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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. 3-RES-JB
    6. 21488
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    Explanations

    phrases indicating to keep an eye on something or someone

    oai_token-act-pair · gpt-3.5-turbo

    phrases that indicate keeping watch or paying attention

    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
    minus
    -0.68
    theless
    -0.67
     congr
    -0.66
     Violent
    -0.64
     Vide
    -0.64
    stood
    -0.63
    channelAvailability
    -0.63
    emonium
    -0.62
     Resistance
    -0.60
    ities
    -0.59
    POSITIVE LOGITS
     sockets
    0.99
    ight
    0.89
    bre
    0.86
     eye
    0.84
    eyed
    0.83
    ovember
    0.79
     noses
    0.79
     nose
    0.78
    bridge
    0.78
    Eye
    0.78
    Activations Density 0.016%

    No Known Activations