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    3. Joseph Bloom · Open Source Sparse Autoencoders for all Residual Stream Layers of GPT2-Small
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    7. Residual Stream
    9. 3-RES-JB
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    Explanations

    mentions of different types of cheese, particularly cheddar and cheese in various contexts

    oai_token-act-pair · gpt-3.5-turbo

    references to cheese

    oai_token-act-pair · gpt-4o-miniTriggered by @bot
    New Auto-Interp
    Top Features by Cosine Similarity
    Comparing With GPT2-SMALL @ 3-res-jb
    Configuration
    jbloom/GPT2-Small-SAEs-Reformatted/blocks.3.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.3.hook_resid_pre
    Architecture
    standard
    Context Size
    128
    Dataset
    Skylion007/openwebtext
    Hook Point Layer
    3
    Activation Function
    relu
    Embeds
    IFrame
    Link
    Not in Any Lists

    No Comments

    Negative Logits
    nant
    -0.79
    igious
    -0.72
    usc
    -0.66
     eminent
    -0.66
     Walton
    -0.66
    NEY
    -0.66
    OR
    -0.63
    ITNESS
    -0.63
     Latter
    -0.63
     LDS
    -0.62
    POSITIVE LOGITS
    cloth
    1.22
     cheese
    0.99
     slic
    0.91
    fruit
    0.89
    ecake
    0.89
     sandwich
    0.88
    weed
    0.88
    bowl
    0.87
     popcorn
    0.86
     sauce
    0.85
    Activations Density 0.013%

    No Known Activations