Neuron Explorer

Qwen3-0.6B

"what" after verbs of knowing

A neuron in layer 19 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 "what" after words like "know", "find out", or "shows", as in "find out what the Gospels are". It is most negative on "a" or "an" right before the word "example" or "list". Turning it down made the text loop on the word "example".

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

    30 grams of protein at each meal to optimize muscle protein synthesis. Though most people eat a combination of protein-containing foods at each meal, this infographic shows what 25 grams of protein from different sources looks like.⏎Researchers in Ireland recognized

  2. #2 peak

    assistance was required several times a day. For a description of what is included in each of these activities, please see the Glossary.<|endoftext|>Mad

  3. #3 peak

    is for adults 19 years of age or older. It does not apply to pregnant or breastfeeding women, because they have different nutritional requirements. Once you know what you need the most, the next logical step is to add supplements of those vitamins

  4. #4 peak

    Write About, I’m obviously partial to a publishing platform that incorporates visual writing ideas and customized user groups.⏎3. Communicate with parents.⏎Explain to parents what a blog is and why it can be a powerful tool. Ask for their feedback

  5. #5 peak

    patristic texts?⏎1. Did Mary merit to be the Mother of God?⏎2. Find out what the apocryphal Gospels are and of what value they are.⏎

  6. #6 peak

    and on-line practice.⏎GED Math - What to Expect⏎Are you considering earning your GED? Take away some of the fear of math by knowing what to expect.⏎How to Find Common Factors⏎An Efficient Method to Find Common Factors

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: an example of a simple example of a small example of a small example of an example of an example of an example of an example of an example of an example of a simple example of a small example of a small example of an example of

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

    or not and take the default route. Here’s an example of⏎mwhen for Monoids:⏎-- Important numbers!⏎xs = [

  2. #2 peak

    , but all recalls are listed in FDA’s weekly Enforcement Reports. See a list of vaccines that have been recalled in the past few years. ⏎Parents ought

  3. #3 peak

    test numbers. If you⏎determine that num is not divisible by 5 and 7, then increase⏎count by one.⏎Share your code⏎Show a friend, family member, or teacher what you've done!<|endoftext|>cigarette

  4. #4 peak

    citizens, including a more democratic government with greater powers given to civilians and less power given to military officials. There are many articles contained within this document, and here are some of the main ones:⏎1) Section 1 – The people shall be

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. L13:2498 excites
  2. L17:266 suppresses
  3. L17:2950 suppresses
  4. L13:2491 suppresses
  5. L18:2489 suppresses
  6. L18:1927 suppresses
  7. L8:1179 suppresses
  8. L15:695 suppresses
  9. L4:538 suppresses
  10. L17:2504 suppresses
  11. L9:2337 suppresses
  12. L10:2952 excites
  13. L5:798 excites
  14. L9:463 excites
  15. L15:2860 weakly excites
  16. L16:1118 weakly excites
  17. L12:2065 weakly suppresses
  18. L17:2548 weakly excites
  19. L11:1024 weakly suppresses
  20. L17:2316 weakly excites
  21. L12:1811 weakly excites
  22. L7:861 weakly excites 'of' after 'benefits' or 'features'
  23. L15:919 weakly excites
  24. L18:780 weakly suppresses

Feeds later layers

  1. L20:2654 suppresses
  2. L22:1309 suppresses
  3. L26:1091 suppresses
  4. L27:1444 suppresses
  5. L21:2023 suppresses
  6. L20:1413 suppresses
  7. L21:2259 suppresses
  8. L22:1151 suppresses
  9. L20:2433 suppresses
  10. L22:1729 suppresses
  11. L22:1593 suppresses
  12. L20:858 suppresses
  13. L25:882 suppresses
  14. L24:2381 suppresses
  15. L20:2216 suppresses
  16. L22:1900 suppresses
  17. L21:1089 suppresses
  18. L20:1065 suppresses
  19. L24:930 weakly suppresses
  20. L23:1468 weakly suppresses
  21. L26:558 weakly suppresses
  22. L26:3035 weakly suppresses
  23. L21:2372 weakly suppresses
  24. L21:2658 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 -6.62 and -6.191 values between -5.32 and -4.891 values between -4.89 and -4.462 values between -4.03 and -3.592 values between -3.59 and -3.162 values between -3.16 and -2.737 values between -2.73 and -2.306 values between -2.30 and -1.8719 values between -1.87 and -1.4437 values between -1.44 and -1.00128 values between -1.00 and -0.57899 values between -0.57 and -0.142273 values between -0.14 and 0.29359 values between 0.29 and 0.7279 values between 0.72 and 1.1633 values between 1.16 and 1.5919 values between 1.59 and 2.0213 values between 2.02 and 2.4510 values between 2.45 and 2.881 values between 2.88 and 3.322 values between 3.32 and 3.755 values between 3.75 and 4.182 values between 4.18 and 4.611 values between 4.61 and 5.041 values between 5.91 and 6.341 values between 6.34 and 6.771 values between 7.20 and 7.631 values between 7.63 and 8.071 values between 9.36 and 9.791 values between 10.22 and 10.66offnegativepositiveoften

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. vature
  2. straints
  3. tright
  4. tera
  5. UIControl
  6. processable
  7. arms
  8. tracts
  9. ่า
  10. \Entity

Suppresses

  1. examples
  2. 例子
  3. example
  4. 的例子
  5. 示
  6. Examples
  7. illustration
  8. exemple
  9. illustrate
  10. examples