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Model: GELU Model: 2 Layers, 2048 Neurons per Layer

Dataset: 80% C4 (Web Text) and 20% Python Code

Neuron 680 in Layer 1

Load this data into an Interactive Neuroscope

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Transformer Lens Loading: HookedTransformer.from_pretrained('gelu-2l')



Text #0

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Max Act: 4.9197. Min Act: -0.1700

Data Index: 1122068 (C4 (Web Text))

Max Activating Token Index: 47

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Text #1

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Max Act: 4.9822. Min Act: -0.1700

Data Index: 1594502 (Python Code)

Max Activating Token Index: 650

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Text #2

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Max Act: 4.8885. Min Act: -0.1700

Data Index: 1315988 (C4 (Web Text))

Max Activating Token Index: 381

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Text #3

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Data Index: 41671 (C4 (Web Text))

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Text #4

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Data Index: 1463982 (Python Code)

Max Activating Token Index: 586

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Text #5

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Data Index: 857768 (C4 (Web Text))

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Text #6

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Data Index: 872134 (C4 (Web Text))

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Text #7

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Data Index: 1019103 (C4 (Web Text))

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Text #8

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Data Index: 463564 (C4 (Web Text))

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Text #9

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Data Index: 822741 (C4 (Web Text))

Max Activating Token Index: 382

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Text #10

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Data Index: 990783 (C4 (Web Text))

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Text #11

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Data Index: 58822 (C4 (Web Text))

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Text #12

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Data Index: 949721 (C4 (Web Text))

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Text #13

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Text #14

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Text #15

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Data Index: 1328773 (C4 (Web Text))

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Text #16

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Data Index: 487233 (C4 (Web Text))

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Text #17

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Data Index: 1443246 (Python Code)

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Text #18

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Max Act: 4.7010. Min Act: -0.1700

Data Index: 791110 (C4 (Web Text))

Max Activating Token Index: 890

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Text #19

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Data Index: 1077365 (C4 (Web Text))

Max Activating Token Index: 643

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