Neuron Explorer

Qwen3-0.6B

'to' after 'what' or 'where' (what to do)

A neuron in layer 5 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 'to' when it follows 'know what' or 'where', as in 'know what to do' or 'where to start'. It is pointing at the start of a phrase that says how to act or what to pick. Turning it up made the model write about what to say or what to do next, which fits the trigger reasonably well.

Written by Claude from this neuron's activations and steering outputs. Confidence: high. 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

    our lives is to improve our experiences.⏎Improvement requires change, and change requires work. And with encompassing holistic changes, most people are challenged to know where to start.⏎In epistemology, the philosophical study of knowledge, “tabula

  2. #2 peak

    including health insurance.⏎This is a guest post by Murray Newlands, the CEO and Founder of Influence People. Influence People<|endoftext|>It can be challenging to know what to do to get your middle school child ready for all of the activities, requirements and

  3. #3 peak

    because we dont have the ability to handle truth. Truth is to much or to bright for us because it can be interpreted wrongly, and we wouldnt know what to do with it either.,⏎5. Lines 5 and 6 provide an example

  4. #4 peak

    your application and see how prepared you are for college courses. Understanding the timing breakdown of the SAT and ACT will help you practice under realistic testing conditions and know what to expect on test day. Understanding how your scores are

  5. #5 peak

    So, it should be inevitable that companies start making headway towards bringing a revolutionary technology out of the hospitals and into the markets.⏎It’s hard to know what to call a device that would

  6. #6 peak

    library supports. Invoking the tag library imports custom processing and actions into the Java environment so a Java runtime environment like JRun 3.0 will know what to do with such tags as they're recognized. Tag libraries are usually packaged as Java

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: let's go! Wait, I can't do that. Wait, let's go! Okay, let's see! Let's go! Wait, let's go! Okay, I'm not sure about this

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

    ideas you'd like to contribute, we'd love to hear from you.⏎Find the latest in professional publications, learn new techniques and strategies, and find out how you can connect with other literacy professionals.⏎Teacher Resources by Grade⏎|1st -

  2. #2 peak

    loose with our terms, but that just makes it more confusing.⏎You ought to be able to think these through - take a shot and we'll tell you where you're on and off-target.⏎Ok, thats a good idea.⏎1. Unfortunately

  3. #3 peak

    given/known data If an athlete jumped 2 feet high and left the ground at an angle of 20 degrees with respect to the horizontal, how fast was the athlete going in the forward (positive horizontal) and upward (positive vertical)

  4. #4 peak

    ideas you'd like to contribute, we'd love to hear from you.⏎Find the latest in professional publications, learn new techniques and strategies, and find out how you can connect with other literacy professionals.⏎Teacher Resources by Grade⏎|1st -

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. L3:393 excites
  2. L4:1936 weakly excites
  3. L4:2855 weakly excites
  4. L1:2354 weakly excites
  5. L3:368 weakly excites
  6. L4:182 weakly suppresses
  7. L4:423 weakly excites
  8. L0:599 weakly suppresses
  9. L2:899 weakly suppresses
  10. L1:1779 weakly excites
  11. L2:410 weakly suppresses
  12. L4:12 weakly suppresses
  13. L0:2931 weakly suppresses
  14. L2:2551 weakly suppresses
  15. L3:2369 weakly suppresses
  16. L1:1515 weakly excites
  17. L3:456 weakly suppresses
  18. L3:842 weakly suppresses
  19. L1:2440 weakly suppresses
  20. L2:1106 weakly excites
  21. L4:87 weakly excites
  22. L3:843 weakly excites
  23. L4:443 weakly excites
  24. L1:2083 weakly suppresses

Feeds later layers

  1. L8:2795 excites
  2. L6:781 excites
  3. L7:1651 excites
  4. L21:1427 excites
  5. L10:2169 weakly excites
  6. L17:1822 weakly excites 'should' in advice and claims
  7. L19:2844 weakly excites
  8. L7:2459 weakly excites
  9. L15:955 weakly excites
  10. L8:973 weakly excites
  11. L11:1514 weakly suppresses
  12. L22:798 weakly suppresses
  13. L15:522 weakly excites
  14. L15:1986 weakly excites
  15. L12:2544 weakly suppresses
  16. L9:556 weakly excites
  17. L19:2472 weakly excites
  18. L6:1921 weakly suppresses
  19. L8:632 weakly excites
  20. L10:1121 weakly excites 'to' or verb after 'what' or 'whether'
  21. L9:1789 weakly suppresses
  22. L13:1687 weakly excites
  23. L17:2171 weakly suppresses
  24. L8:1169 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.

7 values between -0.82 and -0.714 values between -0.71 and -0.605 values between -0.60 and -0.4925 values between -0.49 and -0.3857 values between -0.38 and -0.27268 values between -0.27 and -0.16815 values between -0.16 and -0.051698 values between -0.05 and 0.06703 values between 0.06 and 0.17241 values between 0.17 and 0.2851 values between 0.28 and 0.3913 values between 0.39 and 0.505 values between 0.50 and 0.615 values between 0.61 and 0.723 values between 0.72 and 0.831 values between 0.83 and 0.941 values between 1.05 and 1.161 values between 1.49 and 1.602 values between 1.60 and 1.711 values between 1.71 and 1.821 values between 2.92 and 3.031 values between 3.47 and 3.58offnegativepositiveoften

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. 应当
  2. 最合适
  3. should
  4. altung
  5. Should
  6. ала
  7. abilidade
  8. nels
  9. кал
  10. 应该

Suppresses

  1. ored
  2. ouri
  3. orary
  4. 蝠
  5. ChangeEvent
  6. \V
  7. pressive
  8. cono
  9. ,dim
  10. our