Neuron Explorer

Qwen3-0.6B

'mean' after 'doesn't' in 'just because' sentences

A neuron in layer 6 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 'mean' in sentences like 'just because X doesn't mean Y', where a point is being qualified or pushed back on. Turning it up made the model drift into rambling text, with no clear theme.

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

    connected to the National Institute of Health, has some pretty amazing fact sheets that lay everything out in great detail. But just because supplements are safe in moderation doesn't mean that more is better. Combining

  2. #2 peak

    of health benefits, including weight loss, improved lung function, lower risk of stroke, cancer and heart disease.⏎But just because the humble apple is ubiquitous doesn't mean it's without some mystery as well. Read on to learn more about the fruit

  3. #3 peak

    consonant sounds work better throughout the melodic phrase. Just because a word or two fits the number of syllables you need to fill in your melody doesn't mean it will necessarily flow out nicely when sung

  4. #4 peak

    to consider, such as job market demand, salary ranges, long-range opportunities, skills required, etc. Just because an occupation is "hot" now does not mean it will be equally in demand in 10 years or that your child has

  5. #5 peak

    Also just because milk is rich in calcium doesn’t mean much if you don’t have vitamin D to accompany it, otherwise the calcium can

  6. #6 peak

    try with your children.⏎1. Brainstorm ways to save and collect money.⏎Your child may be too young for an official “job,” but that doesn’t mean he or she is too young for other entrepreneurial endeavors. Help your child brainstorm different

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: 10, 20, 30, 40, 60, 80, 90, 120, 150, 180, 200,

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

    limitations cannot be understood only through electrical specifications and nameplate data, because redundancy is included in the design. In order to obtain a thorough understanding of electromagnetic force/torque generation, one must consider thermal characteristics

  2. #2 peak

    There is now firm evidence supporting the lever arm hypothesis for the origin of directional movement. Nevertheless, many pivotal issues remain. For

  3. #3 peak

    as the origin, distances to the left would have negative values while the distances to right would be positive. In a perfect world, the sum of all of the moments would equal zero, indicating balanced forces across the press slide.⏎Table 1 quant

  4. #4 peak

    to work side by side with humans without any physical boundaries. Also, compared to industrial robots, they are programmed to operate at lower speeds and with less ”torque strength”, making them safer to use in parallel to human workers.⏎Indeed, industrial

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. L4:2893 excites the word 'means' after a subject
  2. L0:1483 weakly suppresses
  3. L5:102 weakly excites
  4. L5:1518 weakly excites
  5. L2:2281 weakly excites
  6. L0:2082 weakly excites
  7. L3:2026 weakly excites
  8. L3:2140 weakly suppresses
  9. L4:2434 weakly excites
  10. L3:877 weakly suppresses
  11. L4:12 weakly suppresses
  12. L2:2666 weakly suppresses
  13. L1:2583 weakly excites
  14. L4:2105 weakly suppresses
  15. L2:1566 weakly excites
  16. L2:1896 weakly suppresses
  17. L4:2825 weakly excites "to" after 'for X' infinitives
  18. L4:2089 weakly suppresses
  19. L4:2787 weakly suppresses
  20. L1:2843 weakly suppresses
  21. L2:2335 weakly suppresses
  22. L1:2801 weakly excites
  23. L5:60 weakly excites
  24. L1:1546 weakly excites

Feeds later layers

  1. L8:2053 suppresses
  2. L15:1167 weakly suppresses
  3. L9:2374 weakly excites
  4. L7:3064 weakly excites
  5. L23:1959 weakly suppresses
  6. L8:1922 weakly suppresses
  7. L7:2557 weakly suppresses
  8. L8:1716 weakly excites
  9. L10:251 weakly excites
  10. L10:2133 weakly excites
  11. L11:2761 weakly excites
  12. L10:2281 weakly excites
  13. L26:2687 weakly excites unclear: word starts like Ag and Re
  14. L7:1539 weakly excites
  15. L23:1846 weakly excites
  16. L7:1150 weakly excites
  17. L9:298 weakly excites
  18. L16:135 weakly excites
  19. L8:281 weakly suppresses
  20. L7:1801 weakly suppresses
  21. L8:2771 weakly excites
  22. L15:264 weakly suppresses
  23. L15:2550 weakly suppresses
  24. L7:1630 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.

1 values between -1.62 and -1.541 values between -0.81 and -0.732 values between -0.64 and -0.564 values between -0.56 and -0.4818 values between -0.48 and -0.4063 values between -0.40 and -0.32175 values between -0.32 and -0.24377 values between -0.24 and -0.16723 values between -0.16 and -0.071105 values between -0.07 and 0.01791 values between 0.01 and 0.09377 values between 0.09 and 0.17165 values between 0.17 and 0.2561 values between 0.25 and 0.3326 values between 0.33 and 0.428 values between 0.42 and 0.504 values between 0.50 and 0.581 values between 0.66 and 0.741 values between 0.90 and 0.991 values between 0.99 and 1.071 values between 1.15 and 1.231 values between 1.39 and 1.482 values between 1.56 and 1.64offnegativepositiveoften

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. tú
  2. 就意味着
  3. 체
  4. СП
  5. nero
  6. Trainer
  7. изатор
  8. uego
  9. tweeting
  10. trainers

Suppresses

  1. ays
  2. ld
  3. kinson
  4. Pradesh
  5. Cos
  6. licensors
  7. arms
  8. mdi
  9. ofs
  10. 支柱