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    3. Google DeepMind · Exploring Gemma 2 with Gemma Scope
    5. Gemma-2-9B
    7. Residual Stream - 16k
    9. 34-GEMMASCOPE-RES-16K
    11. 1271
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    Explanations

    references to different types of food, particularly cheese and cereal products

    oai_token-act-pair · gpt-4o-miniTriggered by @bot
    New Auto-Interp
    Top Features by Cosine Similarity
    Comparing With GEMMA-2-9B @ 34-gemmascope-res-16k
    Configuration
    google/gemma-scope-9b-pt-res/layer_34/width_16k/average_l0_114
    Prompts (Dashboard)
    24,576 prompts, 128 tokens each
    Dataset (Dashboard)
    monology/pile-uncopyrighted
    Features
    16,384
    Data Type
    float32
    Hook Name
    blocks.34.hook_resid_post
    Hook Layer
    34
    Architecture
    jumprelu
    Context Size
    1,024
    Dataset
    monology/pile-uncopyrighted
    Activation Function
    relu
    Embeds
    IFrame
    Link
    Not in Any Lists

    No Comments

    Negative Logits
    GEBURTSDATUM
    -0.54
    rungsseite
    -0.46
    ampunk
    -0.46
     WebDriverWait
    -0.44
    MLLoader
    -0.43
    smithy
    -0.43
    PropertyChanging
    -0.42
    醐
    -0.41
     cuarzo
    -0.41
     Apparatus
    -0.40
    POSITIVE LOGITS
     Pring
    0.83
     canned
    0.74
     cereal
    0.69
     packaged
    0.69
     Kellogg
    0.66
     Nestle
    0.66
     instant
    0.66
     brands
    0.66
     frozen
    0.64
     cans
    0.61
    Activations Density 0.282%

    No Known Activations