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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. 6972
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    Explanations

    quantitative measurements expressed as rates or quantities per unit of time or other units

    oai_token-act-pair · gpt-3.5-turbo

    metrics and measurements related to time intervals and quantities

    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
    actionDate
    -0.85
    ĪĴ
    -0.72
    reth
    -0.70
    tymology
    -0.69
    atorial
    -0.69
     Roose
    -0.68
     Jonas
    -0.66
    gotten
    -0.66
    uador
    -0.65
    enhagen
    -0.64
    POSITIVE LOGITS
     thereafter
    0.85
     increments
    0.81
     basis
    0.78
    ²
    0.73
     average
    0.71
     throughout
    0.69
     (~
    0.68
     allowance
    0.68
    Footnote
    0.67
     plus
    0.67
    Activations Density 0.041%

    No Known Activations