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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. 6-RES-JB
    11. 17347
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    Explanations

    references to water-related issues and events, particularly focusing on contamination, availability, and distribution

    oai_token-act-pair · gpt-3.5-turbo

    terms related to water quality and availability issues

    oai_token-act-pair · gpt-4o-miniTriggered by @bot
    New Auto-Interp
    Top Features by Cosine Similarity
    Comparing With GPT2-SMALL @ 6-res-jb
    Configuration
    jbloom/GPT2-Small-SAEs-Reformatted/blocks.6.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.6.hook_resid_pre
    Architecture
    standard
    Context Size
    128
    Dataset
    Skylion007/openwebtext
    Hook Point Layer
    6
    Activation Function
    relu
    Embeds
    IFrame
    Link
    Not in Any Lists

    No Comments

    Negative Logits
     suprem
    -0.78
    elligent
    -0.77
    KK
    -0.74
    ierre
    -0.73
    aughs
    -0.72
    ¯
    -0.70
     Reboot
    -0.69
    iev
    -0.68
    ocus
    -0.67
    IX
    -0.67
    POSITIVE LOGITS
     gravel
    0.94
     contamin
    0.92
     sediment
    0.92
     dams
    0.91
     reservoirs
    0.90
     polluted
    0.90
     contaminated
    0.87
     vegetation
    0.87
     grains
    0.86
     minerals
    0.86
    Activations Density 0.716%

    No Known Activations