Fine-tuning larger models can be tricky on consumer hardware. In this video I go over why its better to use large models for fine-tuning vs smaller models, I go over the issue with the naive approach to fine-tuning, and finally, I go over how to use DeepSpeed to successfully fine-tune even the largest GPT Neo model.

Notebook Git repo: https://github.com/mallorbc/GP....T_Neo_fine-tuning_no
finetuning repo: https://github.com/Xirider/finetune-gpt2xl
DeepSpeed repo: https://github.com/microsoft/DeepSpeed
happy transformers: https://happytransformer.com/
GPT article with images: https://towardsdatascience.com..../gpt-3-the-new-might

Timestamps

00:00 - Intro
00:36 - Background on fine-tuning
02:41 - Setting up Jupyter
05:17 - Incorrect naive fine-tuning method
10:02 - Correctly fine-tuning with DeepSpeed
15:35 - Fine-tuning the 2.7B model
16:35 - Fine-tuning the 1.3B model
17:55 - Looking at the README
20:08 - Outro and future work

There are so many videos circulating and forums saying that you actually can't see the difference in 4K and Full HD...its time to blow the whistle on it and technically break down how insanely different it really is.

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Basic mathematics from a language model? Rob Miles on GPT3, where it seems like size does matter!

More from Rob Miles: http://bit.ly/Rob_Miles_YouTube

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This video was filmed and edited by Sean Riley.

Computer Science at the University of Nottingham: https://bit.ly/nottscomputer

Computerphile is a sister project to Brady Haran's Numberphile. More at http://www.bradyharan.com