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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. 10-RES-JB
    6. 11581
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    INDEX
    Explanations

    traits or characteristics with positive or negative connotations such as "all-star", "criminal", "professional", "dark", "competitive", "dumb", "visual", "Swedish residency", "direct", "concrete", "moral", "liberal", "diplomatic", "

    oai_token-act-pair · gpt-3.5-turbo

    keywords related to criminality and professional qualifications

    oai_token-act-pair · gpt-4o-miniTriggered by @bot
    New Auto-Interp
    Top Features by Cosine Similarity
    Comparing With GPT2-SMALL @ 10-res-jb
    Configuration
    jbloom/GPT2-Small-SAEs-Reformatted/blocks.10.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.10.hook_resid_pre
    Architecture
    standard
    Context Size
    128
    Dataset
    Skylion007/openwebtext
    Hook Point Layer
    10
    Activation Function
    relu
    Embeds
    IFrame
    Link
    Not in Any Lists

    No Comments

    Negative Logits
    hander
    -0.82
    href
    -0.78
    rame
    -0.72
    xtap
    -0.71
    hooting
    -0.70
    hemer
    -0.69
    tyard
    -0.69
    onement
    -0.66
    wark
    -0.66
    hower
    -0.65
    POSITIVE LOGITS
     aspirations
    1.26
     ambitions
    1.22
     tendencies
    1.21
     intentions
    1.19
     privileges
    1.14
     backgrounds
    1.12
     sensibilities
    1.09
     capabilities
    1.07
     ties
    1.06
     responsibilities
    1.05
    Activations Density 0.335%

    No Known Activations