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.
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.