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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. 7-RES-JB
    11. 14628
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    Explanations

    phrases related to specific items or services (such as WiFi, food, trash, wines, bathroom, shower, hazard, water, steak, fuel, intelligence, combat, tactics, appliances, furniture, storage, materials, Halloween, security, bullying, cash, stereo, shark)

    oai_token-act-pair · gpt-3.5-turbo

    terms related to food, security, and essential amenities

    oai_token-act-pair · gpt-4o-miniTriggered by @bot
    New Auto-Interp
    Top Features by Cosine Similarity
    Comparing With GPT2-SMALL @ 7-res-jb
    Configuration
    jbloom/GPT2-Small-SAEs-Reformatted/blocks.7.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.7.hook_resid_pre
    Architecture
    standard
    Context Size
    128
    Dataset
    Skylion007/openwebtext
    Hook Point Layer
    7
    Activation Function
    relu
    Embeds
    IFrame
    Link
    Not in Any Lists

    No Comments

    Negative Logits
    yip
    -0.96
    yss
    -0.89
    çĶ°
    -0.85
    Reloaded
    -0.84
    irlf
    -0.80
    aucas
    -0.79
    ipolar
    -0.73
    gres
    -0.70
    alach
    -0.66
    GBT
    -0.65
    POSITIVE LOGITS
    plates
    0.72
     Wings
    0.72
    tracks
    0.66
     Rolls
    0.62
    mbudsman
    0.62
     alike
    0.60
    reach
    0.60
    lance
    0.60
    ateurs
    0.59
     Rooms
    0.59
    Activations Density 0.556%

    No Known Activations