'of' after 'one' (one of)
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. 4 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 word 'of' when it follows 'one', as in 'one of our representatives' or 'one of the following'. Turning it up made the model's text wander into repeated phrases about meanings and questions, which does not clearly match the pattern.
Written by Claude from this neuron's activations and steering outputs. Confidence: medium. Think it is wrong? Flag it (opens an email to the maker with the neuron already filled in).
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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00 experiments, then it is high probability that the hypothesis has been refuted.⏎If the hypothesis failed in their experiments, repeatedly, the scientist can do one of two things:⏎- He
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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: 2005. The function is given by a function $ f(x) $ with the x-axis of interest and $ f(x) $ with the x-axis of interest, and the x-axis of interest. The function is given
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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LDS Church. Other buildings were built on the plot, including a tabernacle (prior to the one occupying Temple Square today) and Endowment House, both of which were later torn down. The Salt Lake Tabernacle, home of the
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other words, is⏎where n is an integer.⏎Find the values for the range.⏎Similar to the tangent function, you can define cotangent as⏎Both of these values can be decimals. The range of cotangent also has no restrictions;
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and a shape is a group of coloured cells that are directly or indirectly connected to each other.⏎The diameter of a shape is the greatest distance between any two of its
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Best of all, it looks gorgeous, as well as improving health.⏎Carefully selected varieties of plants remove pollutants including nitrogen dioxide and microscopic particulate matter, both of which are are significant problems in cities in developed and developing countries: UK Government Environmental
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
- L3:1673 suppresses
- L1:2683 weakly excites
- L1:1590 weakly excites
- L1:329 weakly excites
- L1:1099 weakly excites
- L2:467 weakly suppresses
- L3:704 weakly suppresses
- L3:416 weakly excites
- L3:2886 weakly suppresses
- L3:675 weakly suppresses
- L1:2907 weakly excites
- L3:1581 weakly excites "the" after "percent of" or "a lot of"
- L3:2034 weakly excites
- L3:50 weakly excites
- L2:2817 weakly suppresses
- L3:691 weakly excites
- L3:1375 weakly excites
- L1:1674 weakly excites
- L2:1919 weakly suppresses
- L3:441 weakly excites
- L3:122 weakly excites
- L2:2806 weakly suppresses
- L1:1511 weakly excites
- L2:574 weakly suppresses
Feeds later layers
- L8:2002 excites
- L6:1652 weakly suppresses
- L9:1227 weakly excites
- L6:746 weakly excites
- L8:1475 weakly excites
- L14:2734 weakly excites
- L8:532 weakly suppresses
- L17:2197 weakly excites
- L18:2273 weakly suppresses
- L16:2457 weakly suppresses
- L17:1355 weakly suppresses
- L20:1914 weakly excites
- L14:48 weakly excites
- L5:2179 weakly excites "the" after "one of"
- L25:2273 weakly excites
- L8:1698 weakly suppresses
- L13:1945 weakly excites
- L15:2528 weakly suppresses
- L8:1858 weakly excites
- L19:769 weakly suppresses
- L5:1296 weakly suppresses
- L21:1430 weakly excites
- L10:1393 weakly suppresses
- L23:2763 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
- isch
- ius
- 一方
- joining
- join
- 或
- witch
- atom
- .alibaba
- dir
Suppresses
- edException
- ulado
- culos
- cribed
- onto
- 习惯了
- 誊
- ington
- UpdatedAt
- *)((