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Google DeepMind · Exploring Gemma 2 with Gemma Scope
Gemma-2-2B
Residual Stream - 16k
6-GEMMASCOPE-RES-16K
5187
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MODEL
6-gemmascope-res-16k
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Explanations
the word "who" in various contexts
oai_token-act-pair · gpt-4o-mini
Triggered by @bot
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New Auto-Interp
AutoInterp Type
claude-4-5-haiku
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Top Features by Cosine Similarity
Comparing With
GEMMA-2-2B @ 6-gemmascope-res-16k
Configuration
google/gemma-scope-2b-pt-res/layer_6/width_16k/average_l0_70
How To Load
Prompts (Dashboard)
36,864 prompts, 128 tokens each
Dataset (Dashboard)
monology/pile-uncopyrighted
Features
16,384
Data Type
float32
Hook Name
blocks.6.hook_resid_post
Hook Layer
6
Architecture
jumprelu
Context Size
1,024
Dataset
monology/pile-uncopyrighted
Activation Function
relu
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<iframe src="https://www.neuronpedia.org/gemma-2-2b/6-gemmascope-res-16k/5187?embed=true&embedexplanation=true&embedplots=true&embedsteer=true&embedactivations=true&embedlink=true&embedtest=true" title="Neuronpedia" style="height: 300px; width: 540px;"></iframe>
Link
https://www.neuronpedia.org/gemma-2-2b/6-gemmascope-res-16k/5187?embed=true&embedexplanation=true&embedplots=true&embedsteer=true&embedactivations=true&embedlink=true&embedtest=true
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Negative Logits
らう
-0.73
Eis
-0.71
Steck
-0.70
alis
-0.64
Merk
-0.64
Wellen
-0.62
έκ
-0.62
ます
-0.61
Augustin
-0.61
Veer
-0.59
POSITIVE LOGITS
who
2.00
who
1.66
WHO
1.55
Who
1.53
Who
1.47
WHO
1.40
którzy
1.35
whom
1.34
whom
1.25
quien
1.24
Act
ivations
Density 0.101%
Test
Steer
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