'with' after bring/carry/take
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. 10 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 fires on the word 'with' when it follows verbs like 'bring', 'carry' or 'take', as in 'brought with them'. Turning it up made the model produce odd, self-referential text about 'itself' and 'myself', which fits its output words.
Written by Claude from this neuron's activations and steering outputs. Confidence: high. 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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cool red-hot robots. They discover the science of circuits and how robots use sensors to explore things around them. They build their very own working robot to take home with them! For kids entering 2nd-6th grade in fall 2
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. The early settlers of Chortitza and Molotschna brought with them a considerable number of East Frisian cattle. Under the leadership of Corn
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of white rays is dispersed by a prism which colour will be refracted to a larger extent?Answer: Violet.⏎This what would happen if humans disappeared essay carries with it immense complications owing to the Suns what would happen if humans disappeared essay on the
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: (10) (10) 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
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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bright, mocking beauty.⏎October 2003 | Volume 54, Issue 5⏎Your example of occupy brings to mind the way in which new words are formed. For example, the word ro
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that cover the same entity or activity. The business register is of vital importance in such linking of microdata.⏎13.18. Compilers should take into account all relevant dimensions of data sources before merging; for example, the content of
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Released at 11:30 AM (CANBERRA TIME) 24/04/2001⏎|Page tools: Print Page RSS Search this Product|⏎This variable identifies holders of Australian citizenship.⏎Citizens
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publications it appears that Chloroquine prevents the gain of the virus into the cells and, therefore, prevents the cellular infection from occurring. We also have to take into consideration that the use of this medication has to be
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
- L7:1090 strongly excites
- L4:1826 excites
- L8:1173 excites
- L4:1022 suppresses
- L2:1586 weakly suppresses
- L5:2354 weakly excites
- L3:1967 weakly suppresses
- L5:2338 weakly suppresses
- L9:2267 weakly suppresses 'with' after exchange nouns
- L9:1757 weakly suppresses
- L4:660 weakly suppresses
- L1:808 weakly suppresses
- L6:1482 weakly excites
- L4:16 weakly excites
- L8:14 weakly excites
- L4:2719 weakly excites
- L8:2045 weakly excites
- L7:2763 weakly excites
- L6:13 weakly excites
- L2:1450 weakly suppresses
- L6:1895 weakly excites
- L5:258 weakly excites
- L5:616 weakly excites
- L9:929 weakly excites 'each' in 'each other'
Feeds later layers
- L22:643 excites
- L21:1144 excites
- L11:173 excites
- L16:592 excites
- L14:677 suppresses
- L12:887 excites
- L27:1381 excites
- L17:259 excites
- L20:1476 excites
- L16:415 weakly excites
- L18:2290 weakly excites
- L25:849 weakly excites
- L19:541 weakly excites
- L23:1062 weakly excites
- L23:2992 weakly suppresses unclear: first pieces of split words
- L18:513 weakly excites
- L13:1513 weakly excites
- L18:1026 weakly excites
- L25:2825 weakly suppresses
- L14:611 weakly excites
- L27:631 weakly suppresses
- L15:364 weakly excites
- L20:272 weakly excites
- L14:248 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
- itself
- himself
- 自己
- oneself
- myself
- self
- 내
- sich
- herself
- собой
Suppresses
- мент
- rowsers
- LookAndFeel
- 第一家
- CollectionView
- PackageManager
- pez
- toLocale
- 玘
- arshal