Neuron Explorer

Qwen3-0.6B

unclear: verbs after a noun or 'that'

A neuron in layer 19 of Qwen3-0.6B. Its strongest snippets are mixed: they peak on different tokens. It is featured, so it has steering outputs.

What this neuron does

This neuron is hard to pin down. It fires on verbs such as 'harvest', 'used', 'burn' and 'molting' that come right after a noun or 'that', but the pattern is loose. Steering did not produce a clear theme.

Written by Claude from this neuron's activations and steering outputs. Confidence: low. 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

    . As a result, farmers will be able to significantly reduce pesticide applications and harvest bigger yields. The crop is also expected to support the nation's economic development and

  2. #2 peak

    structure is similar to that of the ___2___ and national governments, with an elected mayor as chief of the ___3___ and an elected council that ___4___ the various neighborhoods forming the ___5___ . The ___6___ appoints heads

  3. #3 peak

    For cantaloupe, the principal harvest indices are surface color and the development of the abscission zone.⏎Most fruit vegetables are harvested by hand. Some harvest aids may be used, including pickup machines and conveyors

  4. #4 peak

    not found anywhere else, and now rare even in Jordan; the Christian Bedouins.⏎Many of the traditional houses of Hmoud were built on caves that used to be a strong attraction for settling this site, as they provided early shelters prior

  5. #5 peak

    be extremely stingy with neutrons. The coolant moderator combination had to be one that did not absorb neutrons to any great extent. To make natural uranium “burn” every available neutron has to find its way into fuel material. Using a neutron

  6. #6 peak

    izing the cities.⏎Designing the board, deciding how to connect the cities⏎Creating their board based on having 6 players⏎Setting up the game to playtest it⏎“The city defence changes when

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 number 0 is often the number 000000000000000000000000000000000000000

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

    while loop keeps running while the value of i is equal to or greater than 0.⏎- Inside the loop, we are printing the element for index i and decrementing the value of i by 1.⏎If you run this program, it

  2. #2 peak

    ⏎Create sequnce 1 4 9 16 25.........⏎Basics: Divide integers to get integer outputs in all cases⏎Determine value using Look-up table method⏎Create Volcano (or Atoll) mart

  3. #3 peak

    plane is still on the ground, you design and develop measuring devices that take temperature, noise, altitude, navigation, and maneuverability readings. Then you hard-wire the devices into place, and send the Test Pilot into the clouds. When he

  4. #4 peak

    progress over the years!⏎- Color and cut out the pieces.⏎- Roll the large triangular piece and use the tab to glue it into a cone⏎- Glue the nose onto the face using a dot of glue on the nose (this allows

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. L18:1601 suppresses
  2. L16:739 excites
  3. L8:216 excites
  4. L18:229 excites
  5. L18:935 excites
  6. L18:2787 excites
  7. L6:375 suppresses
  8. L15:675 excites
  9. L16:2227 suppresses
  10. L11:153 suppresses
  11. L2:1282 excites
  12. L16:652 excites
  13. L2:491 excites
  14. L17:153 suppresses
  15. L14:1148 excites
  16. L4:48 suppresses
  17. L18:1516 suppresses
  18. L14:526 suppresses
  19. L16:1158 suppresses
  20. L18:2703 excites
  21. L17:1282 suppresses
  22. L17:1177 suppresses
  23. L14:1209 suppresses
  24. L16:2128 excites

Feeds later layers

  1. L20:593 strongly suppresses
  2. L20:1972 strongly suppresses 'of' in 'of course'
  3. L22:353 strongly excites
  4. L25:247 suppresses
  5. L20:728 suppresses
  6. L20:64 excites
  7. L22:1124 suppresses
  8. L22:197 excites
  9. L20:271 excites
  10. L24:49 excites
  11. L21:310 suppresses
  12. L20:544 suppresses
  13. L27:108 excites
  14. L20:331 excites
  15. L20:955 suppresses
  16. L23:96 excites
  17. L27:663 suppresses
  18. L20:482 suppresses
  19. L20:762 excites
  20. L27:121 excites
  21. L22:171 suppresses
  22. L26:139 excites
  23. L27:149 excites
  24. L20:94 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 -10.98 and -10.562 values between -10.15 and -9.733 values between -9.73 and -9.311 values between -8.90 and -8.481 values between -8.07 and -7.653 values between -7.65 and -7.231 values between -7.23 and -6.824 values between -6.82 and -6.407 values between -6.40 and -5.992 values between -5.99 and -5.578 values between -5.57 and -5.154 values between -5.15 and -4.744 values between -4.74 and -4.327 values between -4.32 and -3.9110 values between -3.91 and -3.497 values between -3.49 and -3.078 values between -3.07 and -2.6613 values between -2.66 and -2.2422 values between -2.24 and -1.8317 values between -1.83 and -1.4119 values between -1.41 and -1.0030 values between -1.00 and -0.58221 values between -0.58 and -0.162865 values between -0.16 and 0.25558 values between 0.25 and 0.6762 values between 0.67 and 1.0912 values between 1.09 and 1.503 values between 1.50 and 1.921 values between 1.92 and 2.336 values between 2.33 and 2.751 values between 2.75 and 3.171 values between 3.58 and 4.001 values between 4.00 and 4.411 values between 4.41 and 4.831 values between 4.83 and 5.241 values between 5.24 and 5.66offnegativepositiveoften

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. \Extension
  2. ieties
  3. Sikh
  4. ่อน
  5. umb
  6. ستان
  7. respectfully
  8. 构
  9. submits
  10. Dresses

Suppresses

  1. the
  2. them
  3. these
  4. those
  5. 它
  6. their
  7. everything
  8. our
  9. it
  10. ate