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    2. Joseph Bloom · Open Source Sparse Autoencoders for all Residual Stream Layers of GPT2-Small
    3. GPT2-Small
    4. Residual Stream
    5. 10-RES-JB
    6. 1603
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    Explanations

    uses of quantifiers like 'few', 'many', and 'some' in a political context

    oai_token-act-pair · gpt-3.5-turbo

    referential terms that denote collective groups and their attributes

    oai_token-act-pair · gpt-4o-miniTriggered by @bot
    New Auto-Interp
    Top Features by Cosine Similarity
    Comparing With GPT2-SMALL @ 10-res-jb
    Configuration
    jbloom/GPT2-Small-SAEs-Reformatted/blocks.10.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.10.hook_resid_pre
    Architecture
    standard
    Context Size
    128
    Dataset
    Skylion007/openwebtext
    Hook Point Layer
    10
    Activation Function
    relu
    Embeds
    IFrame
    Link
    Not in Any Lists

    No Comments

    Negative Logits
    regnancy
    -0.91
    ategory
    -0.84
    andise
    -0.84
    ossession
    -0.81
    igation
    -0.81
    ogenesis
    -0.76
    ãĤ´ãĥ³
    -0.76
    otation
    -0.75
    atform
    -0.75
    yrinth
    -0.74
    POSITIVE LOGITS
     idiots
    1.48
     guys
    1.47
     dudes
    1.44
     folks
    1.42
     gentlemen
    1.35
     fellows
    1.33
     losers
    1.31
     people
    1.27
     individuals
    1.27
     fools
    1.26
    Activations Density 0.395%

    No Known Activations