Neuron Explorer

Qwen3-0.6B

'the' after 'to' in fixed phrases

A neuron in layer 18 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 responds to the word "the" when it comes right after "to", as in "to the point of", "to the west" or "burned to the ground". It is pushed the other way by some abbreviations and spelled-out terms. Turning it up made the text repeat "the" a lot without a clear meaning.

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

    (and ‘living’) foods, well, alive! In a world inundated by highly processed or Genetically Modified foods that use chemicals as preservatives, to the point where animals will not even consider what we eat to be edible (see note

  2. #2 peak

    on paper. Never mind what words they actually wrote, whether they used printing or cursive is Everything.⏎It's pretty serious business in Japan as well, to the point where a Japanese teacher, without seeing the actual action of writing taking place,

  3. #3 peak

    005, over 7,000 natural disasters worldwide had taken the lives of nearly 2 million people and produced economic losses of more than $1

  4. #4 peak

    The two rims are separated by a distance of only a couple of kilometers. To the west is the bowl-shaped crater Schmidt, and farther to the north are Manners

  5. #5 peak

    . history. The attack on the Greenwood section of Tulsa, home to a prosperous African American community in the segregated city, left over 35 blocks burned to the ground and over 10,000 people — most of them black

  6. #6 peak

    discussion of issues of race in the 1960s through the use of science fiction is an excellent example of this sort of approach. Now, to the matter of policebots.⏎The policebots under consideration are

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: the need to *over* the * ** ** the * *** *** **** *** *** over the *** **** ***** need to ********** over the *****

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

    in nearly every single manufacturing sector.⏎The possibilities of including women in this field are limitless. More women are pursuing college degrees in the fields of STEM (Science, Technology, Engineering, and Math) than ever before. Common misconceptions

  2. #2 peak

    D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | 0-

  3. #3 peak

    amount of tolerable global emissions over a period of time -- has to be set roughly at 1,600 Gt CO2 eq (gigatonnes of carbon dioxide equivalent) between 1990 and 20

  4. #4 peak

    ters, ditches, man-made channels, storm drains, etc.) that is also:⏎- Owned or operated by a public entity (which can include cities, townships, counties, military bases, hospitals, prison complexes,

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. L10:445 strongly excites
  2. L2:1585 strongly excites
  3. L7:219 strongly excites
  4. L0:246 strongly excites
  5. L4:613 strongly excites
  6. L17:166 strongly excites
  7. L16:205 strongly suppresses
  8. L1:30 strongly suppresses
  9. L16:827 strongly suppresses
  10. L0:495 strongly excites
  11. L17:360 strongly suppresses
  12. L16:260 excites
  13. L10:393 suppresses signs and constants in equations
  14. L7:674 suppresses
  15. L2:2029 suppresses
  16. L17:509 suppresses
  17. L0:2471 suppresses
  18. L17:420 excites
  19. L2:292 excites
  20. L16:353 excites
  21. L16:524 excites
  22. L13:620 excites
  23. L6:1434 excites
  24. L10:951 suppresses

Feeds later layers

  1. L20:164 strongly suppresses
  2. L19:643 strongly suppresses
  3. L22:2254 strongly suppresses
  4. L21:1112 suppresses
  5. L19:1865 excites
  6. L27:1887 suppresses
  7. L27:2672 suppresses word-start 't' fragment in split words
  8. L19:526 excites
  9. L26:1501 suppresses
  10. L26:1899 suppresses
  11. L26:2433 suppresses
  12. L21:2676 suppresses
  13. L19:1878 excites
  14. L23:918 suppresses
  15. L23:2868 suppresses
  16. L26:2921 suppresses
  17. L25:2552 excites
  18. L23:2818 suppresses
  19. L19:563 excites
  20. L25:2836 suppresses
  21. L20:359 excites
  22. L27:2092 suppresses
  23. L21:1793 excites
  24. L26:1224 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 -23.34 and -22.713 values between -21.43 and -20.792 values between -18.87 and -18.236 values between -18.23 and -17.603 values between -17.60 and -16.962 values between -16.96 and -16.323 values between -16.32 and -15.681 values between -15.68 and -15.043 values between -15.04 and -14.404 values between -14.40 and -13.764 values between -13.76 and -13.126 values between -13.12 and -12.4910 values between -12.49 and -11.855 values between -11.85 and -11.217 values between -11.21 and -10.575 values between -10.57 and -9.931 values between -9.93 and -9.291 values between -9.29 and -8.655 values between -8.65 and -8.015 values between -8.01 and -7.381 values between -7.38 and -6.749 values between -6.74 and -6.108 values between -6.10 and -5.463 values between -5.46 and -4.8211 values between -4.82 and -4.1816 values between -4.18 and -3.5424 values between -3.54 and -2.9131 values between -2.91 and -2.2726 values between -2.27 and -1.6336 values between -1.63 and -0.9972 values between -0.99 and -0.35453 values between -0.35 and 0.291603 values between 0.29 and 0.931352 values between 0.93 and 1.57186 values between 1.57 and 2.21offnegativepositiveoften

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. IDER
  2. IME
  3. agu
  4. berger
  5. ihar
  6. icense
  7. FTER
  8. keley
  9. iders
  10. иде

Suppresses

  1. perature
  2. vj
  3. ож
  4. sort
  5. ология
  6. .operations
  7. kindly
  8. verse
  9. sort
  10. ór