Neuron Explorer

Qwen3-0.6B

end of document marker

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 the invisible marker that tells the model one document has ended and a new, unrelated one begins. Its negative side reacts to odd line breaks and quotes, and steering did not produce a clear pattern.

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

    0240.<|endoftext|>By 2003, plenty of data suggested that pesticide drift was harming

  2. #2 peak

    can be used to<|endoftext|>| Trisha Sertori |⏎JUGA, Indonesia (Reuters) –

  3. #3 peak

    rounded to the nearest integer.<|endoftext|>Christine Duncan, MD⏎Sickle cell disease (SCD) affects as

  4. #4 peak

    ,0<|endoftext|>Guidelines for Restoring Cultural Landscapes⏎Identifying, retaining and preserving

  5. #5 peak

    , 2012<|endoftext|>Some weather conditions are simply not fun to ride in. As cyclists, windy days

  6. #6 peak

    . On<|endoftext|>Anatoly Lunacharsky On Literature and Art 1909

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 don't know where I'm going, but I know that I'm getting somewhere." That is, I'm not necessarily *knowing* where I'm going, but *knowing that I'm getting somewhere." That's a

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

    example sentence"⏎- S: (n) fire (the event of something burning (often destructive

  2. #2 peak

    True, obj)". If it failed, you might return "(False, reason)". Often, you can

  3. #3 peak

    a matrix⏎Find nth maximum⏎Matlab Basics II - Create a vector with a repeated entry

  4. #4 peak

    for<|endoftext|>- Agriculture in NSW is important for the state’s economy, industries, communities and

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. L18:1288 excites
  2. L16:211 suppresses
  3. L18:1128 excites
  4. L8:18 weakly suppresses
  5. L18:2790 weakly suppresses
  6. L17:36 weakly excites
  7. L9:1788 weakly suppresses
  8. L17:1237 weakly suppresses
  9. L2:3058 weakly excites
  10. L8:26 weakly excites
  11. L8:562 weakly suppresses
  12. L7:1022 weakly excites
  13. L15:1914 weakly suppresses
  14. L12:227 weakly excites
  15. L1:313 weakly excites
  16. L18:738 weakly excites
  17. L12:2870 weakly suppresses
  18. L17:1205 weakly excites
  19. L16:2775 weakly suppresses
  20. L6:1619 weakly suppresses
  21. L13:1411 weakly excites
  22. L15:1121 weakly suppresses
  23. L12:465 weakly suppresses
  24. L4:85 weakly suppresses

Feeds later layers

  1. L20:508 excites
  2. L20:431 excites
  3. L27:267 weakly suppresses
  4. L27:2157 weakly suppresses
  5. L20:2214 weakly excites
  6. L21:2895 weakly suppresses
  7. L22:2524 weakly suppresses
  8. L23:1481 weakly excites
  9. L23:1713 weakly suppresses
  10. L22:1633 weakly suppresses
  11. L22:355 weakly excites
  12. L20:2602 weakly suppresses
  13. L25:931 weakly suppresses
  14. L27:874 weakly suppresses
  15. L27:34 weakly excites
  16. L25:67 weakly suppresses
  17. L27:1571 weakly suppresses
  18. L25:113 weakly suppresses
  19. L20:1599 weakly suppresses
  20. L27:1238 weakly suppresses
  21. L27:669 weakly suppresses
  22. L24:1722 weakly suppresses
  23. L22:2310 weakly suppresses
  24. L27:662 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.

2 values between -13.43 and -12.901 values between -11.33 and -10.801 values between -10.28 and -9.752 values between -9.75 and -9.221 values between -9.22 and -8.701 values between -8.70 and -8.173 values between -8.17 and -7.651 values between -7.12 and -6.603 values between -6.60 and -6.071 values between -6.07 and -5.542 values between -5.54 and -5.023 values between -5.02 and -4.498 values between -4.49 and -3.972 values between -3.97 and -3.445 values between -3.44 and -2.917 values between -2.91 and -2.3917 values between -2.39 and -1.8617 values between -1.86 and -1.3444 values between -1.34 and -0.81391 values between -0.81 and -0.292584 values between -0.29 and 0.24773 values between 0.24 and 0.7733 values between 0.77 and 1.292 values between 1.29 and 1.822 values between 1.82 and 2.341 values between 2.34 and 2.871 values between 7.08 and 7.60offnegativepositiveoften

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. ism
  4. uddy
  5. opr
  6. essor
  7. esso
  8. ov
  9. 同事
  10. Invoker

Suppresses

  1. .Optional
  2. RecognitionException
  3. bones
  4. ilha
  5. OfWork
  6. icates
  7. plied
  8. (Int
  9. *)&
  10. →catch