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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. 0-RES-JB
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    Explanations

    mentions of and references to lunch

    oai_token-act-pair · gpt-3.5-turbo

    references to lunch meals

    oai_token-act-pair · gpt-4o-miniTriggered by @bot
    New Auto-Interp
    Top Features by Cosine Similarity
    Comparing With GPT2-SMALL @ 0-res-jb
    Configuration
    jbloom/GPT2-Small-SAEs-Reformatted/blocks.0.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.0.hook_resid_pre
    Architecture
    standard
    Context Size
    128
    Dataset
    Skylion007/openwebtext
    Hook Point Layer
    0
    Activation Function
    relu
    Embeds
    IFrame
    Link
    Not in Any Lists

    No Comments

    Negative Logits
    worthiness
    -0.85
     Sync
    -0.75
    der
    -0.68
    ases
    -0.67
    alties
    -0.66
    oc
    -0.66
    borne
    -0.65
    itcher
    -0.64
     warranted
    -0.64
    itching
    -0.63
    POSITIVE LOGITS
     lunch
    3.77
     Lunch
    2.81
     breakfast
    2.33
     dinner
    2.29
     supper
    2.06
     brunch
    1.94
     meal
    1.83
     lun
    1.82
     meals
    1.78
     Dinner
    1.65
    Activations Density 0.012%

    No Known Activations