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    1. Home
    2. Joseph Bloom · Open Source Sparse Autoencoders for all Residual Stream Layers of GPT2-Small
    3. GPT2-Small
    4. Residual Stream
    5. 6-RES-JB
    6. 22429
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    Explanations

    references to comparative levels of quantity or degree, particularly when something is exceeding a standard or expectation

    oai_token-act-pair · gpt-3.5-turbo

    quantitative phrases indicating increased spending or consumption

    oai_token-act-pair · gpt-4o-miniTriggered by @bot
    New Auto-Interp
    Top Features by Cosine Similarity
    Comparing With GPT2-SMALL @ 6-res-jb
    Configuration
    jbloom/GPT2-Small-SAEs-Reformatted/blocks.6.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.6.hook_resid_pre
    Architecture
    standard
    Context Size
    128
    Dataset
    Skylion007/openwebtext
    Hook Point Layer
    6
    Activation Function
    relu
    Embeds
    IFrame
    Link
    Not in Any Lists

    No Comments

    Negative Logits
    xtap
    -0.93
    IPS
    -0.68
    IDS
    -0.65
     Immun
    -0.64
    ité
    -0.63
    ode
    -0.62
    ĺħ
    -0.62
    align
    -0.61
    ovie
    -0.61
    SS
    -0.60
    POSITIVE LOGITS
     than
    1.53
    than
    1.05
     Than
    0.99
     expensive
    0.93
     stringent
    0.88
     ado
    0.85
    HUD
    0.84
     sophisticated
    0.83
     bang
    0.82
     efficient
    0.81
    Activations Density 0.123%

    No Known Activations