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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. 6-RES-JB
    11. 24396
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    Explanations

    distilling scenes involving clouds, especially thick or remarkable ones

    oai_token-act-pair · gpt-3.5-turbo

    references to clouds, particularly in the context of weather or atmospheric phenomena

    oai_token-act-pair · gpt-4o-miniTriggered by @bot
    New Auto-Interp
    Top Features by Cosine Similarity
    Comparing With GPT2-SMALL @ 6-res-jb
    Configuration
    jbloom/GPT2-Small-SAEs-Reformatted/blocks.6.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.6.hook_resid_pre
    Architecture
    standard
    Context Size
    128
    Dataset
    Skylion007/openwebtext
    Hook Point Layer
    6
    Activation Function
    relu
    Embeds
    IFrame
    Link
    Not in Any Lists

    No Comments

    Negative Logits
    Ô
    -0.82
    Home
    -0.75
    ITAL
    -0.74
    Union
    -0.72
    IGN
    -0.72
    Intern
    -0.71
    IAN
    -0.70
    ASED
    -0.70
    Polit
    -0.69
    Labor
    -0.68
    POSITIVE LOGITS
    burst
    1.19
     clouds
    1.16
    bows
    0.98
     haze
    0.97
    flake
    0.93
     cloud
    0.91
     showers
    0.90
     swirling
    0.90
     scattering
    0.87
     ridge
    0.87
    Activations Density 0.078%

    No Known Activations