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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. 1-RES-JB
    6. 9231
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    Explanations

    instances where something is especially vulnerable or susceptible to certain conditions or events

    oai_token-act-pair · gpt-3.5-turbo

    the word "prone" in various contexts related to risk or vulnerability

    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
    ACA
    -0.79
    tein
    -0.78
     CLSID
    -0.75
    ILE
    -0.73
    ZA
    -0.70
    âĸijâĸij
    -0.70
    zyme
    -0.67
    GROUP
    -0.67
    elle
    -0.66
    OTOS
    -0.65
    POSITIVE LOGITS
     prone
    1.18
    entimes
    1.04
     challeng
    1.02
     confir
    1.01
     tremend
    0.96
     condem
    0.90
     experien
    0.86
     mathemat
    0.86
     conduc
    0.85
     compr
    0.85
    Activations Density 0.008%

    No Known Activations