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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

    references to different aspects of the learning process

    oai_token-act-pair · gpt-3.5-turbo

    references to different types and aspects of learning

    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
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    Negative Logits
    berto
    -0.67
    een
    -0.65
    osi
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    vati
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     tightly
    -0.61
     pers
    -0.60
    gpu
    -0.60
     Dak
    -0.60
    xon
    -0.60
    eah
    -0.60
    POSITIVE LOGITS
    learn
    0.92
    Learning
    0.90
    Lear
    0.84
     disabilities
    0.80
     Clicker
    0.78
    llor
    0.78
    Learn
    0.77
     Curve
    0.76
    shire
    0.76
     learners
    0.75
    Activations Density 0.016%

    No Known Activations