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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. 9-RES-JB
    6. 11840
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    Explanations

    instances where someone is being advised or reminded to pay attention to something

    oai_token-act-pair · gpt-3.5-turbo

    phrases emphasizing paying attention

    oai_token-act-pair · gpt-4o-miniTriggered by @bot
    New Auto-Interp
    Top Features by Cosine Similarity
    Comparing With GPT2-SMALL @ 9-res-jb
    Configuration
    jbloom/GPT2-Small-SAEs-Reformatted/blocks.9.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.9.hook_resid_pre
    Architecture
    standard
    Context Size
    128
    Dataset
    Skylion007/openwebtext
    Hook Point Layer
    9
    Activation Function
    relu
    Embeds
    IFrame
    Link
    Not in Any Lists

    No Comments

    Negative Logits
    rie
    -0.73
    yre
    -0.64
    iHUD
    -0.63
    lua
    -0.62
    \\\\\\\\
    -0.61
    venge
    -0.58
    \\\\
    -0.58
    atu
    -0.57
     Royale
    -0.57
    apons
    -0.57
    POSITIVE LOGITS
     attent
    0.90
     to
    0.74
     NOTICE
    0.73
    olescent
    0.68
    é¾įå¥ij士
    0.66
    idious
    0.66
     closely
    0.65
    itness
    0.65
     attention
    0.64
    ibilities
    0.64
    Activations Density 0.050%

    No Known Activations