Neuron Explorer

Qwen3-0.6B

document boundary end-of-text token

A neuron in layer 15 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

Fires on the hidden 'end of text' marker that separates one document from the next, placed in the middle of a passage where a new, unrelated text begins. Its negative side is on pieces of file names or code tokens. Turning it up gave text with no clear link to the trigger.

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

    are carried over to the new data directory.⏎Stop the existing MySQL instance using the data directory. This must be a clean shutdown<|endoftext|>On April 16, 1853, the first train left

  2. #2 peak

    . Shapiro added that human hunters could have exterminated species that were already weakened by climatic and environmental changes.⏎The smell of bison DNA⏎The mitochondrial DNA<|endoftext|>Comfrey - Symphytum officinale - 5 Seeds - Medic

  3. #3 peak

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

  4. #4 peak

    been detected about 4 to 6 times more frequently in the high-use areas than elsewhere.⏎In addition to its studies of ambient ground water, USGS is<|endoftext|>

  5. #5 peak

    code<|endoftext|>Jet engines, or gas turbines, suck in air at the front with a fan

  6. #6 peak

    different aspect of the question of uniqueness is addressed in the spotlight text The many ways of building an empty, unchanging universe.⏎Synonyms: uniqueness theorems<|endoftext|>(b) A small bone that is part of the partition between the nostrils in

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 things I have been thinking about, the things that I have been thinking about, the things that I have been thinking about ... the things that I have been thinking about ... ... the ... the ... the ... the ... the ... ... the

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

    output=gadm36_dissolved_nocats option=del⏎Then, add unique categories to them.⏎v.category input=gadm36_dissolved_nocats output=gadm36_dissolved_cats option=add⏎Figure

  2. #2 peak

    : Extract Compressed Drupal⏎Extract the Drupal zip file in the /var/www/html directory using following command:⏎# cd /var/www/html⏎# unzip drupal-7.33.zip⏎It will create a folder as /var/www

  3. #3 peak

    you can see the other students’ posts. Submit your initial post(s) early in the module.⏎See the Discussion Board Guidelines and Rubrics⏎Download Discussion Board Guidelines and Rubrics.⏎Reh, F. J. (2018

  4. #4 peak

    .⏎typedef void * model_3d_ref;⏎model_3d_ref load_model_from_disk(const char * absolute_file_path);⏎void draw_model(model_3d_ref the_model, float

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. L13:2924 strongly suppresses
  2. L12:689 excites
  3. L12:1368 excites
  4. L14:1294 excites
  5. L14:2719 suppresses
  6. L14:1078 excites
  7. L14:1379 suppresses
  8. L14:1471 excites
  9. L13:1385 suppresses
  10. L7:143 excites
  11. L14:2219 excites
  12. L11:2187 excites
  13. L14:2280 excites
  14. L13:2175 excites
  15. L13:879 excites
  16. L10:2259 excites
  17. L13:1268 excites
  18. L11:707 excites
  19. L13:2827 suppresses
  20. L14:2277 suppresses
  21. L9:1056 excites
  22. L14:2336 excites
  23. L14:2406 suppresses
  24. L11:647 suppresses

Feeds later layers

  1. L16:2777 strongly suppresses
  2. L17:1098 strongly suppresses
  3. L19:568 suppresses
  4. L16:1170 suppresses
  5. L16:2174 suppresses
  6. L16:1476 suppresses
  7. L16:1375 excites
  8. L16:343 suppresses
  9. L16:878 weakly suppresses
  10. L19:2456 weakly excites
  11. L19:1623 weakly excites
  12. L19:1037 weakly excites
  13. L18:2936 weakly excites
  14. L18:322 weakly suppresses
  15. L19:2260 weakly excites
  16. L19:1106 weakly excites
  17. L16:1362 weakly suppresses
  18. L22:135 weakly excites
  19. L17:1419 weakly excites
  20. L19:280 weakly excites
  21. L18:1917 weakly suppresses
  22. L17:274 weakly suppresses
  23. L18:2210 weakly excites
  24. L17:549 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 -2.01 and -1.871 values between -1.32 and -1.181 values between -1.18 and -1.041 values between -1.04 and -0.901 values between -0.62 and -0.481 values between -0.48 and -0.3413 values between -0.34 and -0.20157 values between -0.20 and -0.061800 values between -0.06 and 0.081454 values between 0.08 and 0.22370 values between 0.22 and 0.3685 values between 0.36 and 0.5014 values between 0.50 and 0.632 values between 0.63 and 0.771 values between 0.77 and 0.911 values between 0.91 and 1.051 values between 1.47 and 1.611 values between 1.75 and 1.891 values between 2.17 and 2.311 values between 2.58 and 2.721 values between 3.42 and 3.56offnegativepositiveoften

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. udence
  2. ński
  3. ję
  4. unintention
  5. uş
  6. gomery
  7. .toHexString
  8. unos
  9. mers
  10. PLEX

Suppresses

  1. 斫
  2. 他自己
  3. 俎
  4. 所以他
  5. 着他
  6. 他是一个
  7. 립
  8. hamstring
  9. 他曾
  10. 硎