Neuron Explorer

Qwen3-0.6B

end of document boundary token

A neuron in layer 21 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 fires on the invisible marker the training text uses to show that one document has ended and another begins. When turned up, the model tended to start writing code-style backtick quotes, which is a weak match for what the neuron seems to predict.

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

    er is 800 mum x 150 mum, and the delay time from the clock to Sum31 measured 0.9 ns.<|endoftext|>Hard work and smart work are two distinct approaches to achieving a goal. Hard work

  2. #2 peak

    the GUI which displays and analyze the possibility of fire happening. The system can display the fire location and provides early warning to allow occupants to escape the building safely.<|endoftext|>Ross and Wilson Anatomy and Physiology in Health and Illness : Anne Waugh :

  3. #3 peak

    shows promise for the future of electric vehicles and the aerospace industry. This research is supported by the Grainger Center for Electric Machinery and Electromechanics.<|endoftext|>This story now⏎IN History & Culture ON 20 Oct, 2

  4. #4 peak

    that already have been demonstrated experimentally. Thus, the results presented here suggest that such improvements might be realized in the near term for important, special-purpose applications.<|endoftext|>Communitarianism as a group of related but distinct philosphies began in

  5. #5 peak

    25% for conditions 1, 2 and 3, respectively. Thus, monocular depth cues aid the detection of moving objects by moving observers.<|endoftext|>Cut flowers need proper care to prolong the length of time you can enjoy them.

  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: what is the result of the sum of the number of all possible permutations of the numbers 1,2,3,4,5,6,7,8,9 on the number 8. In the answer, I just have

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

    .codeguru.com/algorithms/factorial.shtml I don't happen to think that the code there is easily understandable though...⏎January 10th, 2004 01:3

  2. #2 peak

    1|⏎|-||Announced||May 4, 1990|⏎|-||Restored||August 21, 1991|⏎|-||Total||64,589 km2 (124

  3. #3 peak

    as 14,000 years. It came as a shock to the couple, who had recently moved from Washington state to their new home in Vegas.⏎A couple of days later, police and investigation officers showed up on their door,

  4. #4 peak

    See Speed Force Powers section.) However, the Speed Force is not the source from which other DC characters with super speed such as Superman or Captain Marvel derive their powers.⏎Writer John Byrne maintained modest abilities for the speedster character Danny Hilltop in his

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. L20:1889 excites
  2. L13:1325 excites
  3. L17:424 weakly suppresses sentence-ending full stops
  4. L6:16 weakly excites
  5. L5:2174 weakly excites
  6. L10:725 weakly suppresses
  7. L3:493 weakly excites
  8. L7:2083 weakly suppresses
  9. L6:13 weakly excites
  10. L19:2031 weakly excites line breaks in web footers and metadata
  11. L2:768 weakly suppresses
  12. L18:5 weakly suppresses
  13. L4:327 weakly excites
  14. L19:997 weakly suppresses
  15. L3:1175 weakly excites
  16. L20:8 weakly suppresses
  17. L10:923 weakly suppresses
  18. L20:44 weakly excites
  19. L9:1376 weakly excites
  20. L19:2057 weakly suppresses
  21. L19:501 weakly suppresses
  22. L9:1489 weakly suppresses
  23. L19:2250 weakly suppresses
  24. L10:952 weakly excites

Feeds later layers

  1. L26:1853 excites
  2. L25:711 excites
  3. L26:2738 excites
  4. L25:878 excites
  5. L23:577 weakly excites
  6. L27:2391 weakly excites
  7. L25:255 weakly excites pieces of split scientific or place names
  8. L25:1880 weakly suppresses
  9. L26:47 weakly excites
  10. L25:2541 weakly excites 'set' as in 'set up'
  11. L25:1914 weakly excites
  12. L26:2284 weakly excites
  13. L27:2964 weakly excites
  14. L25:2703 weakly suppresses
  15. L23:3020 weakly suppresses
  16. L23:3025 weakly excites
  17. L26:1082 weakly excites
  18. L23:793 weakly suppresses
  19. L26:1188 weakly excites
  20. L23:374 weakly suppresses
  21. L25:484 weakly suppresses
  22. L25:443 weakly excites
  23. L24:369 weakly excites
  24. L25:2207 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 -16.88 and -16.031 values between -13.50 and -12.661 values between -11.82 and -10.972 values between -10.97 and -10.133 values between -10.13 and -9.293 values between -9.29 and -8.444 values between -8.44 and -7.603 values between -7.60 and -6.763 values between -6.76 and -5.916 values between -5.91 and -5.074 values between -5.07 and -4.2310 values between -4.23 and -3.385 values between -3.38 and -2.5412 values between -2.54 and -1.7053 values between -1.70 and -0.852513 values between -0.85 and -0.011252 values between -0.01 and 0.8414 values between 0.84 and 1.6812 values between 1.68 and 2.522 values between 2.52 and 3.371 values between 3.37 and 4.211 values between 6.74 and 7.581 values between 9.27 and 10.111 values between 16.02 and 16.86offnegativepositiveoften

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. ``
  2. ``
  3. ```
  4. ```
  5. ``,
  6. ```⏎
  7. ``(
  8. \`
  9. ``⏎
  10. 〕

Suppresses

  1. ",⏎
  2. ',⏎
  3. ',⏎
  4. '}}⏎
  5. ', ⏎
  6. ',⏎⏎
  7. ", ⏎
  8. ',{⏎
  9. &⏎
  10. пом