'to' after 'for someone' (purpose infinitive)
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 'to' when it comes after 'for' plus a person or thing, as in 'for people to take shelter'. It switches off, strongly, on the word 'tips'. Turning it up did not lead to a clear change in topic.
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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to European excellence in imaging for life sciences researchers,” says interim-director Professor John Eriksson from Finland. “All of the facilities are excellent and open for anyone to use, with nominal fees. We provide training, data management services and access to
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historic world from your own imagination⏎A thick jungle scene puzzle with dinosaurs hidden for others to find⏎Well, I could go on and on. That's just a sample
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into the school gymnasium.⏎With this is mind I wanted to ensure that the data that was being sourced within the session would be instantly displayed for the students to analyse. To make this possible I setup a
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experienced by His flesh, and even the humiliation he encountered when the soldiers beat him and stripped off his clothes, and hung Him on the cross for the world to see. Even Through rejection, deprivation, persecution, and many other dealings with sinful
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see how the superpowers of Egypt and Hatti fought each other to a standstill, leaving, for a short period, the land open for God’s people to take over.⏎Special thanks to Dr. Robert L Diss Jr. for his materials
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can withstand the dynamic shock of his touching the ground at speeds over 100 miles an hour, and a brain that can process information fast enough for him to react to his surroundings at high speed.⏎DC Comics dispenses with such explanations with
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: when you have a question for someone who is trying to get a better answer for the question. So, when you're thinking about a question to get a better answer to the question, this is for someone who is thinking about a question to
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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printable page, download the free Adobe Acrobat Reader.)⏎Click for the answers to this week's puzzle.⏎Click for Sudoku Puzzles from previous weeks.⏎Click for tips for teaching students to do Sudoku puzzles.⏎Copyright © 2006 Education
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gum disease, and tooth loss. Here are some tips on how to take care of teeth.⏎1. Brush your teeth twice daily:⏎
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getting organized. It offers tips for setting short- and long-term goals, choosing activities and fitting them into your
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answers to tough Oracle questions from Brian Peasland are available.⏎- The Best Oracle Web Links: tips, tutorials, scripts, and more.⏎- Have an Oracle or SQL tip to
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
- L3:2666 suppresses
- L3:3020 weakly suppresses
- L3:1791 weakly suppresses
- L0:2068 weakly excites
- L3:1873 weakly suppresses
- L3:2092 weakly suppresses
- L3:115 weakly suppresses
- L3:2361 weakly excites
- L3:3000 weakly excites
- L1:1109 weakly suppresses
- L3:2263 weakly excites the verb 'guide' or 'navigate'
- L0:122 weakly suppresses
- L1:2805 weakly suppresses
- L3:1210 weakly suppresses
- L1:2099 weakly suppresses
- L3:3040 weakly suppresses
- L2:1235 weakly excites
- L2:3027 weakly suppresses
- L3:1608 weakly suppresses
- L3:2517 weakly excites
- L2:1710 weakly suppresses
- L3:1641 weakly suppresses
- L3:2194 weakly suppresses
- L0:2042 weakly suppresses
Feeds later layers
- L22:2213 weakly suppresses
- L6:2512 weakly suppresses
- L6:2388 weakly suppresses
- L8:2050 weakly suppresses
- L5:1837 weakly suppresses
- L13:1438 weakly suppresses
- L6:431 weakly suppresses
- L6:442 weakly suppresses
- L12:1983 weakly excites
- L7:2498 weakly excites
- L11:1706 weakly suppresses
- L5:1604 weakly suppresses
- L10:3019 weakly suppresses
- L5:2174 weakly suppresses
- L27:2531 weakly suppresses
- L20:3063 weakly excites
- L9:2811 weakly suppresses
- L11:653 weakly suppresses
- L16:917 weakly suppresses 'of' inside organization names
- L7:2351 weakly suppresses
- L5:2538 weakly suppresses
- L8:2362 weakly excites
- L12:1358 weakly suppresses
- L6:1367 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
- trand
- allis
- ignment
- iationException
- igr
- -state
- urger
- alled
- state
- agnet
Suppresses
- 要点
- Osborne
- _oct
- cps
- cps
- cciones
- dyn
- 窍
- ally
- Tips