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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. 6-RES-JB
    6. 24222
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    Explanations

    verbs related to actions or objectives, especially those that involve bringing, creating, showing, spreading, raising, providing, making, giving, or holding

    oai_token-act-pair · gpt-3.5-turbo

    actions related to bringing attention or awareness to something

    oai_token-act-pair · gpt-4o-miniTriggered by @bot
    New Auto-Interp
    Top Features by Cosine Similarity
    Comparing With GPT2-SMALL @ 6-res-jb
    Configuration
    jbloom/GPT2-Small-SAEs-Reformatted/blocks.6.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.6.hook_resid_pre
    Architecture
    standard
    Context Size
    128
    Dataset
    Skylion007/openwebtext
    Hook Point Layer
    6
    Activation Function
    relu
    Embeds
    IFrame
    Link
    Not in Any Lists

    No Comments

    Negative Logits
     eyed
    -0.64
     Torn
    -0.63
     attached
    -0.63
     fret
    -0.62
     pse
    -0.61
     rumored
    -0.60
     preferring
    -0.60
     fearing
    -0.59
     incl
    -0.59
    Guard
    -0.59
    POSITIVE LOGITS
    uate
    0.87
    atform
    0.81
    edia
    0.78
    entious
    0.78
    imize
    0.76
    IENT
    0.75
     disruptive
    0.74
    igate
    0.73
     awareness
    0.73
     disruption
    0.70
    Activations Density 0.375%

    No Known Activations