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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. 4-RES-JB
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    Explanations

    statistics related to football interceptions

    oai_token-act-pair · gpt-3.5-turbo

    occurrences of interceptions in sports contexts

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

    No Comments

    Negative Logits
    atten
    -0.80
    hov
    -0.72
    appa
    -0.72
    imb
    -0.71
    illac
    -0.70
    vet
    -0.70
    oras
    -0.69
    travel
    -0.69
    agame
    -0.69
    ood
    -0.67
    POSITIVE LOGITS
     interceptions
    1.05
     interception
    0.94
     touchdowns
    0.89
     touchdown
    0.84
     catcher
    0.83
     intercepted
    0.79
    DERR
    0.78
     fumble
    0.74
     cornerback
    0.71
     quarterback
    0.70
    Activations Density 0.007%

    No Known Activations