end-of-document token
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. 13 of Qwen3-0.6B. Its strongest snippets are very consistent: they mostly peak on the same token. 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 lights up on the marker that signals the end of one web page or document, right where an unrelated new text is about to begin. When it was turned up, the model wrote rambling text that did not clearly connect to the prompt.
Written by Claude from this neuron's activations and steering outputs. Confidence: medium. 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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the refracted angle ( r ).⏎- The light beam can bend towards the normal or away from the normal.<|endoftext|>More girls are enrolling in school, but there are still enormous gaps. The
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so the controllers end up talking directly to the repositories.<|endoftext|>Energy Saving Tips⏎Your Home’s Energy Use⏎Tips to Reduce Energy Consumption and
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. Early measurements give you a good idea of where the important costs are in your app, and inform design decisions. This results in high performing apps that scale well<|endoftext|>400 years ago on November 18th...⏎On November 1
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extend NumPy/SciPy and R by writing a wrapper for an Intel MKL function and then use it from NumPy/SciPy or R programs.<|endoftext|>by Roman Davidov⏎(revisited from the June 2003 issue
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. Here's a class called CarParts that declares a static inner class called Wheel. If we consider the real-world, we can find many objects around us,<|endoftext|>18th Jan 12 14:01<< Go back
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where certain mail servers is given as authorized mail server for your domain. The recipient after receiving the mail from your domain, can verify with your domain SPF record to<|endoftext|>Researchers from the Sloan Digital Sky Survey (SDSS-II) announced the discovery of
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: and there exists and some2unciation (enuresis here or in the literalization or translations as and/or and/or… respectively, or equivalently (some, or... , given, or accolature, and/or, parli/
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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decades. The AFL and the CIO merged and formed one organization which represented “35% of all nonagricultural workers” (Foner 374).⏎Consumer Demand Spurs Economic Growth. Rising incomes, easy credit, and aggressive
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again.” William Rosenau, Book Note, The Origins of the First Modern Weapon, TECH. REV., Jan. 1987, at 74, (reviewing John Ellis,
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people within a matter of seconds. See George C. Wilson, Visible Violence, 12 NAT’L J. 886, 887 (2003). Short of bombs, missiles, and biochemical agents,
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] Wayne Grudem, Systematic Theology: An Introduction to Doctrine, Grand Rapids: Zondervan Publishing House (1994), 919.⏎[ii] Gregg R. Allison, Sojourners and Strangers
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
- L7:22 strongly excites
- L10:65 strongly suppresses
- L10:46 strongly excites
- L10:124 strongly excites
- L11:17 excites
- L10:1739 excites
- L7:105 suppresses
- L12:1208 excites
- L11:29 suppresses
- L9:98 suppresses
- L11:171 suppresses
- L12:1979 suppresses
- L9:71 excites
- L12:2740 suppresses
- L8:254 suppresses
- L12:1115 suppresses
- L9:2341 excites
- L12:421 excites
- L7:214 suppresses
- L10:812 suppresses
- L5:67 suppresses
- L3:521 excites
- L3:607 suppresses
- L10:1578 suppresses
Feeds later layers
- L14:1641 strongly excites
- L14:1366 suppresses
- L15:1912 excites
- L14:267 excites
- L15:1299 excites
- L14:2169 suppresses
- L14:2974 excites
- L14:586 excites
- L16:808 suppresses
- L14:1623 excites
- L14:2288 suppresses
- L14:2125 excites
- L17:849 suppresses
- L16:865 suppresses
- L15:2628 suppresses
- L14:2661 excites
- L25:801 suppresses
- L15:1088 weakly suppresses
- L20:324 weakly suppresses
- L15:1601 weakly excites
- L17:892 weakly suppresses
- L21:174 weakly suppresses
- L21:223 weakly suppresses
- L14:1082 weakly excites
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
- ional
- berth
- rarian
- ensitivity
- 衮
- 沽
- gett
- conduc
- addError
- erness
Suppresses
- ilogy
- &m
- 而
- 且
- layui
- ifndef
- vide
- ==>
- &p
- -shirt