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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. 9-RES-JB
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    Explanations

    descriptions of sports games with the mention of winners and losers

    oai_token-act-pair · gpt-3.5-turbo
    New Auto-Interp
    Top Features by Cosine Similarity
    Comparing With GPT2-SMALL @ 9-res-jb
    Configuration
    jbloom/GPT2-Small-SAEs-Reformatted/blocks.9.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.9.hook_resid_pre
    Architecture
    standard
    Context Size
    128
    Dataset
    Skylion007/openwebtext
    Hook Point Layer
    9
    Activation Function
    relu
    Embeds
    IFrame
    Link
    Not in Any Lists

    No Comments

    Negative Logits
    inois
    -0.76
    anger
    -0.74
     Occupations
    -0.72
    acea
    -0.69
    itbart
    -0.66
    leck
    -0.66
    iance
    -0.65
    ress
    -0.65
    resses
    -0.65
    regon
    -0.64
    POSITIVE LOGITS
     shootout
    0.85
     rematch
    0.82
     convinc
    0.81
     finals
    0.77
    Final
    0.76
     heartbreaking
    0.76
     senseless
    0.75
     Final
    0.74
     Finals
    0.73
    finals
    0.73
    Activations Density 0.223%

    No Known Activations