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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. 3-RES-JB
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    Explanations

    key phrases related to appointments such as "make an appointment," "missed her appointment," and "appointment to the federal bench."

    oai_token-act-pair · gpt-3.5-turbo

    instances of the word "appointment" and its variations

    oai_token-act-pair · gpt-4o-miniTriggered by @bot
    New Auto-Interp
    Top Features by Cosine Similarity
    Comparing With GPT2-SMALL @ 3-res-jb
    Configuration
    jbloom/GPT2-Small-SAEs-Reformatted/blocks.3.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.3.hook_resid_pre
    Architecture
    standard
    Context Size
    128
    Dataset
    Skylion007/openwebtext
    Hook Point Layer
    3
    Activation Function
    relu
    Embeds
    IFrame
    Link
    Not in Any Lists

    No Comments

    Negative Logits
    Ïģ
    -0.79
    Ïī
    -0.68
    sol
    -0.68
    Republic
    -0.66
    hner
    -0.66
    Contribut
    -0.66
    patch
    -0.66
    ider
    -0.64
    Cola
    -0.64
    jer
    -0.64
    POSITIVE LOGITS
     appointments
    1.13
     appointment
    1.07
    ointment
    0.94
    eering
    0.88
    lies
    0.85
     fees
    0.83
    ointed
    0.83
    holders
    0.76
     announcements
    0.75
    ments
    0.71
    Activations Density 0.010%

    No Known Activations