'find' in 'to find out'
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. 20 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 "find" when it is part of the phrase "to find out", as in "Take our quiz to find out". When it was turned up, the model kept producing the word "out" over and over, which matches the pattern.
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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used the strategy more often once it was mastered.⏎It's only one example of clever tricks used by cephalopods. Check out our full-length feature to find out more about these animals' astounding mental skills.<|endoftext|>- Cooking Time:
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tip of the month.⏎VSTD9 Oscilloscope Operation & Signal Test Methods⏎Use our online self assessment tool to find the right course for you⏎Using our '<|endoftext|>Dividing by zero. Even
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 can be a nurse and I can be a manager, and I can be a doctor. But I can also start a clinic, which is a unique, private, and ultimately, a second management of my first manager. It is a
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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Questions with Answer related to Computer Networking. Four options for each questions and only one options is the correct answer. Correct answers of these MCQ / Objective Questions are given below of this question set.⏎Multiple Choice Questions of Computer
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gathering information about visited websites and started applications. It is also measuring time in front of the monitor.<|endoftext|>DRDO Placement Paper (EMT & Microprocessor)⏎Paper: DRDO Placement Paper (EMT & Microprocessor)⏎EMT and micro
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banks⏎25. What is reserve deposit ration ? Explain⏎26. Explain briefly the process of investment multiplier⏎( Section – C )⏎27. Write four instruments of monetary policy of Reserve Bank of India⏎28. Describe any
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
- L19:1228 excites
- L10:190 excites
- L18:177 suppresses
- L3:62 excites
- L9:812 excites
- L16:692 suppresses
- L18:1156 weakly suppresses
- L19:332 weakly excites
- L6:750 weakly suppresses
- L9:429 weakly excites
- L6:16 weakly excites
- L8:495 weakly suppresses
- L3:1930 weakly suppresses
- L15:390 weakly suppresses
- L19:702 weakly excites
- L10:1032 weakly excites
- L10:435 weakly suppresses
- L4:2946 weakly suppresses
- L8:2948 weakly excites
- L2:1925 weakly suppresses
- L4:1890 weakly suppresses
- L4:1467 weakly excites
- L5:135 weakly suppresses
- L3:1676 weakly excites
Feeds later layers
- L23:906 strongly excites
- L27:850 excites
- L23:3038 excites
- L21:1525 excites
- L23:1433 excites the word 'figure' in 'figure out'
- L26:1474 excites
- L22:2937 weakly excites
- L22:2031 weakly excites
- L26:3016 weakly suppresses
- L22:23 weakly excites
- L24:492 weakly suppresses
- L22:174 weakly suppresses
- L25:2211 weakly suppresses
- L21:1580 weakly suppresses
- L25:444 weakly suppresses
- L24:2796 weakly excites
- L21:413 weakly excites
- L26:2695 weakly excites
- L24:1341 weakly excites
- L27:3019 weakly excites
- L26:2261 weakly excites
- L21:538 weakly excites
- L23:2987 weakly suppresses
- L21:452 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
- out
- out
- -out
- →out
- outs
- Out
- OUT
- Out
- _out
- -outs
Suppresses
- #__
- rome
- лект
- Past
- elijke
- 輪
- ?url
- Präsident
- Past
- bast