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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. 9-RES-JB
    11. 3334
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    INDEX
    Explanations

    phrases emphasizing intensity or emphasis, particularly the repetition of intensifiers like "really" or "very"

    oai_token-act-pair · gpt-3.5-turbo

    intensifiers and adverbs that emphasize degree or extent

    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
    ascript
    -0.77
    itures
    -0.68
    udic
    -0.67
    atches
    -0.66
    iture
    -0.66
    isode
    -0.66
    Footnote
    -0.65
    acas
    -0.64
    Frames
    -0.64
    idas
    -0.64
    POSITIVE LOGITS
     fucked
    0.87
     unlucky
    0.85
     bad
    0.84
     pissed
    0.80
     stupid
    0.78
     screwed
    0.77
     messed
    0.77
     close
    0.77
     boring
    0.73
     shitty
    0.73
    Activations Density 0.124%

    No Known Activations