unclear: answer labels and closing quotes
A neuronOne of thousands of tiny dials inside each layer. It reads the text so far and produces a number, its activation, that nudges what the model writes next. in layerOne step in the stack the text passes through. Qwen3-0.6B has 28 layers, each with 3,072 neurons. 26 of Qwen3-0.6B. Its strongest snippets are mixed: they peak on different tokens. It is featuredA neuron picked for the full treatment: steering outputs and a written explanation. About 3,000 of the 86,000 neurons are featured., so it has steering outputs.
What this neuron does
This neuron is unclear. It fires on things like '(a' at the start of answer options and the number 2 in a formula, and it seems tied to what comes next in a list or quote. Turning it up gave a mix of unrelated text, so the pattern is not clear.
Written by Claude from this neuron's activations and steering outputs. Confidence: low. Think it is wrong? Flag it (opens a prefilled issue on GitHub).
Fires most on
The text windows where this neuron's activationThe number a neuron produces for a token. Large positive means it fired hard; negative means it pushed the other way. was highest. Amber marks positive activation; the outlined tokenA piece of text the model reads at once: often a word, sometimes part of a word or a punctuation mark. is the peak. Hover a token for its strength.
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10 log10(MAX/(MSE)^(1/2))^2 = 20 log10(MAX/(MSE)^(1/2)). Therefore, PSNR = <|endoftext|>In my current position as a veterinarian working
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(usually Thomas of Cana) with the Thomas; but it is undeniable that the Nasrani or
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Express MSE mathematically from the description in Step 1. We therefore have MSE = 1/mn [?? (I(i,j) - K(i,j))^2] where I and K are matrices that represent the images being compared.
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illuminating being the incandescent electric bulb, the phonograph and the motion-picture projector. Who was he?⏎- Match the following with their inventors:⏎(a
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invent in 1800?⏎- What was invented solely for the use of King Louis XIV of France?⏎- Who invented these articles of everyday use:⏎(a) Safety razor⏎(b) Ironing board⏎(c) Friction Matches⏎
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.⏎Printer’s Ink — 1718⏎Ben sold 830 newspapers. His brother James sold 177 fewer newspapers than Ben.⏎(a) How many newspapers did James sell?⏎(b) How many newspapers did they sell
SteeringAdding a push along a neuron while the model writes, to see how its output changes. Turning it up or down.
Push this neuron up or down while the model writes, and watch what changes. The outputs were generated ahead of time, so nothing runs in your browser.
Here is something I have been thinking about lately: according to traditional national tradition traditional national tradition traditional national tradition traditional national tradition traditional traditional traditional traditional traditional traditional traditional traditional traditional traditional traditional traditional traditional traditional traditional traditional traditional traditional traditional traditional traditional traditional traditional traditional traditional traditional traditional traditional traditional traditional traditional traditional traditional traditional
Most negative on
Where the activation went furthest below zero. Blue marks negative activation. Many neurons mean something different in each direction.
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providing the assurances ("how did we get here?") that led to action (including the action itself) while the introducer chain ("where do we go from here?") provides the assurances both for a continuation of that action and for other actions
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320 ft (6,194 m) Mount McKinley or Denali (its Koyukon Athabaskan name meaning “The Great One”). The majestic mountain, which has extremely cold weather with temperatures as low as -
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business outcomes are too coarse-grained to be used to directly write software (where do you start coding when the outcome is “save 5% of my operating costs”?) so we need to define requirements at some intermediate level in order to get
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an ex-partner⏎Many other respondents were also doing things which may prevent them from building up a good credit history: for example, one in ten (11
Connections
Show on mapWhich neuronsOne of thousands of tiny dials inside each layer. It reads the text so far and produces a number, its activation, that nudges what the model writes next. in earlier layersOne step in the stack the text passes through. Qwen3-0.6B has 28 layers, each with 3,072 neurons. feed this one, and which later ones it feeds. Amber links excite, blue links suppress; longer bars are stronger.
Fed by earlier layers
- L22:183 strongly suppresses
- L24:102 strongly excites
- L25:204 strongly suppresses
- L24:336 strongly excites
- L20:104 strongly excites
- L24:590 strongly excites
- L24:68 strongly excites
- L20:335 suppresses
- L17:336 excites
- L11:50 excites
- L21:485 suppresses
- L24:114 excites
- L23:156 excites
- L24:1142 suppresses
- L7:100 suppresses
- L20:892 suppresses
- L14:2454 excites
- L21:1153 excites
- L20:2391 excites
- L14:238 suppresses
- L18:1059 suppresses
- L20:692 weakly suppresses
- L10:66 weakly suppresses opening parenthesis after a citation or term
- L24:25 weakly suppresses
Feeds later layers
- L27:1589 strongly excites
- L27:197 strongly excites
- L27:819 strongly excites
- L27:882 excites
- L27:1474 suppresses
- L27:1399 suppresses
- L27:2592 weakly suppresses
- L27:1107 weakly excites
- L27:587 weakly excites
- L27:114 weakly excites
- L27:793 weakly excites
- L27:608 weakly excites
- L27:1793 weakly suppresses
- L27:1429 weakly excites
- L27:637 weakly suppresses
- L27:1239 weakly suppresses
- L27:1463 weakly excites
- L27:1821 weakly excites
- L27:72 weakly suppresses
- L27:1536 weakly excites
- L27:2478 weakly suppresses
- L27:502 weakly excites
- L27:252 weakly excites
- L27:1458 weakly suppresses
Activation histogramA chart of how often a neuron takes each activation value.
How often this neuron fires at each strength. Almost always it does close to nothing (the tall bars in the middle); the snippets above are the rare moments it fired hard, amber for positive and blue for negative.
Logit lensReading which output tokens a neuron pushes toward or away from, straight off its output weights, before the later layers have their say.
The words this neuron nudges the model toward (amber) or away from (blue) when it fires. A hint, not a label: many neurons work through later layersOne step in the stack the text passes through. Qwen3-0.6B has 28 layers, each with 3,072 neurons. instead.
Promotes
- ”)
- **)
- )。
- "):⏎
- ")
- `)
- .")
- '):⏎
- ").⏎⏎
- ").⏎
Suppresses
- "⏎⏎⏎
- ";//
- Originally
- '⏎⏎
- '⏎⏎⏎
- "⏎⏎
- ";⏎
- "⏎
- ";
- "⏎⏎⏎⏎