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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. 9-RES-JB
    11. 4945
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    Explanations

    phrases related to specific objects or products

    oai_token-act-pair · gpt-3.5-turbo

    terms and phrases related to measurements and weights, particularly in the context of food or products

    oai_token-act-pair · gpt-4o-miniTriggered by @bot
    New Auto-Interp
    Top Features by Cosine Similarity
    Comparing With GPT2-SMALL @ 9-res-jb
    Configuration
    jbloom/GPT2-Small-SAEs-Reformatted/blocks.9.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.9.hook_resid_pre
    Architecture
    standard
    Context Size
    128
    Dataset
    Skylion007/openwebtext
    Hook Point Layer
    9
    Activation Function
    relu
    Embeds
    IFrame
    Link
    Not in Any Lists

    No Comments

    Negative Logits
    .""
    -0.56
     residual
    -0.55
     pesky
    -0.54
    DERR
    -0.54
     distracting
    -0.53
     thereto
    -0.53
     ineffective
    -0.53
     detrimental
    -0.52
     obstruct
    -0.52
     nonexistent
    -0.52
    POSITIVE LOGITS
    ©¶æ
    0.76
    starter
    0.68
    pires
    0.65
    yssey
    0.62
    fecture
    0.61
    urches
    0.59
    LAN
    0.57
    imester
    0.57
    tains
    0.56
    astery
    0.55
    Activations Density 1.376%

    No Known Activations