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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

    mentions of losing in various sports contexts

    oai_token-act-pair · gpt-3.5-turbo

    instances of losses in sports contexts

    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
    ographies
    -0.74
    tan
    -0.73
    ridges
    -0.67
    mens
    -0.66
    amen
    -0.64
    NVIDIA
    -0.63
    vec
    -0.61
    rity
    -0.61
    enegger
    -0.60
     imported
    -0.59
    POSITIVE LOGITS
     miser
    1.03
     horribly
    0.81
     convinc
    0.81
     Incarn
    0.79
     decisively
    0.75
     streaks
    0.75
    onent
    0.70
     streak
    0.69
     rematch
    0.68
     badly
    0.68
    Activations Density 0.099%

    No Known Activations