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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. 1-RES-JB
    11. 2251
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    Explanations

    phrases related to preparation and planning

    oai_token-act-pair · gpt-3.5-turbo

    terms related to upfront costs and actions taken in advance

    oai_token-act-pair · gpt-4o-miniTriggered by @bot
    New Auto-Interp
    Top Features by Cosine Similarity
    Comparing With GPT2-SMALL @ 1-res-jb
    Configuration
    jbloom/GPT2-Small-SAEs-Reformatted/blocks.1.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.1.hook_resid_pre
    Architecture
    standard
    Context Size
    128
    Dataset
    Skylion007/openwebtext
    Hook Point Layer
    1
    Activation Function
    relu
    Embeds
    IFrame
    Link
    Not in Any Lists

    No Comments

    Negative Logits
     Mehran
    -0.69
     Tune
    -0.64
     Dys
    -0.64
    ifling
    -0.64
     Jarvis
    -0.63
     Occupations
    -0.61
     Wend
    -0.60
     Rapp
    -0.60
    =-=-=-=-=-=-=-=-
    -0.60
    ĪĴ
    -0.60
    POSITIVE LOGITS
     upfront
    1.36
    cale
    0.80
    alone
    0.76
    intensive
    0.75
    lishes
    0.72
     rehears
    0.70
    setup
    0.70
     onstage
    0.69
     nailed
    0.69
    ebted
    0.69
    Activations Density 0.013%

    No Known Activations