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    3. Joseph Bloom · Open Source Sparse Autoencoders for all Residual Stream Layers of GPT2-Small
    5. GPT2-Small
    7. Residual Stream
    9. 11-RES-JB
    11. 13968
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    Explanations

    statistics related to sports performance, specifically focused on field goals, shooting and save percentages, and goals scored

    oai_token-act-pair · gpt-3.5-turbo

    statistics and metrics related to performance in sports

    oai_token-act-pair · gpt-4o-miniTriggered by @bot
    New Auto-Interp
    Top Features by Cosine Similarity
    Comparing With GPT2-SMALL @ 11-res-jb
    Configuration
    jbloom/GPT2-Small-SAEs-Reformatted/blocks.11.hook_resid_pre
    Prompts (Dashboard)
    24,576 prompts, 128 tokens each
    Dataset (Dashboard)
    Skylion007/openwebtext
    Features
    24,576
    Data Type
    torch.float32
    Hook Point
    blocks.11.hook_resid_pre
    Architecture
    standard
    Context Size
    128
    Dataset
    Skylion007/openwebtext
    Hook Point Layer
    11
    Activation Function
    relu
    Embeds
    IFrame
    Link
    Not in Any Lists

    No Comments

    Negative Logits
    enegger
    -1.13
     Instruments
    -0.68
     Canaver
    -0.64
    layer
    -0.62
     ItemLevel
    -0.62
    abee
    -0.61
     Yanuk
    -0.60
    grim
    -0.60
     Brist
    -0.59
     Hau
    -0.59
    POSITIVE LOGITS
     percentage
    1.02
    Attempts
    0.98
     assists
    0.90
     Percentage
    0.90
    Rate
    0.87
    phies
    0.86
    %),
    0.85
    Attempt
    0.84
    %,
    0.83
    agements
    0.82
    Activations Density 0.087%

    No Known Activations