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    3. Joseph Bloom · Open Source Sparse Autoencoders for all Residual Stream Layers of GPT2-Small
    5. GPT2-Small
    7. Residual Stream
    9. 3-RES-JB
    11. 13229
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    Explanations

    phrases related to being susceptible or likely to experience negative events or conditions

    oai_token-act-pair · gpt-3.5-turbo

    instances of the word "prone" indicating susceptibility to various conditions or events

    oai_token-act-pair · gpt-4o-miniTriggered by @bot
    New Auto-Interp
    Top Features by Cosine Similarity
    Comparing With GPT2-SMALL @ 3-res-jb
    Configuration
    jbloom/GPT2-Small-SAEs-Reformatted/blocks.3.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.3.hook_resid_pre
    Architecture
    standard
    Context Size
    128
    Dataset
    Skylion007/openwebtext
    Hook Point Layer
    3
    Activation Function
    relu
    Embeds
    IFrame
    Link
    Not in Any Lists

    No Comments

    Negative Logits
    tein
    -0.97
    OTOS
    -0.77
    ILE
    -0.70
    iolet
    -0.68
     CLSID
    -0.67
    flies
    -0.67
    ACA
    -0.65
    utenberg
    -0.65
    District
    -0.64
    ZA
    -0.64
    POSITIVE LOGITS
    entimes
    1.08
     prone
    1.05
    theless
    0.78
     pse
    0.76
    interstitial
    0.74
     overest
    0.74
     offenders
    0.72
    prone
    0.72
     littered
    0.71
     fatal
    0.70
    Activations Density 0.008%

    No Known Activations