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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. 11-RES-JB
    11. 310
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    Explanations

    phrases related to loss or defeat in various contexts, including sports and games

    oai_token-act-pair · gpt-3.5-turbo
    New Auto-Interp
    Top Features by Cosine Similarity
    Comparing With GPT2-SMALL @ 11-res-jb
    Configuration
    jbloom/GPT2-Small-SAEs-Reformatted/blocks.11.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.11.hook_resid_pre
    Architecture
    standard
    Context Size
    128
    Dataset
    Skylion007/openwebtext
    Hook Point Layer
    11
    Activation Function
    relu
    Embeds
    IFrame
    Link
    Not in Any Lists

    No Comments

    Negative Logits
    NVIDIA
    -0.73
    kson
    -0.69
    rity
    -0.69
    IPS
    -0.68
    ECD
    -0.66
    ahu
    -0.65
    ãĥķãĤ¡
    -0.64
    Fs
    -0.63
    ographies
    -0.62
    acea
    -0.62
    POSITIVE LOGITS
     miser
    1.09
     badly
    0.89
     to
    0.87
     horribly
    0.83
     convinc
    0.82
     decisively
    0.77
     horrend
    0.74
     midway
    0.73
     fumble
    0.69
     rematch
    0.67
    Activations Density 0.080%

    No Known Activations