Neuron Explorer

Qwen3-0.6B

'sh' starting a word piece

A neuron in layer 26 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 'sh' sound at the start of a split word, as in 'shatter', 'shins', 'shelve' and 'shoveling'. When it was pushed up, the model fell into repeated symbols such as asterisks or line breaks, and when pushed down it produced rows of blanks.

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

    from deep pumped bores in vertical transmissive fault or shatter zones could augment mountain rivers for power generation. Flows might be augmented in

  2. #2 peak

    Implicit and Explicit Processing⏎We often learn a task by telling ourselves what to do. In the squat, I think about knees tracking out, butt going back, shins perpendicular, and opening up the hips on the way down. The explicit directions

  3. #3 peak

    to struggle to make reasons for not doing things they obviously ought to do. When the particular thing they ought to do involves the encouragement of art, their struggles to shelve the whole matter become quite painful. As an illustration of this, the case

  4. #4 peak

    million tons of plastic bottles. Plastic water bottles offer many advantages to both the manufacturer and the consumer. It is lightweight, which lowers transportation costs. It does not shatter, making it a safer option. However,

  5. #5 peak

    tibia) hurts when you press on it firmly with your finger.⏎The causes of shins splints include problems with body mechanics, a sudden or excessive increase in the

  6. #6 peak

    bi-products such as saw dust and planer shavings. This production demanded large amounts of water, and thus the pump tower and

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: __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __ __

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

    and regulate blood pressure.⏎- Hijiki has more calcium than any other sea vegetable (14 times more calcium than milk), and is high in fiber.⏎- Nori contains as much protein as soybeans and

  2. #2 peak

    Описание презентации Презентация london-history-12162149479

  3. #3 peak

    flows, dotted: scoria cones. // in Elliot Hildner / University of Bremen⏎The main crack<|endoftext|>analysis of algoritms⏎robert.kern at gmail.com⏎Fri Sep 10 00:10

  4. #4 peak

    an ancient temple for Lord Krishna. Dwapara Yug<|endoftext|>Simple technique appears to be safe and effective, review suggests⏎By Robert Preidt⏎MONDAY, Oct. 15 (HealthDay News)

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. L25:255 strongly excites pieces of split scientific or place names
  2. L19:1755 suppresses
  3. L14:2195 weakly suppresses
  4. L16:41 weakly suppresses
  5. L2:67 weakly suppresses
  6. L22:591 weakly suppresses
  7. L14:67 weakly suppresses
  8. L5:204 weakly suppresses
  9. L0:117 weakly suppresses
  10. L16:2438 weakly excites
  11. L5:168 weakly excites
  12. L24:244 weakly suppresses
  13. L22:229 weakly suppresses "of" after a noun in "X of Y"
  14. L12:467 weakly suppresses
  15. L15:1691 weakly excites
  16. L22:2398 weakly excites
  17. L18:1470 weakly suppresses comma after an introductory phrase
  18. L21:81 weakly excites
  19. L15:1116 weakly suppresses
  20. L0:1064 weakly suppresses
  21. L20:2102 weakly excites
  22. L24:331 weakly suppresses
  23. L21:881 weakly suppresses comma after 'if' clause in prose, braces in code
  24. L22:379 weakly excites

Feeds later layers

  1. L27:799 excites
  2. L27:860 weakly excites
  3. L27:136 weakly excites
  4. L27:445 weakly excites
  5. L27:2927 weakly excites
  6. L27:1466 weakly excites
  7. L27:519 weakly suppresses
  8. L27:1595 weakly excites
  9. L27:1630 weakly excites
  10. L27:1902 weakly excites
  11. L27:1472 weakly excites
  12. L27:2893 weakly suppresses
  13. L27:1053 weakly excites
  14. L27:207 weakly excites
  15. L27:2449 weakly excites
  16. L27:1535 weakly excites
  17. L27:1768 weakly suppresses
  18. L27:1326 weakly excites
  19. L27:2720 weakly excites
  20. L27:1527 weakly excites
  21. L27:1739 weakly excites
  22. L27:2688 weakly excites
  23. L27:1065 weakly excites
  24. L27:604 weakly excites

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 -15.78 and -15.102 values between -13.73 and -13.051 values between -13.05 and -12.362 values between -11.68 and -10.992 values between -9.62 and -8.943 values between -8.94 and -8.263 values between -8.26 and -7.577 values between -7.57 and -6.897 values between -6.89 and -6.209 values between -6.20 and -5.5210 values between -5.52 and -4.8316 values between -4.83 and -4.1523 values between -4.15 and -3.4737 values between -3.47 and -2.7857 values between -2.78 and -2.1091 values between -2.10 and -1.41175 values between -1.41 and -0.731170 values between -0.73 and -0.052048 values between -0.05 and 0.64133 values between 0.64 and 1.3248 values between 1.32 and 2.0126 values between 2.01 and 2.6915 values between 2.69 and 3.384 values between 3.38 and 4.065 values between 4.06 and 4.744 values between 4.74 and 5.433 values between 5.43 and 6.111 values between 6.11 and 6.801 values between 6.80 and 7.481 values between 8.17 and 8.851 values between 9.53 and 10.221 values between 10.22 and 10.901 values between 10.90 and 11.59offnegativepositiveoften

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. $⏎⏎
  2. …”⏎⏎
  3. ”⏎⏎
  4. $⏎⏎
  5. ”⏎⏎
  6. *⏎⏎
  7. »⏎⏎
  8. (){}⏎⏎
  9. !”⏎⏎
  10. ?”⏎⏎

Suppresses

  1. __
  2. остоя
  3. 到了
  4. 稠
  5. Recall
  6. ost
  7. oner
  8. 许
  9. FormData
  10. oods