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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. 9-RES-JB
    11. 5694
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    Explanations

    numerical sequences representing sports teams' scores or statistics

    oai_token-act-pair · gpt-3.5-turbo

    specific numerical values or team names related to sports

    oai_token-act-pair · gpt-4o-miniTriggered by @bot
    New Auto-Interp
    Top Features by Cosine Similarity
    Comparing With GPT2-SMALL @ 9-res-jb
    Configuration
    jbloom/GPT2-Small-SAEs-Reformatted/blocks.9.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.9.hook_resid_pre
    Architecture
    standard
    Context Size
    128
    Dataset
    Skylion007/openwebtext
    Hook Point Layer
    9
    Activation Function
    relu
    Embeds
    IFrame
    Link
    Not in Any Lists

    No Comments

    Negative Logits
    catentry
    -0.70
    mop
    -0.67
    appropri
    -0.64
    kus
    -0.63
    mat
    -0.63
    inia
    -0.62
    gency
    -0.61
    SpaceEngineers
    -0.60
    minster
    -0.59
    chart
    -0.59
    POSITIVE LOGITS
     13
    1.02
     29
    1.01
     17
    1.00
     27
    0.99
     31
    0.98
     6
    0.98
     14
    0.97
     39
    0.97
     5
    0.96
     9
    0.95
    Activations Density 0.032%

    No Known Activations