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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. 4-RES-JB
    11. 16266
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    Explanations

    negative outcomes in sports games

    oai_token-act-pair · gpt-3.5-turbo

    instances of loss 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
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    No Comments

    Negative Logits
    tan
    -0.89
    haar
    -0.75
    osi
    -0.70
    mun
    -0.70
    ourge
    -0.68
    azon
    -0.67
    de
    -0.66
    available
    -0.65
    ridges
    -0.65
    enegger
    -0.65
    POSITIVE LOGITS
     miser
    0.82
     streak
    0.78
     streaks
    0.74
    esses
    0.74
     foes
    0.74
     Champions
    0.72
     captain
    0.71
     Quarter
    0.71
     boss
    0.69
    bite
    0.68
    Activations Density 0.029%

    No Known Activations