@DanielTompkinsGuitar

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

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.

@Vinayakan-s4y

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

@gayathrisaranath666

Thanks for this clear explanation about the topic!
Your way of relating back to research papers is very interesting and helpful!

@mandrakexTV

This is the best detailed video and nicest explanation on youtube right now. I do think your channel will grow because you are doing an EXCELENT job. Thank you man.

@frameworkinvestment

What an awesome video! Thank you Mark.

@drstrangeluv1680

I loved the explanation! Please make more such videos! :popcorn-yellow-striped-smile:

@YLprime

Dude u look like the lich king with those blue eyes

@SanjaySingh-gj2kq

Good explanation of LoRA  and QLoRA

@manudevjain7738

Such amazing video this! So simple yet covers it all. Please keep making these videos :)

@andrepemmelaar8728

Very useful! Marvelous clear explanation with the right amount of detail about a subject that’s worth understanding

@steve_wk

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

@VerdonTrigance

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

@thiashomme

Incredible explanation, Thanks!

@v.smourya8005

Damn this is so well put and easy to understand! Thanks Mark!

- New Subscriber 😄

@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 !

@user-wr4yl7tx3w

This is really well presented

@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.

@thelitbit

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

@varun_skywalker

This is really helpful, Thank you!!