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.
@@rishiktiwari Ty. I learned something new. :) If I understand well, this is a form of distillation.
@@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.
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
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.
@@EntryPointAI got it!! Thank you
how is Lora fine-tuning track changes from creating two decomposition matrix?
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.
@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?
@@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.
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! 😂
Fair - didn’t create it, just explaining it 😂
Oh my god your eyes 😍😍😍😍everybody deserves hot teacher😂❤
Bright eyes
Are you trying to hypnotize us with those eyes 😜
I wish people would actually share links to papers they reference...
LoRA: arxiv.org/abs/2106.09685
QLoRA: arxiv.org/abs/2305.14314
Click "Download PDF" in top right to view the actual papers.
@@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 :^)
Ahh very interesting thank you!
*goes to fine tune pictures of anime girls*
omg I always distracted by his blue eyes😆and ignoring what his talking