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    1. Home
    2. Joseph Bloom · Open Source Sparse Autoencoders for all Residual Stream Layers of GPT2-Small
    3. GPT2-Small
    4. Residual Stream
    5. 3-RES-JB
    6. 14624
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    Explanations

    references to different types of wines

    oai_token-act-pair · gpt-3.5-turbo

    mentions of wine

    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
    aneous
    -0.81
    aneously
    -0.79
    ulation
    -0.72
    ordinate
    -0.72
    ulu
    -0.70
    urat
    -0.70
    uled
    -0.70
    DonaldTrump
    -0.69
    urtle
    -0.68
     WATCHED
    -0.68
    POSITIVE LOGITS
     wine
    1.16
     tasting
    1.12
    wine
    1.09
     vinegar
    1.08
     grapes
    1.05
     cellar
    1.00
     tast
    0.96
     wines
    0.93
     vine
    0.92
     Wine
    0.88
    Activations Density 0.011%

    No Known Activations