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

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

Neuron 432 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: 5.1472. Min Act: -0.1700

Data Index: 487597 (C4 (Web Text))

Max Activating Token Index: 926

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Text #1

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Max Act: 5.0847. Min Act: -0.1700

Data Index: 400674 (C4 (Web Text))

Max Activating Token Index: 607

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Text #2

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Max Act: 4.9597. Min Act: -0.1700

Data Index: 168718 (C4 (Web Text))

Max Activating Token Index: 81

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Text #3

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Max Act: 4.8972. Min Act: -0.1700

Data Index: 1599508 (Python Code)

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Text #4

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Data Index: 301078 (C4 (Web Text))

Max Activating Token Index: 253

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Text #5

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Data Index: 825769 (C4 (Web Text))

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Text #6

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Data Index: 838132 (C4 (Web Text))

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Text #7

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Data Index: 773956 (C4 (Web Text))

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Text #8

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Data Index: 1650472 (Python Code)

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Text #9

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Data Index: 1459803 (Python Code)

Max Activating Token Index: 963

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Text #10

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Data Index: 607643 (C4 (Web Text))

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Text #11

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Text #12

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Data Index: 1128916 (C4 (Web Text))

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Text #13

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Data Index: 102789 (C4 (Web Text))

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Text #14

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Text #15

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Text #16

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Text #17

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Data Index: 427470 (C4 (Web Text))

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Text #18

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Data Index: 1044980 (C4 (Web Text))

Max Activating Token Index: 982

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Text #19

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Data Index: 273015 (C4 (Web Text))

Max Activating Token Index: 439

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