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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. 3-RES-JB
    11. 15491
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    Explanations

    mentions of dining establishments or dining-related activities

    oai_token-act-pair · gpt-3.5-turbo

    mentions of dining and seating arrangements

    oai_token-act-pair · gpt-4o-miniTriggered by @bot
    New Auto-Interp
    Top Features by Cosine Similarity
    Comparing With GPT2-SMALL @ 3-res-jb
    Configuration
    jbloom/GPT2-Small-SAEs-Reformatted/blocks.3.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.3.hook_resid_pre
    Architecture
    standard
    Context Size
    128
    Dataset
    Skylion007/openwebtext
    Hook Point Layer
    3
    Activation Function
    relu
    Embeds
    IFrame
    Link
    Not in Any Lists

    No Comments

    Negative Logits
     Archangel
    -0.77
     Aerospace
    -0.77
     Mub
    -0.74
     Belarus
    -0.73
    Downloadha
    -0.69
    vernment
    -0.67
    RAY
    -0.66
     Watts
    -0.65
     Mehran
    -0.65
    ISS
    -0.64
    POSITIVE LOGITS
     dining
    1.06
    eteria
    1.01
     halls
    0.92
     Restaur
    0.91
     tastes
    0.88
     establishments
    0.88
    iasco
    0.87
     restaurants
    0.86
     etiquette
    0.84
     guests
    0.84
    Activations Density 0.013%

    No Known Activations