Neuron Explorer

Qwen3-0.6B

end of a document

A neuron in layer 11 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 is weak and fires at the very end of a piece of text, where the model sees the document-ending marker or a final line break. Turning it up gave unrelated text, so the steering doesn't add much.

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

    (6-8) Show all Grade Level⏎Resource Type: Activities⏎Results 1 - 7 of 7 matches⏎From Grid to Home⏎Lisa Gardiner, National Center for Atmospheric Research; Marie

  2. #2 peak

    utes to Heinrich Rudolf Hertz, film critic Francois Truffaut and Charles Dickens, who was born on February 7 1812.<|endoftext|>Combating AIDS Around the Globe⏎Lead story from the February Centerpoint⏎In

  3. #3 peak

    America continental shelf (Fig. 1).⏎Additional publication details⏎Pulley reef: a deep photosynthetic coral reef on the West Florida Shelf, USA<|endoftext|>Good morning lovely people!⏎We’re at the end of another week of home learning

  4. #4 peak

    chance to explore STEM topics in a way that deepens their knowledge while also developing 21st-century skills.⏎Read the rest of this entry »⏎Filed under: Gaming, Services, Software, 3 million dollar grant, Associate Professor,

  5. #5 peak

    input=gadm36_dissolved_nocats output=gadm36_dissolved_cats option=add⏎Figure 3 shows the final categorized layer.<|endoftext|>Despite the good knowledge and practices among the Nepalese migrant women, the breastfeeding

  6. #6 peak

    movies made without consuming any films. It was transferred to six metropolises in the USA via Internet. Toy Story hinted the coming of the era of digital movies<|endoftext|>Short overview of Machine Learning techniques⏎In this article we cover four major types of

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: "What is the purpose of the two different types of time, one for the past and one for the future?" This question is about the purpose of the time types, leading to the conclusion that the question is answered by the two 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

    archive.jpr.org.uk/10.1177%2F1359104505053

  2. #2 peak

    two different numbers cant have the same cube). Therefore, by Definition 1, is one-to-one....⏎View Full Document⏎- Spring '09<|endoftext|>part of speech: noun⏎definition 1:

  3. #3 peak

    an-RM⏎Am Ind Hyg Assoc J 1980 Oct; 41(10):758-765⏎The effectiveness of open versus closed-face c

  4. #4 peak

    to get the integral into a reasonable form⏎Doing u-substitution twice (second time with w)⏎⇐ Use this menu to view and help create subtitles for this video in many different languages. You'll probably want to hide YouTube's captions if using

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. L10:2423 excites
  2. L10:1932 weakly suppresses
  3. L10:58 weakly excites
  4. L10:196 weakly excites
  5. L10:710 weakly suppresses
  6. L7:1070 weakly suppresses
  7. L2:1037 weakly excites
  8. L7:61 weakly suppresses
  9. L3:194 weakly suppresses
  10. L9:2332 weakly suppresses
  11. L0:2863 weakly excites
  12. L10:72 weakly excites
  13. L6:843 weakly excites
  14. L3:547 weakly suppresses
  15. L3:329 weakly excites
  16. L0:876 weakly excites
  17. L1:1340 weakly suppresses
  18. L6:2345 weakly excites
  19. L6:1214 weakly suppresses
  20. L7:1663 weakly suppresses
  21. L10:701 weakly suppresses everyday objects and animals, mid-word
  22. L7:63 weakly excites
  23. L2:542 weakly suppresses
  24. L2:1919 weakly excites

Feeds later layers

  1. L13:969 excites
  2. L12:2138 excites
  3. L15:2544 excites
  4. L16:2510 excites
  5. L12:326 suppresses
  6. L15:2249 suppresses
  7. L12:1900 weakly suppresses end of document boundaries
  8. L14:571 weakly excites
  9. L14:2525 weakly excites
  10. L12:1439 weakly excites
  11. L12:849 weakly excites
  12. L16:1251 weakly suppresses
  13. L17:1980 weakly suppresses
  14. L15:1088 weakly excites
  15. L22:163 weakly excites
  16. L23:1891 weakly excites
  17. L12:1199 weakly suppresses
  18. L16:2814 weakly excites
  19. L22:1685 weakly suppresses
  20. L12:1964 weakly excites
  21. L13:595 weakly excites
  22. L13:1624 weakly suppresses
  23. L12:1690 weakly suppresses
  24. L15:101 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.

2 values between -0.64 and -0.603 values between -0.60 and -0.574 values between -0.57 and -0.548 values between -0.54 and -0.516 values between -0.51 and -0.4711 values between -0.47 and -0.4416 values between -0.44 and -0.4117 values between -0.41 and -0.3833 values between -0.38 and -0.3450 values between -0.34 and -0.3164 values between -0.31 and -0.28115 values between -0.28 and -0.25140 values between -0.25 and -0.21161 values between -0.21 and -0.18207 values between -0.18 and -0.15255 values between -0.15 and -0.12292 values between -0.12 and -0.09318 values between -0.09 and -0.05360 values between -0.05 and -0.02386 values between -0.02 and 0.01310 values between 0.01 and 0.05276 values between 0.05 and 0.08236 values between 0.08 and 0.11182 values between 0.11 and 0.14139 values between 0.14 and 0.1883 values between 0.18 and 0.2175 values between 0.21 and 0.2452 values between 0.24 and 0.2741 values between 0.27 and 0.3024 values between 0.30 and 0.3418 values between 0.34 and 0.378 values between 0.37 and 0.405 values between 0.40 and 0.435 values between 0.43 and 0.473 values between 0.47 and 0.501 values between 0.50 and 0.532 values between 0.63 and 0.66offnegativepositiveoften

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. afford
  2. ease
  3. fl
  4. nil
  5. heit
  6. 跤
  7. arith
  8. ifndef
  9. NSS
  10. nia

Suppresses

  1. obody
  2. 另一个
  3. ーター
  4. 了他的
  5. 观光
  6. _mirror
  7. _solution
  8. ivor
  9. 绲
  10. ți