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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. 7-RES-JB
    11. 19049
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    Explanations

    phrases related to grabbing attention or bringing something to focus

    oai_token-act-pair · gpt-3.5-turbo

    references to drawing or attracting attention to various issues or events

    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
     testament
    -0.70
    tein
    -0.68
     Yugoslavia
    -0.65
    rely
    -0.63
     recip
    -0.63
     Tale
    -0.62
     Sic
    -0.60
    surv
    -0.59
     halves
    -0.59
     inval
    -0.59
    POSITIVE LOGITS
     attention
    0.91
     largeDownload
    0.86
    elight
    0.82
     Attention
    0.82
    eele
    0.81
    peed
    0.78
    ð
    0.73
    orial
    0.72
    ively
    0.71
    Sense
    0.71
    Activations Density 0.019%

    No Known Activations