Neuron Explorer

Qwen3-0.6B

articles after building and room nouns

A neuron in layer 23 of Qwen3-0.6B. Its strongest snippets are fairly consistent: several of them peak on the same token. It is featured, so it has steering outputs.

What this neuron does

This neuron fires on small words like 'a' and 'the' that come right after a place or building noun, such as 'school', 'hall' or 'casino'. Its outputs point toward words like 'library' and 'hall'. Turning it up made the model keep talking about libraries, halls and similar buildings.

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

    recording their heart rates and blood pressure during varying exercise intensities. The very nature of this activity meant that we had to leave the standard classroom and venture into the school gymnasium.⏎With this is mind I wanted to ensure that the data that

  2. #2 peak

    2nd class stone or brick cottage with a thatched roof. It had three front windows and two rooms occupied. There were four out-offices a stable, a

  3. #3 peak

    by fire in October 1863.⏎For the next 30 years the court moved into a number of temporary quarters: a school, a remodeled jail, a two-story building on the west side of the square and a Baptist

  4. #4 peak

    Looming over the busy port, the centrepiece of Dover Castle is the Great Tower - the largest keep in Britain - a four storey building housing a basement,

  5. #5 peak

    sing together, the happier we’ll be.⏎I loved singing The More We Get Together when I was a little girl. I remember singing it at school, in the car, at

  6. #6 peak

    by river Po’s⏎tributaries and by important old routes. The complexes’ panoramic and prominent locations on hills or mountains and their subdivisions⏎into chapels, let them become territorial reference points easy to identify; we don’t have to forget

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: there are so many things that we can do to fix a world that is so destructive and full of 9/22...n...n...n...n...n...n...n...n...n...n...n...

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

    towards parking or bike lanes? Bus lanes or street trees? Rain gardens or sidewalks with café dining? The answer likely varies in different situations, but how do we make those

  2. #2 peak

    ano, the bridge was completed in 1922. Paolo Alcazaren describes, “the piers of the bridge were festooned with statuary of mermen on dolphins. The bridge balustrades were ornately molded

  3. #3 peak

    Candles and devices using this oil-burning technique were the main means used to light homes, employed in candelabras, wall sconces, and elegant chandeliers.⏎It wasn't until the Victorian era that oil lamps improved, thanks

  4. #4 peak

    . Hang a long bow and a quiver of arrows conspicuously in your hall or library, and you will soon discover that no exquisite painting or bit of statuary will receive from guests more attention than will be accorded to these ancient weapons

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. L22:1107 suppresses
  2. L22:1565 suppresses
  3. L22:938 excites
  4. L19:1522 suppresses
  5. L21:3051 suppresses
  6. L21:1573 excites
  7. L21:2866 excites
  8. L21:2481 weakly suppresses
  9. L21:1680 weakly suppresses
  10. L17:406 weakly suppresses
  11. L19:1733 weakly suppresses
  12. L21:1413 weakly excites
  13. L21:172 weakly excites
  14. L15:1352 weakly suppresses
  15. L6:2232 weakly excites
  16. L9:2591 weakly excites
  17. L16:1308 weakly excites
  18. L22:323 weakly suppresses
  19. L3:187 weakly excites
  20. L20:1807 weakly excites
  21. L9:807 weakly suppresses
  22. L18:1716 weakly excites institution place nouns like school
  23. L7:925 weakly excites
  24. L0:2470 weakly excites

Feeds later layers

  1. L26:2920 excites
  2. L27:886 weakly excites
  3. L25:2476 weakly excites
  4. L25:2366 weakly excites
  5. L26:2579 weakly excites
  6. L26:1816 weakly excites
  7. L26:995 weakly excites
  8. L25:2525 weakly excites
  9. L27:2181 weakly excites
  10. L25:498 weakly suppresses
  11. L25:2423 weakly suppresses
  12. L27:1824 weakly excites
  13. L24:690 weakly excites
  14. L25:1975 weakly excites
  15. L24:2828 weakly excites
  16. L24:2534 weakly suppresses
  17. L24:1480 weakly excites
  18. L26:2812 weakly excites
  19. L25:2867 weakly excites
  20. L25:2689 weakly excites
  21. L24:2962 weakly suppresses
  22. L24:2893 weakly excites
  23. L24:686 weakly excites
  24. L26:2927 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 -12.46 and -10.662 values between -10.66 and -8.864 values between -8.86 and -7.068 values between -7.06 and -5.2618 values between -5.26 and -3.4539 values between -3.45 and -1.652524 values between -1.65 and 0.151193 values between 0.15 and 1.9545 values between 1.95 and 3.7519 values between 3.75 and 5.5513 values between 5.55 and 7.3519 values between 7.35 and 9.163 values between 9.16 and 10.964 values between 10.96 and 12.762 values between 12.76 and 14.561 values between 14.56 and 16.361 values between 16.36 and 18.164 values between 19.96 and 21.771 values between 21.77 and 23.571 values between 25.37 and 27.171 values between 27.17 and 28.971 values between 28.97 and 30.771 values between 30.77 and 32.571 values between 32.57 and 34.381 values between 57.79 and 59.59offnegativepositiveoften

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. library
  2. 图书馆
  3. Library
  4. hall
  5. Library
  6. 库
  7. libraries
  8. library
  9. garage
  10. 车库

Suppresses

  1. 皴
  2. ijk
  3. emics
  4. 钩
  5. 两岸
  6. 友情链接
  7. ول
  8. clarsimp
  9. uuid
  10. fix