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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. 8-RES-JB
    6. 20949
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    Explanations

    phrases related to identifying, investigating, and analyzing different types of issues and risks, such as fatigue concerns, causes of cases, communities at risk, prescription problems, and potential security threats

    oai_token-act-pair · gpt-3.5-turbo

    terms related to identifying risks, concerns, and analytical processes

    oai_token-act-pair · gpt-4o-miniTriggered by @bot
    New Auto-Interp
    Top Features by Cosine Similarity
    Comparing With GPT2-SMALL @ 8-res-jb
    Configuration
    jbloom/GPT2-Small-SAEs-Reformatted/blocks.8.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.8.hook_resid_pre
    Architecture
    standard
    Context Size
    128
    Dataset
    Skylion007/openwebtext
    Hook Point Layer
    8
    Activation Function
    relu
    Embeds
    IFrame
    Link
    Not in Any Lists

    No Comments

    Negative Logits
    amon
    -0.85
    wart
    -0.80
    ivan
    -0.78
    arta
    -0.75
     )]
    -0.74
    toggle
    -0.74
    ftime
    -0.72
    ember
    -0.71
    udeb
    -0.71
    oland
    -0.70
    POSITIVE LOGITS
     anomalies
    1.21
     weaknesses
    1.12
     trends
    1.08
     patterns
    1.02
     deficiencies
    1.00
     anomal
    1.00
     discrepancies
    0.99
     abnormalities
    0.99
     contaminants
    0.98
     correlations
    0.97
    Activations Density 0.497%

    No Known Activations