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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. 8-RES-JB
    11. 7604
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    Explanations

    specific nouns related to different resources - such as wood, cocaine, water, sap, fat, traffic, blood, money, milk, dirt, rice, light, trash, and garbage

    oai_token-act-pair · gpt-3.5-turbo

    nouns related to natural resources or substances

    oai_token-act-pair · gpt-4o-miniTriggered by @bot
    New Auto-Interp
    Top Features by Cosine Similarity
    Comparing With GPT2-SMALL @ 8-res-jb
    Configuration
    jbloom/GPT2-Small-SAEs-Reformatted/blocks.8.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.8.hook_resid_pre
    Architecture
    standard
    Context Size
    128
    Dataset
    Skylion007/openwebtext
    Hook Point Layer
    8
    Activation Function
    relu
    Embeds
    IFrame
    Link
    Not in Any Lists

    No Comments

    Negative Logits
     Polk
    -0.70
    DonaldTrump
    -0.68
    orio
    -0.65
    Ĥª
    -0.65
    ModLoader
    -0.63
    GBT
    -0.63
    Specific
    -0.62
    inia
    -0.61
    cffffcc
    -0.60
     Sparrow
    -0.60
    POSITIVE LOGITS
    iest
    1.31
     surrounding
    0.84
    fulness
    0.82
     portion
    0.82
     flowing
    0.82
    est
    0.82
     contained
    0.81
     allotted
    0.81
     generated
    0.81
     available
    0.80
    Activations Density 0.355%

    No Known Activations