Neuron Explorer

Qwen3-0.6B

end-of-document boundary 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

This neuron responds most strongly at the invisible marker that signals the end of one web document and the start of another, especially when the text before it stops mid-thought. Turning it up gave wandering, unrelated text, so the steering does not show a clear effect.

Written by Claude from this neuron's activations and steering outputs. Confidence: medium. Think it is wrong? Flag it (opens an email to the maker with the neuron already filled in).

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

    an error has occurred. The ldexpf() and ldexpl() functions do not set errno.⏎See attributes(5) for descriptions of the following attributes:<|endoftext|>The

  2. #2 peak

    phases to spot malfunctions and flaws. It often includes stress testing to estimate the product lifespan. The end goal is a product that meets the client’s quality specifications<|endoftext|>Diploid cells (WI-38, M

  3. #3 peak

    enforcement agencies keep of known "disapproved" files that they want to search people's hard drives for. In these applications, keeping a database of the actual file<|endoftext|>Pre

  4. #4 peak

    formulas and the questions. Students who work on their concepts would always excel in mathematical problems when compared to those who focus on memorizing solutions. Problems like limit solving<|endoftext|>How to dissolve polygons spatially in GRASS GIS⏎You can use the v

  5. #5 peak

    , the range and severity of symptoms and physical characteristics may vary from case to case.⏎LEOPARD is an acronym for the characteristic abnormalities associated with the disorder: L stands for (L)entigines (multiple black or dark brown spots on

  6. #6 peak

    mann machines (RBM), deep belief networks (DBN) and deep convolutional neural networks (CNN). In recent years, CNN-based methods have gained popularity<|endoftext|>typedef unsigned int size_t;⏎This type is used to hold size definitions. Since

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 can be 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

    I walk to the beach.⏎- Action 2⏎We collect all sorts of seashells.⏎- Action 3⏎I spot something dark and shiny.⏎- Action 4⏎I grab it before a wave splashes it away.⏎-

  2. #2 peak

    5 to 88 percent. MENA achieved the same increase in 2 decades: from 1990 to 2010.2⏎However, it is important to note that regional statistics tend to mask the fact that

  3. #3 peak

    those declarations. Humans read code, and humans infer things from the code that will be necessary to work on it.⏎typedef void * model_3d_ref;⏎model_3d_ref load_model_from_disk(const char * absolute_file_path);⏎void

  4. #4 peak

    modification, would have been destructive of the theory and would have made the<|endoftext|>It is not good for the health of a tree to build up soil around its base.⏎In the Chicago suburbs and near Galena, I have planted trees in soil that

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. L10:984 excites
  2. L11:45 suppresses
  3. L7:52 suppresses
  4. L14:2884 excites
  5. L12:73 weakly excites
  6. L12:605 weakly excites
  7. L14:89 weakly suppresses
  8. L13:81 weakly suppresses
  9. L9:891 weakly suppresses
  10. L10:36 weakly excites
  11. L1:87 weakly excites
  12. L13:1727 weakly suppresses
  13. L13:1707 weakly excites
  14. L6:17 weakly excites
  15. L1:1735 weakly suppresses
  16. L14:374 weakly suppresses
  17. L9:25 weakly excites
  18. L8:2 weakly excites
  19. L13:370 weakly suppresses
  20. L10:38 weakly suppresses
  21. L13:271 weakly suppresses
  22. L2:1737 weakly suppresses
  23. L9:2447 weakly excites
  24. L13:121 weakly excites

Feeds later layers

  1. L16:225 excites
  2. L19:31 suppresses
  3. L17:114 suppresses
  4. L18:138 suppresses
  5. L18:325 weakly excites
  6. L17:363 weakly excites
  7. L19:232 weakly suppresses
  8. L16:860 weakly suppresses
  9. L25:156 weakly suppresses
  10. L17:717 weakly excites
  11. L25:670 weakly excites
  12. L16:196 weakly excites
  13. L16:1341 weakly excites
  14. L21:118 weakly excites
  15. L17:1054 weakly suppresses
  16. L16:402 weakly suppresses
  17. L19:1797 weakly excites
  18. L18:1715 weakly excites
  19. L16:2972 weakly excites
  20. L21:1059 weakly excites
  21. L19:1326 weakly excites
  22. L16:1829 weakly suppresses
  23. L20:36 weakly suppresses
  24. L22:1590 weakly excites

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 -3.89 and -3.721 values between -3.54 and -3.371 values between -3.37 and -3.194 values between -3.19 and -3.021 values between -3.02 and -2.844 values between -2.84 and -2.672 values between -2.67 and -2.494 values between -2.49 and -2.3210 values between -2.32 and -2.148 values between -2.14 and -1.9713 values between -1.97 and -1.794 values between -1.79 and -1.6221 values between -1.62 and -1.4411 values between -1.44 and -1.2727 values between -1.27 and -1.0934 values between -1.09 and -0.9246 values between -0.92 and -0.7465 values between -0.74 and -0.57104 values between -0.57 and -0.39178 values between -0.39 and -0.22404 values between -0.22 and -0.04726 values between -0.04 and 0.14681 values between 0.14 and 0.31488 values between 0.31 and 0.49341 values between 0.49 and 0.66260 values between 0.66 and 0.84163 values between 0.84 and 1.01101 values between 1.01 and 1.1979 values between 1.19 and 1.3650 values between 1.36 and 1.5420 values between 1.54 and 1.7113 values between 1.71 and 1.8910 values between 1.89 and 2.0612 values between 2.06 and 2.246 values between 2.24 and 2.416 values between 2.41 and 2.593 values between 2.59 and 2.763 values between 2.76 and 2.943 values between 2.94 and 3.11offnegativepositiveoften

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. allee
  2. NOW
  3. .removeEventListener
  4. �
  5. 都被
  6. występ
  7. 鹃
  8. были
  9. były
  10. 끔

Suppresses

  1. (**
  2. plies
  3. 蚁
  4. JK
  5. idel
  6. anst
  7. super
  8. ants
  9. ortion
  10. inaire