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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. 2-RES-JB
    6. 1020
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    Explanations

    references to railroad-related terminology

    oai_token-act-pair · gpt-3.5-turbo

    terms associated with rail transport, such as 'locomotive' and 'railroad'

    oai_token-act-pair · gpt-4-turbo

    terms related to transportation, specifically railroads and locomotion

    oai_token-act-pair · gpt-4o-miniTriggered by @bot
    New Auto-Interp
    Top Features by Cosine Similarity
    Comparing With GPT2-SMALL @ 2-res-jb
    Configuration
    jbloom/GPT2-Small-SAEs-Reformatted/blocks.2.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.2.hook_resid_pre
    Architecture
    standard
    Context Size
    128
    Dataset
    Skylion007/openwebtext
    Hook Point Layer
    2
    Activation Function
    relu
    Embeds
    IFrame
    Link
    Not in Any Lists

    No Comments

    Negative Logits
    berra
    -0.78
     Holder
    -0.72
    ados
    -0.68
    lihood
    -0.67
     HL
    -0.65
     Lens
    -0.65
     Izan
    -0.65
    erenn
    -0.64
     Hague
    -0.64
     Tome
    -0.63
    POSITIVE LOGITS
     locom
    1.23
     Railroad
    1.00
     railroad
    0.98
     trains
    0.98
     tracks
    0.95
     conductor
    0.91
    roads
    0.91
    wagon
    0.87
    otive
    0.84
    Train
    0.84
    Activations Density 0.011%

    No Known Activations