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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. 1-RES-JB
    6. 17434
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    Explanations

    references to photography or filming techniques involving taking multiple shots or exposures and combining them

    oai_token-act-pair · gpt-3.5-turbo

    references to "shots" in various contexts

    oai_token-act-pair · gpt-4o-miniTriggered by @bot
    New Auto-Interp
    Top Features by Cosine Similarity
    Comparing With GPT2-SMALL @ 1-res-jb
    Configuration
    jbloom/GPT2-Small-SAEs-Reformatted/blocks.1.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.1.hook_resid_pre
    Architecture
    standard
    Context Size
    128
    Dataset
    Skylion007/openwebtext
    Hook Point Layer
    1
    Activation Function
    relu
    Embeds
    IFrame
    Link
    Not in Any Lists

    No Comments

    Negative Logits
    ŃĶ
    -0.86
    ordinate
    -0.82
    lict
    -0.76
    ebook
    -0.74
    doms
    -0.73
    rab
    -0.72
    gres
    -0.69
    Case
    -0.68
    development
    -0.68
    rador
    -0.67
    POSITIVE LOGITS
     shots
    1.36
     shot
    1.09
     Shots
    1.07
    shots
    0.92
    hooting
    0.90
     shoots
    0.90
     Fired
    0.88
     shoot
    0.86
    shot
    0.86
     fired
    0.83
    Activations Density 0.012%

    No Known Activations