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    1. Home
    2. Joseph Bloom · Open Source Sparse Autoencoders for all Residual Stream Layers of GPT2-Small
    3. GPT2-Small
    4. Residual Stream
    5. 4-RES-JB
    6. 12789
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    Explanations

    sports statistics and achievements

    oai_token-act-pair · gpt-3.5-turbo

    statistics and numerical performance metrics 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
    onym
    -0.97
    actionDate
    -0.84
    nel
    -0.82
    olitical
    -0.76
    igm
    -0.74
    markets
    -0.72
    sonian
    -0.72
    rities
    -0.71
    hod
    -0.69
    ulas
    -0.68
    POSITIVE LOGITS
     averaging
    0.87
     touchdowns
    0.83
     overtime
    0.82
     passing
    0.81
     tying
    0.80
     scored
    0.79
     scoring
    0.78
     postseason
    0.78
     tallied
    0.78
     bullpen
    0.76
    Activations Density 0.404%

    No Known Activations