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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. 10-RES-JB
    11. 4483
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    Explanations

    phrases related to winning and losing

    oai_token-act-pair · gpt-3.5-turbo
    New Auto-Interp
    Top Features by Cosine Similarity
    Comparing With GPT2-SMALL @ 10-res-jb
    Configuration
    jbloom/GPT2-Small-SAEs-Reformatted/blocks.10.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.10.hook_resid_pre
    Architecture
    standard
    Context Size
    128
    Dataset
    Skylion007/openwebtext
    Hook Point Layer
    10
    Activation Function
    relu
    Embeds
    IFrame
    Link
    Not in Any Lists

    No Comments

    Negative Logits
    enhagen
    -0.87
    uga
    -0.68
    dyl
    -0.68
     Huss
    -0.68
    aeda
    -0.65
     agents
    -0.65
    ifles
    -0.62
    annel
    -0.62
     tracks
    -0.61
    istical
    -0.61
    POSITIVE LOGITS
    win
    0.96
     divest
    0.84
     Lose
    0.80
    loss
    0.79
    Win
    0.76
     loser
    0.74
    winner
    0.72
    ratulations
    0.71
     prosper
    0.69
     Loss
    0.69
    Activations Density 0.071%

    No Known Activations