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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. 10-RES-JB
    6. 15785
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    Explanations

    phrases related to business operations, such as improving diversity on boards, shifting profits, cutting back spending, and revising production forecasts

    oai_token-act-pair · gpt-3.5-turbo

    concepts related to corporate regulations and accountability

    oai_token-act-pair · gpt-4o-miniTriggered by @bot
    New Auto-Interp
    Top Features by Cosine Similarity
    Comparing With GPT2-SMALL @ 10-res-jb
    Configuration
    jbloom/GPT2-Small-SAEs-Reformatted/blocks.10.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.10.hook_resid_pre
    Architecture
    standard
    Context Size
    128
    Dataset
    Skylion007/openwebtext
    Hook Point Layer
    10
    Activation Function
    relu
    Embeds
    IFrame
    Link
    Not in Any Lists

    No Comments

    Negative Logits
    REDACTED
    -0.83
    _-
    -0.69
     (@
    -0.69
    achu
    -0.66
    anon
    -0.65
    rome
    -0.64
    ertodd
    -0.64
    Redditor
    -0.63
    soc
    -0.63
     Methodist
    -0.62
    POSITIVE LOGITS
     their
    1.40
     themselves
    1.31
    their
    1.17
     customers
    1.13
     budgets
    1.13
     THEIR
    1.07
    Their
    1.05
     profits
    1.05
     profitability
    1.02
     Their
    1.02
    Activations Density 0.633%

    No Known Activations