Neuron Explorer

Qwen3-0.6B

the prefix 'non' before a word

A neuron in layer 22 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 lights up on the word 'non' when it starts a new word, like 'non-smokers' or 'non-human'. When it was turned up, the model's text drifted into odd, unrelated wording, so the push did not reveal a clear effect.

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

    thermic effect of activity-this, obviously, represents the caloric burn of activity, both exercise and non-exercise acitivty.⏎- NEAT=non exercise activity thermogenesis=general activity like walking the dog,

  2. #2 peak

    kind, not just dark) were also less likely to report depressive symptoms than those who didn’t eat chocolate at all. However researchers found no significant link between any non‐dark chocolate consumption and clinically relevant depressive symptoms.⏎“This study provides some evidence that

  3. #3 peak

    through interviews and recorded into a dedicated online system. The Pearsonχ2 test, multivariate logistic regression models and discriminant models were fitted into its comparisons with the non-norovirus group and other pathogens.⏎Norovirus was detected in

  4. #4 peak

    involving emotional regulation, and those who had more everyday inattention symptoms had larger problems with the non-emotional cognitive task.⏎"One of the reasons why these results are important is that

  5. #5 peak

    often found mutated in non-smokers (EGFR). Led by HMS graduate student Stephen Curtis, the team

  6. #6 peak

    18-35) read short discourse scenarios for comprehension while monitoring for the presence of semantic anomalies. Using a factor analytic approach, we examined if performance in nonlinguistic conflict monitoring tasks (Stroop, AX-CPT) would

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: items items items items items items items items items items items items items items items items items items items items items items items items items items items items items items items items items items items items items items items items items items items items items items items items

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

    are nearby? What type of boundary are the quakes on?⏎Your paper should meet the following requirements:⏎3-4 pages in length (does not include title and reference page)⏎1-2 outside sources⏎Why Work with CheggAnswers⏎

  2. #2 peak

    form by HashiCorp. But what exactly are Terraform's capabilities, and why should your business consider using it?⏎Put simply, Terraform is powerful infrastructure as code (IaC) tool that allows you to create, update, and

  3. #3 peak

    ~10% under the three SGs and decrease 5% under emissions reduction, the latter primarily due to reduced CO2 fertilization, relative to business as usual by the late twenty-first century. Relative humidity dominates the hydrological effect on

  4. #4 peak

    techniques. During the "break-bulk" era, high-value cargo was packaged in cases or pallets for shipment and loaded and unloaded on a piece-by-piece or pallet-by-pallet basis. Easy access encouraged the theft of electronics, appliances,

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. L19:2198 excites
  2. L21:831 excites
  3. L20:2269 suppresses
  4. L20:2979 excites
  5. L20:1651 excites
  6. L21:966 suppresses
  7. L20:1344 excites
  8. L20:983 excites
  9. L20:1197 excites 'this' pointing at the current document
  10. L20:1533 excites
  11. L21:68 suppresses
  12. L19:1371 excites
  13. L18:1691 suppresses
  14. L21:2105 weakly suppresses
  15. L15:817 weakly suppresses
  16. L18:1596 weakly suppresses
  17. L19:2490 weakly suppresses
  18. L20:495 weakly excites
  19. L20:2352 weakly suppresses
  20. L18:1928 weakly suppresses
  21. L19:705 weakly excites
  22. L17:1002 weakly suppresses
  23. L20:864 weakly suppresses
  24. L20:2201 weakly excites 'the' in location phrases like 'at the end'

Feeds later layers

  1. L23:465 suppresses
  2. L25:525 suppresses
  3. L23:355 suppresses
  4. L24:163 suppresses
  5. L26:359 weakly suppresses
  6. L27:735 weakly suppresses
  7. L26:2076 weakly suppresses
  8. L27:227 weakly suppresses
  9. L23:2775 weakly suppresses
  10. L25:390 weakly suppresses
  11. L25:1220 weakly suppresses
  12. L26:490 weakly suppresses the digit 1 starting a number
  13. L24:419 weakly suppresses
  14. L23:1300 weakly suppresses
  15. L25:1588 weakly suppresses
  16. L24:268 weakly suppresses
  17. L26:1764 weakly suppresses
  18. L25:2026 weakly excites
  19. L27:1402 weakly suppresses
  20. L23:232 weakly excites
  21. L26:568 weakly suppresses
  22. L25:886 weakly suppresses
  23. L23:2415 weakly excites
  24. L24:2310 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 -14.31 and -13.761 values between -9.30 and -8.751 values between -8.75 and -8.191 values between -7.64 and -7.081 values between -6.52 and -5.971 values between -5.41 and -4.854 values between -4.85 and -4.306 values between -4.30 and -3.747 values between -3.74 and -3.1815 values between -3.18 and -2.6316 values between -2.63 and -2.0731 values between -2.07 and -1.5177 values between -1.51 and -0.96278 values between -0.96 and -0.402441 values between -0.40 and 0.16790 values between 0.16 and 0.71112 values between 0.71 and 1.2756 values between 1.27 and 1.8224 values between 1.82 and 2.3814 values between 2.38 and 2.9412 values between 2.94 and 3.497 values between 3.49 and 4.055 values between 4.05 and 4.611 values between 5.16 and 5.721 values between 5.72 and 6.282 values between 6.28 and 6.832 values between 6.83 and 7.391 values between 7.39 and 7.95offnegativepositiveoften

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. resse
  2. arily
  3. ophobia
  4. ไหน
  5. rys
  6. атор
  7. ricerca
  8. trưởng
  9. verständ
  10. under

Suppresses

  1. items
  2. 项目
  3. 元素
  4. pieces
  5. 元件
  6. 項目
  7. 板块
  8. piece
  9. 物品
  10. 部门