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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. 9-RES-JB
    6. 9740
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    Explanations

    words related to the action of wearing something

    oai_token-act-pair · gpt-3.5-turbo

    words related to wearing and the condition of items that have been worn

    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
    aminer
    -0.85
    llor
    -0.76
    akespeare
    -0.74
    raction
    -0.70
    heid
    -0.68
    izoph
    -0.66
    ulhu
    -0.66
    yrinth
    -0.65
    ebus
    -0.65
    article
    -0.65
    POSITIVE LOGITS
     apparel
    1.05
    ables
    1.04
     worn
    1.00
     clothing
    0.94
     underwear
    0.93
     diapers
    0.91
     gloves
    0.87
     clothes
    0.85
    wear
    0.84
     shoes
    0.84
    Activations Density 0.045%

    No Known Activations