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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. 2-RES-JB
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    Explanations

    mentions of the American football team "Bears," particularly focusing on mentions of their performance in games

    oai_token-act-pair · gpt-3.5-turbo

    references to the Chicago Bears football team

    oai_token-act-pair · gpt-4o-miniTriggered by @bot
    New Auto-Interp
    Top Features by Cosine Similarity
    Comparing With GPT2-SMALL @ 2-res-jb
    Configuration
    jbloom/GPT2-Small-SAEs-Reformatted/blocks.2.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.2.hook_resid_pre
    Architecture
    standard
    Context Size
    128
    Dataset
    Skylion007/openwebtext
    Hook Point Layer
    2
    Activation Function
    relu
    Embeds
    IFrame
    Link
    Not in Any Lists

    No Comments

    Negative Logits
    employment
    -0.81
    akuya
    -0.69
    onymous
    -0.69
    oppable
    -0.68
    umbn
    -0.67
    egal
    -0.65
    demand
    -0.64
    UTC
    -0.63
    iltration
    -0.63
    archy
    -0.62
    POSITIVE LOGITS
     Bears
    1.33
     Grizz
    0.87
    layer
    0.83
     Cubs
    0.78
    gans
    0.77
     Bryant
    0.77
    eman
    0.76
    士
    0.76
    manship
    0.76
     Scouts
    0.75
    Activations Density 0.006%

    No Known Activations