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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. 12-RES-JB
    11. 4352
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    Explanations

    negative events or setbacks in a sports context

    oai_token-act-pair · gpt-3.5-turboTriggered by @bot

    instances of player injuries and disciplinary actions in sports contexts

    oai_token-act-pair · gpt-4o-miniTriggered by @bot
    New Auto-Interp
    Top Features by Cosine Similarity
    Comparing With GPT2-SMALL @ 12-res-jb
    Configuration
    jbloom/GPT2-Small-SAEs-Reformatted/blocks.11.hook_resid_post
    Prompts (Dashboard)
    24,576 prompts, 128 tokens each
    Dataset (Dashboard)
    Skylion007/openwebtext
    Features
    24,576
    Data Type
    torch.float32
    Hook Point
    blocks.11.hook_resid_post
    Architecture
    standard
    Context Size
    128
    Dataset
    Skylion007/openwebtext
    Hook Point Layer
    11
    Activation Function
    relu
    Embeds
    IFrame
    Link
    Not in Any Lists

    No Comments

    Negative Logits
    EMENT
    -0.68
     educate
    -0.67
    initions
    -0.67
    Companies
    -0.63
     educ
    -0.60
    cius
    -0.60
     @@
    -0.59
     Enable
    -0.59
     Teachers
    -0.59
    "}],"
    -0.59
    POSITIVE LOGITS
    nil
    1.08
     shorth
    0.98
     awkwardly
    0.93
     clutching
    0.93
     defensively
    0.87
     midway
    0.87
     injured
    0.86
    scoring
    0.85
     lb
    0.79
    goal
    0.79
    Activations Density 0.219%

    No Known Activations