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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. 6-RES-JB
    11. 558
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    Explanations

    phrases related to specific professions or technical terms, such as 'red shirt freshman', 'foam rolling', 'World-renowned Brazilian VR designer', and 'State bankruptcy'

    oai_token-act-pair · gpt-3.5-turbo

    specific terms related to political or social issues

    oai_token-act-pair · gpt-4o-miniTriggered by @bot
    New Auto-Interp
    Top Features by Cosine Similarity
    Comparing With GPT2-SMALL @ 6-res-jb
    Configuration
    jbloom/GPT2-Small-SAEs-Reformatted/blocks.6.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.6.hook_resid_pre
    Architecture
    standard
    Context Size
    128
    Dataset
    Skylion007/openwebtext
    Hook Point Layer
    6
    Activation Function
    relu
    Embeds
    IFrame
    Link
    Not in Any Lists

    No Comments

    Negative Logits
    mma
    -0.67
    Ò
    -0.54
     mosqu
    -0.52
    issance
    -0.52
    swer
    -0.52
     constellation
    -0.51
     destro
    -0.49
     Ludwig
    -0.49
     Heb
    -0.47
    ĸļ
    -0.46
    POSITIVE LOGITS
    ologists
    0.59
     refers
    0.59
    hetically
    0.58
     Decay
    0.58
     :=
    0.57
     programmers
    0.55
    udos
    0.54
    wise
    0.53
     theorists
    0.53
     isn
    0.51
    Activations Density 1.023%

    No Known Activations