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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. 1-RES-JB
    6. 4644
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    Explanations

    sequences related to highlighting or calling attention to specific information within a text

    oai_token-act-pair · gpt-3.5-turbo

    instances of the word "marks" related to various contexts or meanings

    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
     Garner
    -0.68
    ADS
    -0.68
    rouse
    -0.66
    OTE
    -0.66
    erest
    -0.65
     disproportionate
    -0.64
    lux
    -0.63
     Huang
    -0.63
    GAN
    -0.63
    ernand
    -0.63
    POSITIVE LOGITS
    manship
    1.48
     marks
    1.26
    marks
    1.22
     mark
    0.99
    mark
    0.96
    emark
    0.93
     marking
    0.91
    terday
    0.86
    marked
    0.85
     markings
    0.84
    Activations Density 0.008%

    No Known Activations