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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. 7-RES-JB
    6. 10218
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    Explanations

    phrases related to planning or scheduling ahead of time

    oai_token-act-pair · gpt-3.5-turbo

    references to planning or scheduling events ahead of time

    oai_token-act-pair · gpt-4o-miniTriggered by @bot
    New Auto-Interp
    Top Features by Cosine Similarity
    Comparing With GPT2-SMALL @ 7-res-jb
    Configuration
    jbloom/GPT2-Small-SAEs-Reformatted/blocks.7.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.7.hook_resid_pre
    Architecture
    standard
    Context Size
    128
    Dataset
    Skylion007/openwebtext
    Hook Point Layer
    7
    Activation Function
    relu
    Embeds
    IFrame
    Link
    Not in Any Lists

    No Comments

    Negative Logits
    BuyableInstoreAndOnline
    -0.84
    ĪĴ
    -0.74
    adra
    -0.71
     Klux
    -0.68
    ById
    -0.67
     Cosponsors
    -0.67
    ikes
    -0.65
    drivers
    -0.64
    ranch
    -0.63
    DCS
    -0.63
    POSITIVE LOGITS
     screenings
    0.94
     screening
    0.94
    noon
    0.93
     planning
    0.82
     scheduling
    0.80
     schedules
    0.74
    ogene
    0.72
     booking
    0.72
     informing
    0.72
     staging
    0.72
    Activations Density 0.024%

    No Known Activations