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Reproducing GPT-2 in llm.c

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Re: Reproducing GPT-2 in llm.c

#3
I just hope than in a couple of years we'll see a submission here titled "Reproduce GPT-4 on legacy RTX 4090."

Because currently even with open source (?) models we are still consumers, and the training is still the domain of the rich.

Re: Reproducing GPT-2 in llm.c

#7
post #3

I just hope than in a couple of years we'll see a submission here titled "Reproduce GPT-4 on legacy RTX 4090." Because currently even with open source (?) models we are still consumers, and the training is still the domain of the rich.

Accessing the dataset to train from scratch will be the biggest hurdle, now a lot of the pile has had ladder pulled since GPT-4

Re: Reproducing GPT-2 in llm.c

#9
post #4

Hi HN the main (more detailed) article is here https://github.com/karpathy/llm.c/discussions/481 Happy to answer questions!

Hi Andrej!

First, thank you for your teaching, it has helped me a lot, didn't think I'd ever have the chance to say thank you, but here you are and I hope this gets to you!

Question - what's a relevant (05-2024) baseline to compare the performance of c code to? Back when you made nanoGPT you were seeing "the file train.py reproduces GPT-2 (124M) on OpenWebText, running on a single 8XA100 40GB node in about 4 days of training". So twice the memory on the c node, but unsure of data size /epochs, any other details I may be missing. I.e. what's the net uplift of running c vs "legacy" torch code?

Thanks again for everything.

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