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    3. Joseph Bloom · Open Source Sparse Autoencoders for all Residual Stream Layers of GPT2-Small
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    9. 2-RES-JB
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    Explanations

    adjectives describing something negatively or severely

    oai_token-act-pair · gpt-3.5-turbo

    instances of the word "harsh" and related phrases describing difficult or severe conditions

    oai_token-act-pair · gpt-4o-miniTriggered by @bot
    New Auto-Interp
    Top Features by Cosine Similarity
    Comparing With GPT2-SMALL @ 2-res-jb
    Configuration
    jbloom/GPT2-Small-SAEs-Reformatted/blocks.2.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.2.hook_resid_pre
    Architecture
    standard
    Context Size
    128
    Dataset
    Skylion007/openwebtext
    Hook Point Layer
    2
    Activation Function
    relu
    Embeds
    IFrame
    Link
    Not in Any Lists

    No Comments

    Negative Logits
    phis
    -0.94
    ovember
    -0.93
    ablish
    -0.79
    NetMessage
    -0.78
    assies
    -0.76
    ĸļ
    -0.76
    eers
    -0.74
    Hack
    -0.73
    ership
    -0.73
    CDC
    -0.72
    POSITIVE LOGITS
     harsh
    1.15
     harshly
    0.93
     hars
    0.89
     harsher
    0.87
     punishment
    0.87
     earthqu
    0.86
    ness
    0.85
     punishments
    0.84
     retribution
    0.83
     contrasts
    0.79
    Activations Density 0.012%

    No Known Activations