QLoRA paper explained (Efficient Finetuning of Quantized LLMs)
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- Опубліковано 22 чер 2024
- QLoRA is the first approach that allows the TRAINING of Large Language Models (LLMs) on a single GPU. It does this by using three contributions namely: 1. a novel NormalFloat data type, 2. Double Quantization, 3. Paged Optimizers.
In this video, we explain the idea behind all three approaches and explain how they are put together to arrive at QLoRA.
⌚️ ⌚️ ⌚️ TIMESTAMPS ⌚️ ⌚️ ⌚️
0:00 - QLoRA
1:34 - Quantization
3:10 - Problem with Quantization
3:50 - Blockwise Quantization
5:42 - Normal Float
6:37 - Double Quantization
7:25 - Paged Optimizers
8:45 - QLoRA Finetuning
9:30 - LoRA vs QLoRA
10:28 - Results
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Your videos on LoRA finally made the concepts click for me. It was clearly explained! Thank you for the content you make
Glad it helped. Welcome 😊
Thank you for the explanation! I find that it's very helpful!
That was really well explained with intuitative diagrams and explination. Thanks for the video , just subscribed .
thank you! :)
Thanks for connecting the dots!
glad you liked! :)
Very well explained
Thanks so much 😊
amazing explanation
Glad you think so!
Waiting to see it.
Sure! :)
Hey. I need your help. I have a curated set of notes and books and I wish to use it to finetune a model. How can it be done?
would you like to see a fine-tuning video on text data? Would that be useful? Do you have any suggestions on the dataset I can show fine-tuning on?
@@AIBites Yes, I have suggestion. Finetuning a model on my journal in which I have written about the truth of nonduality and illusionary nature of reality. I am also actively curating books on truth, and would love your help.
@@AIBites That would be very help. There arent many good videos on fine tune out there.
hope the fine-tuning video was of some help
hope the fine-tuning video was of some help