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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. 3-RES-JB
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    Explanations

    keywords related to sports team performance, particularly focusing on negative outcomes like losing or loss

    oai_token-act-pair · gpt-3.5-turbo

    instances of losing and 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 @ 3-res-jb
    Configuration
    jbloom/GPT2-Small-SAEs-Reformatted/blocks.3.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.3.hook_resid_pre
    Architecture
    standard
    Context Size
    128
    Dataset
    Skylion007/openwebtext
    Hook Point Layer
    3
    Activation Function
    relu
    Embeds
    IFrame
    Link
    Not in Any Lists

    No Comments

    Negative Logits
    tan
    -0.91
    ourge
    -0.82
    ridges
    -0.80
    haar
    -0.77
    acter
    -0.77
    osi
    -0.75
    wik
    -0.75
    enter
    -0.74
    trak
    -0.73
    assies
    -0.73
    POSITIVE LOGITS
     streak
    0.89
     captain
    0.86
     kicker
    0.82
     streaks
    0.76
     Defensive
    0.75
     playoff
    0.74
     unbeaten
    0.74
     tack
    0.73
     playoffs
    0.73
     defeat
    0.73
    Activations Density 0.031%

    No Known Activations