Neuron Explorer

Qwen3-0.6B

"to" after 'for X' infinitives

A neuron in layer 4 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

Fires on the word "to" in phrases like "For it to work" or "for evolution to be true," where a "for someone or something" phrase introduces an action. Its strongest negative cases are the word fragment "bs" and "owns" in place names like suburbs. Turning it up gave text with no clear shared theme.

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 activation was highest. Amber marks positive activation; the outlined token is the peak. Hover a token for its strength.

  1. #1 peak

    another device.⏎For it to work, at least two devices need to be Sparsh-enabled. A user wanting

  2. #2 peak

    ippedTextTolerance property allows you to⏎define a rectangular area in the center of the character, the⏎entire of which must be visible for the character to be visible.⏎The width and height of the rectangular area are determined by the⏎

  3. #3 peak

    new antibiotic.⏎For treatment to be effective and practical, it must be simple to administer by mouth as a single

  4. #4 peak

    and photographic lineups. Lineups involve showing the witness a group of possible suspects. Photographic lineups provide a selection of pictures of possible suspects for the witness to choose from. Showups are done by showing one suspect to the witness.⏎The

  5. #5 peak

    to carbon dating, kids of faith have questions these days. They're struggling to put together what their schools teach about science and what their beliefs are.⏎For evolution to be true, the earth must be billions of years old, with life-forms

  6. #6 peak

    being punishable by the assignment of monetary damages to compensate victims for their losses, and criminal fraud being punishable by jail time as well as by fines. For a fraud to be a criminal offense, it needs to have been in violation of either a federal

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: "Hohu. The question is: Hohu, and the city is: Hohu. We have: Hohu, and the city is: Hohu. Now is: Hohu, and the city

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

    Central Business District and surrounding areas such as Braamfontein, Hillbrow, and Yeoville for more secured houses or offices in the Northern Suburbs. By the late 1990s, the Central<|endoftext|>A shoulder

  2. #2 peak

    and urban blight, many of the buildings are unoccupied as tenants have left for more secure locations in the Northern Suburbs, in particular Sandton and Rosebank. There are significant movements to revive the

  3. #3 peak

    of extraordinary contrasts, of glass and steel skyscrapers and fetid shantytowns, of internationally recognized universities and widespread illiteracy, of glittering abundance and

  4. #4 peak

    and⏎• increase learning opportunities outdoors.⏎Placing children’s well-being at the center of planning of the urban environment brings benefits – not only for children but for city dwellers of all ages. Child-friendly environments support inclusion, well-being, sustainability

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. L3:1880 excites
  2. L3:1035 suppresses
  3. L3:1216 weakly suppresses
  4. L2:1071 weakly excites
  5. L3:2484 weakly excites
  6. L3:421 weakly suppresses
  7. L3:2359 weakly excites
  8. L3:2715 weakly suppresses
  9. L3:50 weakly excites
  10. L3:1390 weakly excites 'that' in 'so that' purpose clauses
  11. L3:2963 weakly suppresses "discrete" and "dispersive" in technical text
  12. L3:2976 weakly suppresses
  13. L2:1447 weakly excites
  14. L2:2868 weakly excites
  15. L3:2012 weakly suppresses
  16. L2:2809 weakly excites
  17. L3:1257 weakly suppresses
  18. L0:1188 weakly suppresses
  19. L3:1831 weakly excites
  20. L2:2713 weakly suppresses
  21. L3:1327 weakly suppresses
  22. L0:451 weakly suppresses
  23. L3:2369 weakly suppresses
  24. L2:370 weakly suppresses

Feeds later layers

  1. L7:2284 suppresses
  2. L5:206 suppresses
  3. L5:981 suppresses
  4. L5:2701 suppresses
  5. L16:2238 suppresses
  6. L6:511 suppresses
  7. L20:2861 suppresses
  8. L5:2698 weakly suppresses
  9. L5:2815 weakly suppresses
  10. L8:2606 weakly suppresses
  11. L12:1227 weakly excites
  12. L25:2863 weakly suppresses
  13. L11:1324 weakly suppresses
  14. L26:2247 weakly suppresses
  15. L11:598 weakly suppresses
  16. L10:1974 weakly suppresses
  17. L6:2236 weakly suppresses
  18. L23:1464 weakly excites
  19. L24:2496 weakly excites
  20. L22:1840 weakly suppresses
  21. L20:1163 weakly suppresses
  22. L14:1037 weakly excites
  23. L9:2496 weakly suppresses
  24. L11:1171 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 -8.01 and -7.692 values between -6.72 and -6.401 values between -6.08 and -5.761 values between -4.80 and -4.481 values between -3.51 and -3.191 values between -2.87 and -2.551 values between -2.55 and -2.231 values between -2.23 and -1.911 values between -1.91 and -1.583 values between -1.58 and -1.265 values between -1.26 and -0.942 values between -0.94 and -0.6224 values between -0.62 and -0.301636 values between -0.30 and 0.022177 values between 0.02 and 0.3444 values between 0.34 and 0.663 values between 0.66 and 0.992 values between 0.99 and 1.311 values between 2.27 and 2.591 values between 4.52 and 4.84offnegativepositiveoften

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. Vict
  2. ViewItem
  3. viewDidLoad
  4. bac
  5. ��
  6. createView
  7. 是一部
  8. 战机
  9. 保驾
  10. lasting

Suppresses

  1. -town
  2. dale
  3. .qml
  4. folk
  5. neighbor
  6. nog
  7. reur
  8. neighborhoods
  9. gossip
  10. infix