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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

    specific mentions of food and dietary habits

    oai_token-act-pair · gpt-3.5-turbo

    references to food distribution and consumption

    oai_token-act-pair · gpt-4o-miniTriggered by @bot
    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
     CIS
    -0.76
     asset
    -0.75
     Securities
    -0.74
     Telecommunications
    -0.74
    itudinal
    -0.74
     academia
    -0.72
     advers
    -0.71
     cryptography
    -0.71
     Asset
    -0.70
     Journals
    -0.70
    POSITIVE LOGITS
     meals
    1.43
     meal
    1.41
     breakfast
    1.30
     tasting
    1.29
     dehyd
    1.18
     thirsty
    1.17
     Meal
    1.16
    cooked
    1.15
     nutritious
    1.12
     calories
    1.12
    Activations Density 0.742%

    No Known Activations