Jakub Smékal

• Jakub Smékal 20 Feb 2024 17:43 UTC

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  in reply to: Joseph Bloom’s comment on: Open Source Sparse Au­toen­coders for all Resi­d­ual Stream Lay­ers of GPT2-Small

  Neel was advised by the authors that it was important minimise batches having tokens from the same prompt. This approach leads to a buffer having activations from many different prompts fairly quickly.

  Oh I see, it’s a constraint on the tokens from the vocabulary rather than the prompts. Does the buffer ever reuse prompts or does it always use new ones?

• Jakub Smékal 19 Feb 2024 19:24 UTC

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  on: Open Source Sparse Au­toen­coders for all Resi­d­ual Stream Lay­ers of GPT2-Small

  • We store activations in a buffer of ~500k tokens which is refilled and shuffled whenever 50% of the tokens are used (ie: Neel’s approach).

  I am not sure I understand the reasoning around this approach. Why do you want to refill and shuffle tokens whenever 50% of the tokens are used? Is this just tokens in the training set or also the test set? In Neel’s code I didn’t see a train/​test split, isn’t that important? Also, can you track the number of epochs of training when using this buffer method (it seems like that makes it more difficult)?

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• Jakub Smékal 25 Jan 2024 5:19 UTC

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  on: Sparse Au­toen­coders Work on At­ten­tion Layer Outputs

  Hey, great post! Are your code or autoencoder weights available somewhere?

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