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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. 0-RES-JB
    11. 15509
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    Explanations

    phrases related to acquiring, attaining, or achieving something

    oai_token-act-pair · gpt-3.5-turbo

    phrases associated with gaining or obtaining something

    oai_token-act-pair · gpt-4o-miniTriggered by @bot
    New Auto-Interp
    Top Features by Cosine Similarity
    Comparing With GPT2-SMALL @ 0-res-jb
    Configuration
    jbloom/GPT2-Small-SAEs-Reformatted/blocks.0.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.0.hook_resid_pre
    Architecture
    standard
    Context Size
    128
    Dataset
    Skylion007/openwebtext
    Hook Point Layer
    0
    Activation Function
    relu
    Embeds
    IFrame
    Link
    Not in Any Lists

    No Comments

    Negative Logits
    soType
    -0.72
    era
    -0.68
    ew
    -0.64
    ymes
    -0.63
    orah
    -0.63
     dated
    -0.61
    busters
    -0.61
    laws
    -0.61
    ror
    -0.60
     Close
    -0.59
    POSITIVE LOGITS
     gain
    3.67
    gain
    2.56
     Gain
    2.25
     gains
    2.24
     regain
    1.89
     gained
    1.87
     gaining
    1.82
     benefit
    1.32
     profit
    1.32
     acquire
    1.29
    Activations Density 0.021%

    No Known Activations