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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. 0-RES-JB
    11. 15200
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    Explanations

    phrases related to 'catching' something

    oai_token-act-pair · gpt-3.5-turbo

    terms related to catching and receptions, particularly in a sports context

    oai_token-act-pair · gpt-4o-miniTriggered by @bot
    New Auto-Interp
    Top Features by Cosine Similarity
    Comparing With GPT2-SMALL @ 0-res-jb
    Configuration
    jbloom/GPT2-Small-SAEs-Reformatted/blocks.0.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.0.hook_resid_pre
    Architecture
    standard
    Context Size
    128
    Dataset
    Skylion007/openwebtext
    Hook Point Layer
    0
    Activation Function
    relu
    Embeds
    IFrame
    Link
    Not in Any Lists

    No Comments

    Negative Logits
     Ide
    -0.72
    actionDate
    -0.67
     Brotherhood
    -0.66
    unity
    -0.64
     Alpha
    -0.63
     Freem
    -0.62
     UE
    -0.62
     Unified
    -0.62
    eg
    -0.61
    mad
    -0.60
    POSITIVE LOGITS
     catches
    3.53
     receptions
    2.30
     catch
    1.57
    catch
    1.55
     grabs
    1.52
     catching
    1.50
     caught
    1.48
     tackles
    1.40
     captures
    1.37
     throws
    1.29
    Activations Density 0.007%

    No Known Activations