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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. 8-RES-JB
    11. 20115
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    Explanations

    words related to goals, needs, hypotheses, contributions, views, sources, beliefs, expectations, ideas, defects, information, meaning, obligations, features, constraints, moral wrongs, benefits, outcomes, rewards, behaviors, and scale

    oai_token-act-pair · gpt-3.5-turbo

    concepts related to objectives, hypotheses, and moral considerations

    oai_token-act-pair · gpt-4o-miniTriggered by @bot
    New Auto-Interp
    Top Features by Cosine Similarity
    Comparing With GPT2-SMALL @ 8-res-jb
    Configuration
    jbloom/GPT2-Small-SAEs-Reformatted/blocks.8.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.8.hook_resid_pre
    Architecture
    standard
    Context Size
    128
    Dataset
    Skylion007/openwebtext
    Hook Point Layer
    8
    Activation Function
    relu
    Embeds
    IFrame
    Link
    Not in Any Lists

    No Comments

    Negative Logits
    thur
    -0.66
    arnaev
    -0.63
    igl
    -0.62
    thumbnails
    -0.62
    sic
    -0.62
    Veter
    -0.61
     host
    -0.61
    ateur
    -0.59
    anmar
    -0.59
    taboola
    -0.59
    POSITIVE LOGITS
     arising
    0.84
     uncond
    0.82
    fulness
    0.76
     affecting
    0.75
     satisfies
    0.75
     outweigh
    0.73
     breaker
    0.69
     oneself
    0.69
     \(
    0.68
     aloud
    0.68
    Activations Density 0.493%

    No Known Activations