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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
    11. 20770
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    Explanations

    numbers associated with minutes played in sports contexts

    oai_token-act-pair · gpt-3.5-turbo

    statistics related to sports performance

    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
    ortium
    -0.72
    rification
    -0.70
     Employ
    -0.67
     commenter
    -0.64
    aptic
    -0.63
    ¿½
    -0.63
     Preservation
    -0.61
    ermanent
    -0.61
    gement
    -0.61
     Expend
    -0.61
    POSITIVE LOGITS
     concurrently
    0.89
     innings
    0.89
     differently
    0.88
     consecut
    0.87
    intensive
    0.82
     professionally
    0.81
    orously
    0.80
     games
    0.79
     aggressively
    0.79
    heartedly
    0.78
    Activations Density 0.467%

    No Known Activations