Neuron Explorer

Qwen3-0.6B

the word 'case' in set phrases

A neuron in layer 18 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 word 'case', as in 'in point', 'the case for', 'case study' or 'in the case of'. When it was turned up, the model kept repeating 'case study' and similar phrases. Its negative side is weak and mostly the word 'depending'.

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

    that our writers write in simple English, with as little use of technical language as possible. Some cases justify the use of some technical words. The following is a case in point. This

  2. #2 peak

    , limited the part it can play in shaping the economy, undervalued its contribution to national output, wrongly led to excessive privatization and outsourcing, ignored the case for the taxpayer sharing in the rewards of a collective—public—process of value

  3. #3 peak

    one Million units of the Coronavirus – a virus, which would get a person in the case of a natural infection never. This was given to the anesthetized monkeys

  4. #4 peak

    global poor. We think it is important to add this to the case study to allow readers to explore it fully, and to encourage discussion and wider dissemination

  5. #5 peak

    says Vyn.⏎If farmers and the industry are concerned about phosphorus run-off, the case for banding gains significant strength, argues Vyn. “It provides an opportunity

  6. #6 peak

    , complementary or even equivalent to the ecosystem approach (e.g. the Code for Responsible Fisheries, the Sustainable Forest Management approach, adaptive forest management). These and the case studies in the searchable component of the sourcebook provide examples of how the ecosystem approach

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: how to design a system that can be used to determine the level of human intelligence levels of people who are currently in the world today, from the most accurate, how to design a way to determine how people are actually capable of doing things,

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

    enter the ID and Password and then we should all be able to speak and see each other at the same time (you may need to have some earphones, depending if your device has a built in speaker or not).⏎PLEASE NOTE PARENTS

  2. #2 peak

    to Newfoundland in the mid 20th century. In fact, the only common thread was a mutual childhood memory of playing on a wreck of varying descriptions – depending on the age and recall power of the narrator.⏎In spite of the odds against

  3. #3 peak

    ?⏎By definition, an aperture is the size (in inches or millimetres) of the optical piece collecting light in a telescope. That optical instrument is different depending on the type of telescope you are using. In a refracting telescope, the

  4. #4 peak

    financial obligations and debts. These can include loans, accounts payable, and accrued expenses. Similar to assets, liabilities can also be classified as current or long-term, depending on their repayment timeline.⏎Shareholders' equity, also referred to as net worth

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. L16:2444 weakly suppresses
  2. L16:260 weakly excites
  3. L13:1166 weakly excites
  4. L5:971 weakly excites
  5. L16:282 weakly excites
  6. L17:1445 weakly excites
  7. L0:2450 weakly suppresses
  8. L10:2711 weakly suppresses
  9. L4:1521 weakly suppresses
  10. L9:1001 weakly excites
  11. L11:2327 weakly suppresses
  12. L0:794 weakly excites
  13. L4:1124 weakly suppresses
  14. L12:457 weakly suppresses
  15. L7:1486 weakly excites
  16. L3:2064 weakly excites
  17. L0:1175 weakly excites
  18. L15:699 weakly excites
  19. L8:1061 weakly suppresses
  20. L10:2520 weakly excites
  21. L17:148 weakly excites
  22. L2:1233 weakly suppresses
  23. L3:2304 weakly excites
  24. L0:1848 weakly excites

Feeds later layers

  1. L24:2442 weakly excites
  2. L19:263 weakly suppresses
  3. L21:2716 weakly excites
  4. L24:2092 weakly suppresses "self" before esteem or image
  5. L25:25 weakly suppresses
  6. L21:2218 weakly excites
  7. L19:337 weakly suppresses
  8. L21:463 weakly suppresses
  9. L24:1018 weakly suppresses
  10. L24:3003 weakly excites
  11. L21:2800 weakly excites
  12. L19:1147 weakly suppresses
  13. L21:997 weakly excites
  14. L22:1541 weakly suppresses
  15. L21:1661 weakly excites
  16. L20:1374 weakly suppresses
  17. L26:1547 weakly excites
  18. L20:2709 weakly suppresses
  19. L24:2444 weakly suppresses
  20. L23:2916 weakly excites
  21. L19:898 weakly suppresses
  22. L20:2199 weakly excites
  23. L21:2656 weakly suppresses
  24. L19:784 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 -7.51 and -6.891 values between -5.02 and -4.393 values between -4.39 and -3.772 values between -3.77 and -3.154 values between -3.15 and -2.536 values between -2.53 and -1.9011 values between -1.90 and -1.2871 values between -1.28 and -0.661624 values between -0.66 and -0.032060 values between -0.03 and 0.5970 values between 0.59 and 1.2119 values between 1.21 and 1.8410 values between 1.84 and 2.463 values between 2.46 and 3.084 values between 3.08 and 3.703 values between 3.70 and 4.333 values between 4.33 and 4.956 values between 4.95 and 5.571 values between 5.57 and 6.201 values between 6.20 and 6.821 values between 6.82 and 7.442 values between 8.69 and 9.311 values between 9.93 and 10.561 values between 16.78 and 17.41offnegativepositiveoften

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. aways
  2. ña
  3. ñas
  4. icates
  5. uits
  6. away
  7. spared
  8. 违
  9. aneous
  10. uit

Suppresses

  1. 主意
  2. 口气
  3. 灵
  4. spir
  5. Bin
  6. 撸
  7. 辦法
  8. oko
  9. Span
  10. Phaser