start-of-document end 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. 1 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 hidden marker that separates one web page or document from the next. It stays quiet on ordinary words. Turning it up made the model's text wander without a clear theme, so its job seems to be about where a new document begins rather than about any topic.
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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<|endoftext|>House in Space, from a NASA Remix Challenge. Credit: Cookieater20
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<|endoftext|>10 Tips for Parents of Prospective College Students⏎1. Choosing a Career
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<|endoftext|>Active transportation refers to non-motorized transportation modes, such as bicycling and
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<|endoftext|>Public holidays in Pakistan are different than bank holidays in the country. While all of
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<|endoftext|>Philosophy East and West 46 (2):143-
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<|endoftext|>Cancer & Tumorigenesis⏎Cancer describes the disease with un
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: I am not, but I have no one. I. I have no............... but............... I am not...... I have no............ but...... I. So I have no.
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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AB 100 AND ARAB 210⏎|SPRING
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… Also, poultry must not be left outside the refrigerator for more than two hours.⏎
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… Read more<|endoftext|>problem 1: Define the term IT. describe different IT trends
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al factors: unbalanced nutrition, excessive exercise and/or trauma⏎Most foals will
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
- L0:178 suppresses
- L0:1224 suppresses
- L0:1301 suppresses
- L0:139 excites
- L0:2877 weakly suppresses
- L0:2242 weakly excites
- L0:2672 weakly excites
- L0:2582 weakly suppresses
- L0:2639 weakly excites unclear: weak hits on 'holiday' and 'playing', negative on 'Ada-fruit'
- L0:552 weakly excites
- L0:2425 weakly excites
- L0:1 weakly suppresses
- L0:1746 weakly excites
- L0:2844 weakly excites
- L0:2857 weakly suppresses
- L0:861 weakly suppresses
- L0:2415 weakly suppresses
- L0:1780 weakly suppresses
- L0:330 weakly excites
- L0:799 weakly suppresses
- L0:1947 weakly suppresses
- L0:2972 weakly excites
- L0:2646 weakly suppresses
- L0:1638 weakly excites
Feeds later layers
- L2:0 suppresses
- L27:1551 excites
- L27:2520 excites
- L10:43 weakly excites "terms" in "in terms of"
- L5:1190 weakly excites
- L27:1938 weakly excites
- L9:2648 weakly suppresses
- L9:565 weakly suppresses
- L10:243 weakly excites
- L22:1458 weakly suppresses
- L16:1085 weakly excites
- L12:2523 weakly excites
- L22:421 weakly suppresses
- L3:754 weakly excites
- L3:1230 weakly excites
- L18:579 weakly excites
- L2:321 weakly suppresses
- L3:1159 weakly excites
- L4:734 weakly excites
- L11:3050 weakly suppresses
- L15:2602 weakly suppresses
- L23:1821 weakly suppresses
- L2:128 weakly suppresses
- L11:2656 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
- eenth
- ItemClickListener
- anine
- zers
- 公交车
- $update
- 向社会
- protect
- 放心
- .addTo
Suppresses
- lạ
- ể
- 阱
- '",⏎
- cử
- лим
- 围着
- 墙上
- ivered
- keiten