'of' after 'all' in phrases
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. 16 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 'of' when it comes right after 'all', as in 'all of the students' or 'all of your files'. Its weakest responses are on decimal points and number ranges. Turning it up made the text repeat words like 'good' and 'bad', so it does not clearly change meaning.
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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't remember the pronunciation, tell them to raise their hands and ask you to say it for them and then have them repeat.⏎- I usually start by having all of the students count from 1-31 with me.⏎- Then I have
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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: of the, or of the, of, of, of, of, of? The of, or of the of? The of, or of? The of or?? The of, or?? The of??
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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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
- L11:31 suppresses
- L13:132 excites "of" after a noun
- L15:381 suppresses
- L11:18 excites
- L1:571 suppresses
- L15:286 excites
- L2:49 excites
- L9:7 excites
- L1:87 weakly excites
- L14:303 weakly excites
- L14:453 weakly suppresses
- L5:9 weakly suppresses
- L9:30 weakly excites
- L15:56 weakly suppresses
- L10:372 weakly suppresses
- L7:191 weakly suppresses
- L1:951 weakly suppresses
- L7:102 weakly excites
- L5:46 weakly suppresses
- L10:33 weakly suppresses
- L9:61 weakly excites
- L6:4 weakly suppresses
- L8:7 weakly suppresses
- L5:50 weakly suppresses
Feeds later layers
- L19:277 suppresses
- L19:217 suppresses
- L19:233 suppresses
- L19:197 suppresses
- L18:1952 excites
- L19:283 weakly suppresses
- L18:2497 weakly excites hyphen inside compound words
- L18:238 weakly excites
- L19:287 weakly suppresses
- L19:230 weakly suppresses
- L19:227 weakly suppresses
- L23:2521 weakly excites
- L19:403 weakly suppresses
- L24:104 weakly suppresses
- L19:216 weakly suppresses
- L19:129 weakly suppresses
- L20:43 weakly suppresses
- L25:1530 weakly excites
- L20:169 weakly suppresses
- L20:217 weakly suppresses
- L17:1039 weakly excites
- L19:1432 weakly suppresses
- L19:243 weakly suppresses
- L19:244 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
- mediately
- fois
- шед
- whole
- slightest
- elaide
- ellidos
- 多地
- えない
- whole
Suppresses
- /object
- oning
- /history
- ��
- /console
- méd
- пад
- villa
- ή
- Méd