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    2. Joseph Bloom · Open Source Sparse Autoencoders for all Residual Stream Layers of GPT2-Small
    3. GPT2-Small
    4. Residual Stream
    5. 0-RES-JB
    6. 21860
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    INDEX
    Explanations

    mentions of the word "trains"

    oai_token-act-pair · gpt-3.5-turbo

    mentions of trains

    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
    wn
    -0.74
    uid
    -0.74
    hed
    -0.70
    cape
    -0.66
     Palm
    -0.65
    cus
    -0.61
     wiped
    -0.60
    hetical
    -0.60
     Herb
    -0.59
    kin
    -0.59
    POSITIVE LOGITS
     trains
    3.98
     train
    2.52
    Train
    2.10
     Train
    1.98
     buses
    1.96
     railways
    1.94
    train
    1.91
     Amtrak
    1.61
    cars
    1.55
     bikes
    1.55
    Activations Density 0.013%

    No Known Activations