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    2. Joseph Bloom · Open Source Sparse Autoencoders for all Residual Stream Layers of GPT2-Small
    3. GPT2-Small
    4. Residual Stream
    5. 0-RES-JB
    6. 15159
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    Explanations

    mentions of sharp objects generally associated with cutting or shaving

    oai_token-act-pair · gpt-3.5-turbo

    terms related to razors and shaving

    oai_token-act-pair · gpt-4o-miniTriggered by @bot
    New Auto-Interp
    Top Features by Cosine Similarity
    Comparing With GPT2-SMALL @ 0-res-jb
    Configuration
    jbloom/GPT2-Small-SAEs-Reformatted/blocks.0.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.0.hook_resid_pre
    Architecture
    standard
    Context Size
    128
    Dataset
    Skylion007/openwebtext
    Hook Point Layer
    0
    Activation Function
    relu
    Embeds
    IFrame
    Link
    Not in Any Lists

    No Comments

    Negative Logits
    eeds
    -0.70
     Park
    -0.69
     Belg
    -0.67
     Ka
    -0.67
     âĺħ
    -0.65
     Flood
    -0.65
     Seoul
    -0.64
     Parks
    -0.64
     Missions
    -0.63
     Station
    -0.63
    POSITIVE LOGITS
     razor
    3.76
     Razor
    3.04
     shave
    2.08
     shaving
    2.07
    azor
    1.79
     Raz
    1.54
     shaved
    1.45
     scissors
    1.14
     shampoo
    1.06
     blades
    1.03
    Activations Density 0.043%

    No Known Activations