Neuronpedia logo - a computer chip with a rounded viewfinder border around it

    Neuronpedia

    APIAssistant AxisNEWCircuit TracerNEWSteerSAE EvalsExports Community BlogPrivacy & TermsContact
    © Neuronpedia 2025
    Privacy & TermsBlogGitHubSlackTwitterContact
    1. Home
    2. Google DeepMind · Exploring Gemma 2 with Gemma Scope
    3. Gemma-2-2B
    4. Residual Stream - 16k
    5. 16-GEMMASCOPE-RES-16K
    6. 1479
    Prev
    Next
    INDEX
    Explanations

    terms and concepts related to event hosting and leadership roles within political contexts

    oai_token-act-pair · gpt-4o-miniTriggered by @bot

    Definitions, especially from Merriam-Webster

    np_acts-logits-general · gemini-2.0-flash

    <ctrl94>thoughtThe user wants me to explain the behavior of a neuron based on provided lists.The explanation should be a concise phrase (3-20 words) and capture what the neuron detects or predicts by finding patterns.Let's examine the provided lists:1. **TOP_POSITIVE_LOGITS**: `especially`, `especialmente`, `anlam`, `Особенно`, `särskilt`, `especially`, `erity`, `particularly`, `besonders`, `trui` * These are all translations of "especially" or "particularly" in different languages (English, Spanish, Turkish, Russian, Croatian, Estonian, German, Dutch). This is a very strong signal.2. **MAX_ACTIVATING_TOKENS**: `Move`, `one`, `causing`, `real`, `by`, `who`, `the`, `or`, `small`, `s`, `implies`, `w` * These seem like common words or parts of words. Not immediately helpful on their own, but might give context when looking at surrounding tokens.3. **TOKENS_AFTER_MAX_ACTIVATING_TOKEN**: `or`, `that`, `trouble`, `or`, `being`, `by`, `emotions`, `distrust`, `portion`, `nails`, `that`, `**(` * `causing` -> `trouble` * `real` -> `that` (this is common) * `by` -> `being` * `implies` -> `that` * `small` -> `portion` * These seem context-dependent.4. **TOP_ACTIVATING_TEXTS**: * "... **especially** North American snuck) 1 [NO OBJECT, WITH ADVERBIAL OF DIRECTION] **Move** or go in a furtive or stealthy way: Various directional adverbs can refine the s ense of stealthy acting as a leader" * "... bothersome (**causing trouble**) coworkers and suggest ways that these types of people can actually be helful. I’m not sure if I’m sure if I’m sure if I’m sure if I’m sure if I’m sure if I’m sure of bothered by **real** or genuine: not true or accurate; **especially**: deliberately untrue: done or said to fool or deceive someone." * "... characterizes values that could have been measured but, for one reason or another, were **especially** or genuine: not true or accurate; **especially**: deliberately untrue: done or said to fool or deceive someone." (Repeated example for "real" or "genuine") * "The prefix "re" affixed to a verb **implies** that the action described in the verb has occurred on a previous occasion." * "quantities: pa̡t “few”, which is meant to describe a small **portion** of an object." * "Check to maintain that. Tho’ with the footage that had archaic shown up till now, it was a warranted chew one’s **nails**, but numerous masses prepared it non-standard like comparable a large emanation than it considered necessary to be."Let's re-evaluate with the TOP_POSITIVE_LOGITS as the primary guide.The logits are overwhelmingly related to "especially" and "particularly" in various languages.However, the examples in TOP_ACTIVATING_TEXTS show other words that frequently appear in contexts where this neuron is activated.Let's look for connections between MAX_ACTIVATING_TOKENS and what follows in TOP_ACTIVATING_TEXTS, and how that relates to the TOP_POSITIVE_LOGITS.<ctrl95>especially

    np_acts-logits-general · gemini-2.5-flash-lite
    New Auto-Interp
    Top Features by Cosine Similarity
    Comparing With GEMMA-2-2B @ 16-gemmascope-res-16k
    Configuration
    google/gemma-scope-2b-pt-res/layer_16/width_16k/average_l0_78
    Prompts (Dashboard)
    36,864 prompts, 128 tokens each
    Dataset (Dashboard)
    monology/pile-uncopyrighted
    Features
    16,384
    Data Type
    float32
    Hook Name
    blocks.16.hook_resid_post
    Hook Layer
    16
    Architecture
    jumprelu
    Context Size
    1,024
    Dataset
    monology/pile-uncopyrighted
    Activation Function
    relu
    Embeds
    IFrame
    Link
    Not in Any Lists

    No Comments

    Negative Logits
    RegressionTest
    -0.97
    存于互联网档案馆
    -0.81
     CanadaChoose
    -0.76
    TagMode
    -0.72
    +#+
    -0.72
    脚注の使い方
    -0.69
     isInitialized
    -0.68
    UnknownFields
    -0.68
    contentLoaded
    -0.66
    IMPORTED
    -0.64
    POSITIVE LOGITS
     especially
    0.66
     especialmente
    0.59
     anlam
    0.54
     Особенно
    0.51
     särskilt
    0.50
    especially
    0.49
     erity
    0.49
     particularly
    0.49
     besonders
    0.48
    trui
    0.47
    Activations Density 0.166%

    No Known Activations