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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. 22575
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    Explanations

    terms related to exploitation and measures of quantity

    oai_token-act-pair · gpt-3.5-turbo

    terms related to exploitative practices and quantitative analyses

    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
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    No Comments

    Negative Logits
     cane
    -0.69
     Crab
    -0.68
     mole
    -0.68
    pin
    -0.63
     circus
    -0.62
    kos
    -0.61
     spice
    -0.60
    angan
    -0.60
    Cos
    -0.59
    ITNESS
    -0.59
    POSITIVE LOGITS
    itative
    4.36
    itatively
    2.41
     quantitative
    1.50
     qualitative
    1.29
     Quant
    1.21
    ulatory
    1.00
    ocative
    0.95
    izational
    0.92
    itive
    0.88
    itations
    0.87
    Activations Density 0.033%

    No Known Activations