the word "no" in "no longer"
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. 23 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 is very clear: it fires on the word "no" when it is followed by "longer", as in "will no longer be supported". Turning it up made the model keep repeating the word "longer" over and over.
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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sense of smell and taste changes as you age, and foods you once enjoyed may no longer seem appealing. Or, changes to your taste buds may lead you to make
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community as a whole.⏎“There have been reports<|endoftext|>Note: As of the 2022-2023 program year, the Cyber Chip is no longer a Cub Scout rank requirement. This information is being retained for reference.⏎The
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0, the Society had to rethink its purpose as it was no longer needed to provide for Belfast’s deprived working-class inhabitants. It instead became a
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it, and thus feel empathy for the idea that it is the last working object on earth. It has lost its purpose—to serve others— because the others are no longer there. In this way, Bradbury is able to evoke emotion in the
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the drug.⏎Strains of gonorrhoea which need a higher concentration of a drug to kill them become the dominant ones. This keeps happening until the drug no
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: Boudou, Boudou, Boudou, Boudou. The word Boudou is also known as Boudou, Boudou, Boudou, Boudou. Boudou is the same as
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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should be called getVolumeOfSphere(), take one parameter called radius and apply the relevant formula to calculate and return the volume of a sphere based on the given radius.⏎- Your second subroutine should be called getVolume
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classically Diophantine geometry), a subfield of algebraic number theory.⏎- The study of
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growth of two human embryonic stem cell lines that lack PIG-A, and therefore do not contain any proteins known as glycosylphosphatidylinositol (GPI) anchor proteins on the cell’s surface. GPI anchor proteins
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.1 to 169.254.255.254⏎D. 169.254.0.1 to 169.254.255.2
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
- L21:724 suppresses
- L22:635 suppresses
- L22:3011 excites
- L18:3044 suppresses
- L17:2393 suppresses
- L21:1414 excites
- L1:509 excites 'to' after 'unable'
- L19:2887 weakly excites
- L16:2976 weakly suppresses
- L8:2941 weakly excites
- L21:2826 weakly suppresses
- L14:2441 weakly excites
- L10:111 weakly suppresses
- L18:569 weakly excites
- L19:259 weakly excites
- L7:2648 weakly excites
- L16:752 weakly excites
- L15:1723 weakly excites
- L17:1620 weakly suppresses
- L6:2954 weakly excites
- L15:1293 weakly suppresses
- L0:1229 weakly excites
- L4:725 weakly suppresses
- L19:1632 weakly excites
Feeds later layers
- L27:1178 excites
- L26:551 excites
- L26:1469 excites
- L24:847 excites
- L26:777 excites
- L27:383 suppresses
- L27:1896 excites split names and comparative stems
- L24:470 suppresses
- L24:1321 excites
- L27:1469 excites
- L24:561 suppresses
- L27:948 weakly excites
- L26:1324 weakly suppresses
- L27:368 weakly suppresses
- L25:2908 weakly suppresses
- L24:2350 weakly excites
- L26:1908 weakly excites
- L26:1458 weakly suppresses
- L25:1196 weakly excites
- L24:2412 weakly excites
- L25:307 weakly suppresses
- L26:2136 weakly excites
- L26:1834 weakly excites
- L25:884 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
- longer
- Longer
- long
- shorter
- 较长
- -long
- Long
- longest
- LONG
- long
Suppresses
- =B
- B
- BR
- B
- ball
- balls
- ering
- (ball
- Ball
- _ball