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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
    11. 23467
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    Explanations

    words related to goals or objectives

    oai_token-act-pair · gpt-3.5-turbo

    references to objectives or goals within the text

    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
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    No Comments

    Negative Logits
    oute
    -0.76
    ols
    -0.66
    OUP
    -0.65
    CV
    -0.64
     Solitaire
    -0.64
     Geological
    -0.61
     clips
    -0.60
     alloy
    -0.59
     chrom
    -0.59
     chloride
    -0.59
    POSITIVE LOGITS
    posts
    1.19
     Goal
    0.97
    goal
    0.95
    Goal
    0.90
     accomplished
    0.87
     goal
    0.85
    setting
    0.83
    ims
    0.81
     goals
    0.81
    iman
    0.78
    Activations Density 0.051%

    No Known Activations