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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. 10-RES-JB
    11. 8057
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    Explanations

    plays in sports-related contexts, especially with a focus on scoring or making significant contributions

    oai_token-act-pair · gpt-3.5-turbo

    actions related to scoring or contributing in sports

    oai_token-act-pair · gpt-4o-miniTriggered by @bot
    New Auto-Interp
    Top Features by Cosine Similarity
    Comparing With GPT2-SMALL @ 10-res-jb
    Configuration
    jbloom/GPT2-Small-SAEs-Reformatted/blocks.10.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.10.hook_resid_pre
    Architecture
    standard
    Context Size
    128
    Dataset
    Skylion007/openwebtext
    Hook Point Layer
    10
    Activation Function
    relu
    Embeds
    IFrame
    Link
    Not in Any Lists

    No Comments

    Negative Logits
     UNCLASSIFIED
    -0.67
    enegger
    -0.67
     EVENTS
    -0.63
     âĸº
    -0.63
    abis
    -0.61
    uary
    -0.60
    verend
    -0.59
     voiced
    -0.58
    Ingredients
    -0.57
     curric
    -0.56
    POSITIVE LOGITS
     Dallas
    0.85
     Pittsburgh
    0.83
    Philadelphia
    0.82
     Detroit
    0.78
    adelphia
    0.76
    Dallas
    0.75
     Philadelphia
    0.74
     PHI
    0.74
     runners
    0.73
     Carolina
    0.73
    Activations Density 0.122%

    No Known Activations