This is one of the easiest to follow explanations of LoRA that I’ve seen. Thanks a lot.

@DeepFindr

4 ай бұрын

Glad you found it useful!

Another great video. I appreciate that you don't skip on giving context and lay a good foundation. Makes understanding a lot easier. Thanks!

I've been scouring for a video like this. You're the best explanation so far!

Nice job with summarizing transfer learning and LoRA!

Thanks a lot Amazing explanation, very clear and straightforward

What an excellent video!! Congrats!!

Amazing video, feel like I finally understood every aspect of LoRA, thank you!

@DeepFindr

2 ай бұрын

Glad it was helpful :)

Awesome! Waiting for a video on implementing LoRA from scratch in pytorch.

great video to explain lora！ thanks

Amazing explanation! Thanks a lot!

Another great video, keep it up!

Explained quite well !

Great video! Liked and subscribed

Really Helpful!

good job!

Amazing!

Yes, indeed was hrlpful! Do you have a video on quantization?

Good explanation

Good summary! Next time it would be great if you add headings to the tables that you show on the video. Sometimes it is hard to follow. For example, what is computational efficiency? is it inference time or inference time increase over the increase in performance (e.g. accuracy, recall, etc.)? Thanks.

I'm interested in fine-tuning a Large Language Model to specialize in specific knowledge, for example about fish species, such as which fish can be found in certain seas or which are prohibited from fishing. Could you guide me on how to prepare a dataset for this purpose? Should I structure it as simple input-output pairs (e.g., 'What fish are in the Mediterranean Sea?' -> 'XX fish can be found in the Mediterranean Sea'), or is it better to create a more complex dataset with multiple columns containing various details about each fish species? Any advice on dataset preparation for fine-tuning an LLM in this context would be greatly appreciated. Thanks in advance!"

Brilliant

Thank you very much for this amazing vide. However, although this was probably only for demo purposes of a forward pass after LoRA finetuning; the modified forward pass method you`ve shown might be mislieading; since the forward pass of the function is assumed to be entirely linear. So, does the addition of the LoRA finetuned weights to the base model weights happen directly within model weights file (like .safetensors) or can it be done on a higher level on pytorch or tensorflow?

XAI techniques on LLMs is really interesting topic! When you would consider it?

Thanks

Good video

how is Lora fine-tuning track changes from creating two decomposition matrix? How the ΔW is determined?

What softwares do you use to make videos?

Thanks!

@DeepFindr

4 ай бұрын

Thank you!

please make video on QLoRA

How do you make the illustrations ?

As clear as mud

@truck.-kun.

4 ай бұрын

Sounds like AI

@anudeepk7390

3 ай бұрын

Is it a compliment or no? Cause mud is not clear.

@iloos7457

2 ай бұрын

​@@anudeepk7390😂😂😂😂😂😂

Ideot read paper. Lol

gold