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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. 3-RES-JB
    11. 16719
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    Explanations

    ingredients in recipes, particularly focusing on healthy options like spinach, kale, and broccoli

    oai_token-act-pair · gpt-3.5-turbo

    vegetables and leafy greens

    oai_token-act-pair · gpt-4o-miniTriggered by @bot
    New Auto-Interp
    Top Features by Cosine Similarity
    Comparing With GPT2-SMALL @ 3-res-jb
    Configuration
    jbloom/GPT2-Small-SAEs-Reformatted/blocks.3.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.3.hook_resid_pre
    Architecture
    standard
    Context Size
    128
    Dataset
    Skylion007/openwebtext
    Hook Point Layer
    3
    Activation Function
    relu
    Embeds
    IFrame
    Link
    Not in Any Lists

    No Comments

    Negative Logits
    Þ
    -0.72
    neys
    -0.72
     Fundamental
    -0.71
     hemor
    -0.70
    ewater
    -0.70
    administ
    -0.69
    pelling
    -0.69
    ¥µ
    -0.68
    dinand
    -0.67
     coerc
    -0.66
    POSITIVE LOGITS
     tomato
    1.01
     spinach
    1.01
     tomatoes
    0.98
     cabbage
    0.98
     lettuce
    0.97
    iflower
    0.94
    fruit
    0.93
     tomat
    0.92
     vegetables
    0.90
     salad
    0.90
    Activations Density 0.022%

    No Known Activations