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Thank you for the post! Either to Neel, or to anyone else in the comments: what datasets have you found most useful for testing tiny image transformers? 


The vit-pytorch repo uses the cats vs dogs repo from Kaggle but I'm wondering if this is too complex for the very simple image transformers.

Hi Neel! Thanks so much for all these online resources. I've been finding them really interesting and helpful.

I have a question about research methods. "How far can you get with really deeply reverse engineering a neuron in a 1 layer (1L) model? (solu-1l, solu-1l-pile or gelu-1l in TransformerLens)."

I've loaded up solu-1l in my Jupyter notebook but now feeling a bit lost. For your IOI tutorial, there was a very specific benchmark and error signal. However, when I'm just playing around with a model without a clear capability in mind, it's harder to know how to measure performance. I could make a list of capabilities/benchmarks, systematically run the model on them, and then pick a capability and start ablating the model and seeing effect on performance. However, I'm then restricted to these predefined capabilities. Like, I'm not even sure what the capabilities of solu-1l are.

I could start feeding solu-1l with random inputs and just "looking" at the attention patterns. But I'm wondering if there's a more efficient way to do this-- or another strategy where research does feel like play, as you describe in your notebook.

Thank you!