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    1. Home
    2. Joseph Bloom · Open Source Sparse Autoencoders for all Residual Stream Layers of GPT2-Small
    3. GPT2-Small
    4. Residual Stream
    5. 8-RES-JB
    6. 5316
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    INDEX
    Explanations

    language related to setting goals and objectives

    oai_token-act-pair · gpt-3.5-turbo

    statements related to goals and intentions

    oai_token-act-pair · gpt-4o-miniTriggered by @bot
    New Auto-Interp
    Top Features by Cosine Similarity
    Comparing With GPT2-SMALL @ 8-res-jb
    Configuration
    jbloom/GPT2-Small-SAEs-Reformatted/blocks.8.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.8.hook_resid_pre
    Architecture
    standard
    Context Size
    128
    Dataset
    Skylion007/openwebtext
    Hook Point Layer
    8
    Activation Function
    relu
    Embeds
    IFrame
    Link
    Not in Any Lists

    No Comments

    Negative Logits
    eor
    -0.72
     emits
    -0.66
    eus
    -0.64
     Ago
    -0.64
    haw
    -0.63
     Happ
    -0.62
    ython
    -0.61
    quin
    -0.61
     Neighbor
    -0.60
     Fault
    -0.59
    POSITIVE LOGITS
     unclear
    0.76
     moot
    0.75
     maximizing
    0.75
     simplicity
    0.75
     simple
    0.72
     laud
    0.70
     clear
    0.69
     therefore
    0.69
     always
    0.69
     undoubtedly
    0.68
    Activations Density 0.179%

    No Known Activations