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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. 3-RES-JB
    6. 7755
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    Explanations

    words related to food and cooking, particularly cheese and specific ingredients

    oai_token-act-pair · gpt-3.5-turbo

    words and phrases that describe food and culinary experiences

    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
     Accountability
    -0.76
     Lines
    -0.61
     Pwr
    -0.57
     Integrity
    -0.56
     Balanced
    -0.56
     Transparency
    -0.54
     Own
    -0.54
     Marketplace
    -0.54
     Parish
    -0.53
     Commons
    -0.53
    POSITIVE LOGITS
    oots
    0.74
    ailable
    0.59
    ettes
    0.59
    ishly
    0.58
    ioned
    0.56
    pees
    0.56
    ciation
    0.56
    arious
    0.55
    kered
    0.55
    ensis
    0.54
    Activations Density 0.657%

    No Known Activations