'the' right before a year or number
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. 0 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 'the' when a year or a number like '1950s' or '20th century' comes next. It seems to expect a date. Turning it up did not produce date-related text in the samples, so the link is mostly from the examples.
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 1992 fieldwork consisted of⏎surface collection and the excavation of five
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The 2025 targets are unlikely to be met, and health service providers will
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the 20th century there was a wave that brought in immigrants from Asia and Africa
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the 1950s and 1960s, and the Free
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the 16-county District averaged 3.89 inches – the second-largest
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the 1890s the Night Riders had done their part, Jim Crow laws
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 have been thinking about lately, and I have been thinking about lately. The answer is that I have been thinking about lately. I have been thinking about lately. I have been thinking about lately. The answer is that I have been thinking
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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, or qubits, can encode both at the same time. This essentially enables quantum computers to work through multiple scenarios simultaneously, rather than exploring them one after the other. However, these mixed states don’t last long, so the information processing must
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the partial results one below the other, starting with the product of the last digit of the second number. Each partial
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directions; the cold will be severe and the winds will be fierce. The sun will be of no use. Three of these winters will come, one after the other, with no summer in between. But before that, there will have
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from one to the other. One qualification: OED refers only to a Roman vaulter but the
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
None: this is the first layer.
Feeds later layers
- L10:756 weakly excites
- L3:630 weakly suppresses
- L20:341 weakly excites
- L5:547 weakly suppresses
- L13:381 weakly excites
- L8:344 weakly suppresses
- L18:1919 weakly excites
- L20:412 weakly excites
- L12:1501 weakly suppresses
- L14:2312 weakly suppresses
- L24:1860 weakly excites
- L8:2465 weakly excites
- L4:415 weakly suppresses
- L4:1121 weakly suppresses
- L7:1709 weakly suppresses
- L14:2157 weakly suppresses
- L3:2090 weakly suppresses
- L13:2329 weakly suppresses
- L4:1670 weakly suppresses
- L1:1317 weakly suppresses religious words like faith and christ
- L26:2286 weakly suppresses
- L15:453 weakly excites
- L19:577 weakly suppresses
- L21:2182 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
- ató
- assign
- Japanese
- Japanese
- игра
- are
- ot
- сти
- seeking
- CAP
Suppresses
- enario
- uard
- دة
- iture
- ospel
- %c
- ائق
- quent
- ocache
- rances