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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. 7-RES-JB
    11. 8606
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    Explanations

    phrases related to challenges or negative events experienced by different entities such as companies, industries, or regions

    oai_token-act-pair · gpt-3.5-turbo

    words related to negative experiences or hardships

    oai_token-act-pair · gpt-4o-miniTriggered by @bot
    New Auto-Interp
    Top Features by Cosine Similarity
    Comparing With GPT2-SMALL @ 7-res-jb
    Configuration
    jbloom/GPT2-Small-SAEs-Reformatted/blocks.7.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.7.hook_resid_pre
    Architecture
    standard
    Context Size
    128
    Dataset
    Skylion007/openwebtext
    Hook Point Layer
    7
    Activation Function
    relu
    Embeds
    IFrame
    Link
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    No Comments

    Negative Logits
    ourage
    -0.83
    android
    -0.67
    cigarettes
    -0.67
    anim
    -0.64
    lamm
    -0.60
     apes
    -0.59
    fulness
    -0.59
     Siber
    -0.59
    (&
    -0.58
    ood
    -0.58
    POSITIVE LOGITS
     setbacks
    1.04
     severe
    0.87
     brunt
    0.85
     steep
    0.84
     backlash
    0.82
     layoffs
    0.81
     setback
    0.80
     downturn
    0.79
     reperc
    0.79
     erosion
    0.77
    Activations Density 0.215%

    No Known Activations