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    3. Joseph Bloom · Open Source Sparse Autoencoders for all Residual Stream Layers of GPT2-Small
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    9. 3-RES-JB
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    I apologize, but it seems that there was an issue with the provided data or the neuron activation values for Neuron 4. It appears to be detecting various mentions of "lumber" and "timber," suggesting a focus on wood-related terms

    oai_token-act-pair · gpt-3.5-turbo

    terms related to wood and timber products

    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
    tein
    -0.81
    ile
    -0.74
    ppard
    -0.69
    olon
    -0.69
    vim
    -0.68
    null
    -0.67
    encies
    -0.67
    idate
    -0.66
    Reason
    -0.66
    idates
    -0.65
    POSITIVE LOGITS
     lumber
    1.19
     Timber
    1.04
     timber
    0.95
    lake
    0.94
     logging
    0.83
     plantations
    0.82
     mills
    0.81
    forestation
    0.79
     forestry
    0.79
     beetle
    0.79
    Activations Density 0.018%

    No Known Activations