Neuron Explorer

Qwen3-0.6B

"word" after a language origin phrase

A neuron in layer 1 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 "word" when a sentence says where another word comes from, like "from the Latin word" or "the Greek word". Turning it up did not make the model write about word origins, so its effect on output is unclear.

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

    ated in any way.⏎Likewise, damage by harsh scratching cannot be reversed.⏎Scratched tiles will continue to perform<|endoftext|>The word biathlon comes from the Greek word meaning two contests. At the Winter Olympics it means joining two sports together. Those

  2. #2 peak

    . The term is commonly used and understood in many different languages, although it's sometimes considered old-fashioned.What does gratis mean in English?⏎Gratis comes from the Latin word for "favor;" so in English a party favor is a small item given gratis

  3. #3 peak

    Thoughts⏎What Is Soil?⏎The term “soil” comes from the Latin word solis, meaning “ground”. In English, we use the terms “earth

  4. #4 peak

    they are the only ones a naked eye can see. The word itself, planet, comes from the ancient Greek word planets, meaning wanderers. While stars look the same night after night, slowly

  5. #5 peak

    . It is in almost every part of the body including the teeth and blood vessels. It is like the glue that holds everything together and is taken from the Greek word “kola” meaning glue. Thus it is easy to see how important it

  6. #6 peak

    complex of earthen works at Poverty Point, Mound A is believed to be the final and crowning addition to the sprawling 700<|endoftext|>The Hebrew word for agency is סוכנות, most recognizably in the Hebrew name of

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: I have been trying to solve a problem, and I have been thinking about it. Now I have a question that is about solving, and I have been trying to solve it. I have been thinking about it, and I have been trying

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

    at 1-800-WWW-DELL.⏎How Safe is Encryption Really?⏎Providing encrypted information online is as safe as doing it over the phone.<|endoftext|>In the Mojave Desert of Southeastern California is a national park the

  2. #2 peak

    ’t waste valuable time spinning their wheels.⏎Does your teen have the “planning gene”?⏎You know when you see them – paper planners, calendars on the phone, everything laid out. While some people and teens are natural planners, most are

  3. #3 peak

    2.htm)View an example of a Dataforth test report that ships with each module.Remember, our Application Engineers can assist you with signal conditioner selection over the phone or via fax and email. Call us at our

  4. #4 peak

    have not responded so enthusiastically to the “market signals” about college. On average, college pays off, but for a sizeable minority of college graduates, the arithmetic is not so convincing.⏎Imagine that you are a recent high school graduate deciding whether

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. L0:1224 suppresses
  2. L0:774 weakly excites
  3. L0:178 weakly suppresses
  4. L0:1301 weakly suppresses
  5. L0:1571 weakly excites
  6. L0:2523 weakly excites
  7. L0:2566 weakly suppresses
  8. L0:769 weakly excites
  9. L0:2590 weakly suppresses
  10. L0:1136 weakly suppresses
  11. L0:903 weakly excites
  12. L0:895 weakly excites
  13. L0:26 weakly excites
  14. L0:31 weakly suppresses
  15. L0:2818 weakly suppresses
  16. L0:2283 weakly suppresses
  17. L0:698 weakly suppresses
  18. L0:970 weakly suppresses
  19. L0:1097 weakly excites
  20. L0:185 weakly excites
  21. L0:1074 weakly excites
  22. L0:450 weakly suppresses
  23. L0:730 weakly suppresses
  24. L0:683 weakly excites

Feeds later layers

  1. L8:2006 excites
  2. L16:3012 excites
  3. L13:838 excites
  4. L6:1244 weakly excites
  5. L14:2282 weakly suppresses
  6. L11:2336 weakly excites
  7. L20:1772 weakly excites
  8. L2:723 weakly suppresses
  9. L9:145 weakly suppresses
  10. L2:1211 weakly excites
  11. L16:1599 weakly excites
  12. L16:1956 weakly suppresses
  13. L19:1934 weakly excites
  14. L2:1304 weakly suppresses
  15. L8:2829 weakly suppresses
  16. L15:1497 weakly excites
  17. L13:1251 weakly excites
  18. L17:2648 weakly suppresses number word before a list of reasons
  19. L3:4 weakly excites
  20. L23:1512 weakly excites
  21. L16:2863 weakly excites
  22. L13:2593 weakly suppresses
  23. L2:2124 weakly suppresses
  24. L8:1580 weakly suppresses 'with' after a comma in news clauses

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 -1.25 and -1.124 values between -0.63 and -0.514 values between -0.51 and -0.3827 values between -0.38 and -0.26177 values between -0.26 and -0.141274 values between -0.14 and -0.021961 values between -0.02 and 0.11381 values between 0.11 and 0.2354 values between 0.23 and 0.358 values between 0.35 and 0.484 values between 0.48 and 0.603 values between 0.60 and 0.722 values between 0.72 and 0.853 values between 0.97 and 1.092 values between 1.09 and 1.222 values between 1.34 and 1.461 values between 3.55 and 3.68offnegativepositiveoften

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. sworth
  2. roring
  3. ised
  4. pill
  5. الي
  6. 生活方式
  7. OVE
  8. pped
  9. ursors
  10. 琢

Suppresses

  1. FromBody
  2. .setAuto
  3. rika
  4. exels
  5. .setAdapter
  6. adesh
  7. trak
  8. ';";⏎
  9. .flatMap
  10. 湿润