Neuron Explorer

Qwen3-0.6B

'to' in 'to do with'

A neuron in layer 23 of Qwen3-0.6B. Its strongest snippets are very consistent: they mostly peak on the same token. It is featured, so it has steering outputs.

What this neuron does

This neuron fires on the word 'to' in phrases like 'has to do with' or 'nothing to do with'. Turning it down made the model output only the word 'to' 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 activation was highest. Amber marks positive activation; the outlined token is the peak. Hover a token for its strength.

  1. #1 peak

    <|endoftext|>Forget the batteries for a second, thats just one of a thousand analogies you could use to describe voltage/current and the reason that no current flows has nothing to do with the electro-chemical properties of batteries, its far simpler.⏎The easiest

  2. #2 peak

    build a modest addition to its building for Sunday school classes. Despite the church demonstrating that the addition is critical to<|endoftext|>STEM and Wall Street⏎What does STEM have to do with Wall Street?⏎When you think about CAREERS on Wall Street, what

  3. #3 peak

    by a single or multiple earthworks of massive proportions. Hillforts must have formed symbols of power within the landscape, while their function may have had as much to do with ostentation and display as defence. Pentre Camp comprises a sub

  4. #4 peak

    carbon dioxide does. Methane gas (CH4) traps about 34 times more heat within the earth’s atmosphere.⏎So what does worm manure have to do with dog waste?⏎Well, dog waste is not just full of bacteria that

  5. #5 peak

    using the disciplinary methods passed down by his father.⏎“I have always believed that the way my parents disciplined me has a great deal to do with the success I have enjoyed as a man,” Peterson said in a statement

  6. #6 peak

    ictions like gambling, that have nothing to do with food. Tolerance is both a result and a cause of addictive behavior

Steering

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.

turned down a lot
Prompt

Here is something I have been thinking about lately: to to to to to to to to to to to to to to to to to to to to to to to to to to to to to to to to to to to to to to to to to to to to to to to to

Most negative on

Where the activation went furthest below zero. Blue marks negative activation. Many neurons mean something different in each direction.

  1. #1 peak

    workable solution for polygonal transects remains elusive.⏎Keywords: Boundary overlap, Inclusion region, Reflection method, Transect configuration⏎- We recommend that you also print this page and attach it to the printout of the article

  2. #2 peak

    , they will absorb more nutrients and end up a similar size as they would in their natural habitat.⏎But, of course, the idea of a bonsai tree is to produce a plant that looks

  3. #3 peak

    has unique experiences with a unique home environment. Her current attitude is a by-product of her life experiences and the people around her. My advice to you is stop worrying about whether your teen’s attitude measures up to other teens’ attitude but

  4. #4 peak

    invented thousands of years ago but stairs are a 13th century invention. Claude Stairs was working on the court of Count Of Flanders and his idea was laughed at when he first introduced it in 1208.⏎The idea

Connections

Show on map

Which neurons in earlier layers feed this one, and which later ones it feeds. Amber links excite, blue links suppress; longer bars are stronger.

Fed by earlier layers

  1. L22:589 excites
  2. L22:192 weakly suppresses
  3. L21:2453 weakly suppresses
  4. L6:2287 weakly suppresses
  5. L19:2347 weakly suppresses
  6. L17:2215 weakly suppresses
  7. L20:2262 weakly excites
  8. L19:2664 weakly excites
  9. L1:1487 weakly suppresses
  10. L2:1482 weakly suppresses
  11. L19:1613 weakly suppresses "is" after a sentence-opening "It"
  12. L19:2472 weakly excites
  13. L19:693 weakly suppresses
  14. L10:2551 weakly suppresses
  15. L5:2332 weakly excites
  16. L18:1768 weakly excites
  17. L18:621 weakly suppresses
  18. L3:2844 weakly suppresses
  19. L11:565 weakly excites
  20. L20:1636 weakly suppresses
  21. L20:2794 weakly excites
  22. L19:1626 weakly suppresses
  23. L6:2145 weakly excites
  24. L12:539 weakly excites

Feeds later layers

  1. L26:408 strongly suppresses
  2. L27:456 strongly suppresses
  3. L24:959 suppresses
  4. L25:70 excites
  5. L24:381 suppresses
  6. L27:158 excites
  7. L24:396 excites
  8. L27:2194 suppresses the letter t starting a word fragment
  9. L25:1240 weakly suppresses
  10. L24:2825 weakly excites
  11. L25:1719 weakly excites
  12. L27:1461 weakly suppresses
  13. L26:148 weakly excites
  14. L24:2797 weakly suppresses single letters starting names
  15. L25:361 weakly suppresses
  16. L24:2304 weakly excites
  17. L24:1784 weakly suppresses
  18. L27:1037 weakly suppresses
  19. L24:445 weakly excites
  20. L26:1610 weakly suppresses
  21. L27:1809 weakly excites
  22. L25:226 weakly excites
  23. L27:1008 weakly excites
  24. L24:2737 weakly suppresses

Activation histogram

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.

1 values between -12.73 and -12.191 values between -8.98 and -8.451 values between -8.45 and -7.911 values between -7.38 and -6.842 values between -6.84 and -6.312 values between -6.31 and -5.771 values between -5.77 and -5.231 values between -5.23 and -4.703 values between -4.70 and -4.162 values between -4.16 and -3.631 values between -3.63 and -3.098 values between -3.09 and -2.5610 values between -2.56 and -2.0214 values between -2.02 and -1.4943 values between -1.49 and -0.95234 values between -0.95 and -0.422182 values between -0.42 and 0.121223 values between 0.12 and 0.65139 values between 0.65 and 1.1919 values between 1.19 and 1.725 values between 1.72 and 2.266 values between 2.26 and 2.794 values between 2.79 and 3.331 values between 3.33 and 3.861 values between 4.93 and 5.472 values between 6.00 and 6.541 values between 8.15 and 8.68offnegativepositiveoften

Logit lens

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 layers instead.

Promotes

  1. cade
  2. 接入
  3. úc
  4. ://%
  5. 坊
  6. pector
  7. 路
  8. 好处
  9. setC
  10. 航道

Suppresses

  1. to
  2. to
  3. To
  4. →to
  5. To
  6. .to
  7. _to
  8. -to
  9. 到
  10. ToDevice