Neuron Explorer

Qwen3-0.6B

"Please" opening a new line

A neuron in layer 18 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

The neuron fires on the word "Please" when it starts a new line, often in instructions such as "Please read" or "Please cite". It goes strongly negative on "Much" starting a sentence. Turning it up gave unrelated text about maths and weather.

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

    and learning is based on the understanding that children develop at different rates.⏎Please find at the bottom of this page a document called 'What to expect in the

  2. #2 peak

    have problems eating and getting enough fluids. Some need to have fluids given into a vein (IV) or have a feeding tube placed to get nutrition during treatment.⏎Please

  3. #3 peak

    ) Swarm of T-wave events during October 2005⏎Please cite this report as:⏎Global Volcanism Program, 2007

  4. #4 peak

    to induce trust, every action needs both a trusted introducer and a trusted witness.⏎Please google for "gerck trust" to find newer papers, applications and also comments

  5. #5 peak

    least colour differences. Polarisation, another property of EM radiation, can be detected by some organisms, with insects and cephalopods having the highest accuracy.⏎Please note, in this text, the focus has been on using EM waves to see

  6. #6 peak

    ⏎these crucial laws at all times. Only through constant review can we hope to⏎make guarding our tongues a habit and save ourselves from severe transgressions.⏎Please verbalize or⏎have in mind that you are studying this material as a merit

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: the time that a time is more time, as time is more time with the time that is more time, as time is more time with the time that is more time, as time is more time with the more time that is more time

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

    , moved by their crazed obsession with wiping away all traces of ancient civilizations, attacked it in August 2015, much the way they had previously done in Mosul, Nimrud, Hatra and Nin

  2. #2 peak

    to see it to completion. Much of it was completed by University of Minnesota students. You can see all of the

  3. #3 peak

    take them down. They are fortresses with layer after layer of protection, from fences to airlock-style entrances to biometric scanners.⏎Good thing, too. Much of the planet relies on these cloud fortresses, and will do so even more

  4. #4 peak

    to a conductor’s ears. Not unlike a musical composition, the intricately woven notes of a successful garden involve a high level of behind-the-scene detail - much more than simply water and sun.⏎So, are you ready to become a conductor

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. L17:2447 suppresses
  2. L17:1490 suppresses
  3. L14:2047 weakly excites
  4. L16:743 weakly suppresses
  5. L4:1344 weakly excites
  6. L16:2242 weakly excites
  7. L15:1554 weakly excites
  8. L15:1186 weakly excites
  9. L11:51 weakly suppresses
  10. L4:621 weakly excites
  11. L9:552 weakly suppresses
  12. L11:2124 weakly excites
  13. L4:1011 weakly excites
  14. L6:590 weakly suppresses
  15. L14:794 weakly suppresses
  16. L13:742 weakly suppresses
  17. L11:90 weakly suppresses
  18. L8:2104 weakly excites
  19. L17:732 weakly excites
  20. L5:1820 weakly suppresses
  21. L3:586 weakly excites
  22. L14:818 weakly excites
  23. L12:1827 weakly suppresses
  24. L4:1751 weakly excites

Feeds later layers

  1. L20:1389 weakly suppresses
  2. L19:1260 weakly excites
  3. L22:991 weakly excites
  4. L20:2477 weakly suppresses
  5. L19:463 weakly excites
  6. L23:1394 weakly suppresses
  7. L21:1790 weakly excites
  8. L25:998 weakly suppresses
  9. L19:2119 weakly excites
  10. L20:2876 weakly suppresses
  11. L23:863 weakly excites
  12. L20:1450 weakly excites
  13. L24:770 weakly suppresses
  14. L22:1630 weakly suppresses
  15. L25:1619 weakly suppresses
  16. L27:1004 weakly excites
  17. L19:599 weakly excites
  18. L20:3036 weakly excites
  19. L24:1405 weakly excites 'so' in 'so-called' and 'sores'
  20. L23:1584 weakly excites
  21. L21:939 weakly excites
  22. L23:543 weakly excites
  23. L20:1795 weakly suppresses
  24. L22:1377 weakly suppresses closing brackets and parentheses

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 -42.13 and -40.941 values between -36.17 and -34.981 values between -34.98 and -33.792 values between -23.08 and -21.891 values between -19.51 and -18.321 values between -18.32 and -17.131 values between -12.37 and -11.181 values between -8.80 and -7.611 values between -7.61 and -6.424 values between -6.42 and -5.238 values between -5.23 and -4.046 values between -4.04 and -2.8516 values between -2.85 and -1.6647 values between -1.66 and -0.473735 values between -0.47 and 0.7281 values between 0.72 and 1.911 values between 4.29 and 5.48offnegativepositiveoften

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. zie
  2. 起
  3. Convertible
  4. iete
  5. uate
  6. fauc
  7. lectic
  8. genesis
  9. 三条
  10. 眼角

Suppresses

  1. ider
  2. .echo
  3. ifndef
  4. Older
  5. ATER
  6. _Code
  7. adar
  8. iler
  9. ỗ
  10. uben