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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. 9-RES-JB
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    Explanations

    sports teams, specifically focusing on the Chicago Bears and the Chicago Cubs

    oai_token-act-pair · gpt-3.5-turbo

    instances of the word "Chicago" specifically in relation to sports teams and events

    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
    merce
    -0.88
     ende
    -0.77
    egal
    -0.76
    ngth
    -0.76
    ilater
    -0.73
    aimon
    -0.72
     convol
    -0.71
    yss
    -0.70
    itton
    -0.70
    undown
    -0.70
    POSITIVE LOGITS
     Tribune
    1.24
     Blackhawks
    1.16
     Cubs
    1.14
     Bulls
    1.07
     Bears
    1.03
     Loop
    0.82
     Booth
    0.82
     Heights
    0.80
    icago
    0.79
     suburbs
    0.77
    Activations Density 0.028%

    No Known Activations