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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. 4-RES-JB
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    Explanations

    mentions of grains, specifically wheat, barley, and rye

    oai_token-act-pair · gpt-3.5-turbo

    references to wheat and related grains

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

    No Comments

    Negative Logits
    ISTER
    -0.95
    Downloadha
    -0.80
    erial
    -0.73
    href
    -0.73
    nels
    -0.71
    isters
    -0.70
    ctica
    -0.68
    SPONSORED
    -0.68
     Recording
    -0.67
     reperto
    -0.66
    POSITIVE LOGITS
    grass
    1.00
    weed
    0.81
     germ
    0.80
     wheat
    0.79
     gluten
    0.79
     harvest
    0.78
     flour
    0.74
    bread
    0.74
    cake
    0.74
    paste
    0.74
    Activations Density 0.010%

    No Known Activations