Comment by Herring
21 days ago
I'd say try the nanogpt speedrun. It's much easier to train, and gives you a better comparison vs optimized systems.
21 days ago
I'd say try the nanogpt speedrun. It's much easier to train, and gives you a better comparison vs optimized systems.
The linked paper tested nanoGPT with this new transformer:
https://www.techrxiv.org/users/685780/articles/1375955-topol...
thanks for linking.
Yes the paper compares the new architecture (that is also a fork of my implementation of nanoGPT) with Karpathy's nanoGPT. There are also links to the code and bench used.
Note I didn't say Karpathy's nanoGPT, I said use the speedrun.
Transformers are universal function approximators. When well-tuned, they often start to approximate other innovations. Not always, thank god, but often enough that you have to be careful.
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Labs were also competing to train BERT's for $20 or less. People still use them a lot, too.
https://www.databricks.com/blog/mosaicbert
I'll add they should do a number of small, training runs with different architectures and data mixes. That proves generalization.