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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. 9-RES-JB
    6. 19001
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    Explanations

    aspects related to ambitions and goals

    oai_token-act-pair · gpt-3.5-turbo

    terms related to ambition and aspirations

    oai_token-act-pair · gpt-4o-miniTriggered by @bot
    New Auto-Interp
    Top Features by Cosine Similarity
    Comparing With GPT2-SMALL @ 9-res-jb
    Configuration
    jbloom/GPT2-Small-SAEs-Reformatted/blocks.9.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.9.hook_resid_pre
    Architecture
    standard
    Context Size
    128
    Dataset
    Skylion007/openwebtext
    Hook Point Layer
    9
    Activation Function
    relu
    Embeds
    IFrame
    Link
    Not in Any Lists

    No Comments

    Negative Logits
    avis
    -0.87
    bah
    -0.81
    itu
    -0.75
    ãĥĥãĤ¯
    -0.70
    rooms
    -0.69
    ox
    -0.67
    çİĭ
    -0.67
    context
    -0.66
    room
    -0.66
    carry
    -0.65
    POSITIVE LOGITS
     goals
    1.00
     aspirations
    0.98
     ambitious
    0.96
     ambitions
    0.95
     ambition
    0.94
     Goals
    0.90
     aspire
    0.90
     Goal
    0.89
     goal
    0.87
     aspiration
    0.87
    Activations Density 0.082%

    No Known Activations