Neuron Explorer

Qwen3-0.6B

'how' starting a degree or manner clause

A neuron in layer 17 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 'how' when it begins a question-like phrase about degree or manner, such as 'how quickly', 'how well' or 'how long'. When it was turned up, the model's text drifted into repeated, garbled phrases instead of saying anything clear.

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

    heard the statistic that we spend 90 percent of our time indoors, or know that you should change your HVAC filter every three months for optimal performance—but just how air

  2. #2 peak

    you have bile duct cancer, you may have questions about your prognosis. A prognosis is the doctor’s best estimate of how cancer will affect someone and how it will respond to treatment. Prognosis and

  3. #3 peak

    penetrate your clothing’s fabric.⏎- SPF: On the other hand, SPF (Sun Protection Factor) serves as a timetable of sorts, allowing you to know about how long it will take before your skin begins to redden while using a product that

  4. #4 peak

    fectionists set unrealistic standards for tasks they do. Often, the said task doesn’t even require much effort. But for them, their self-worth depends on how perfect their result is. Therefore, they tend to overwork and overstress to

  5. #5 peak

    Glycemic index foods⏎This is where glycemic index comes into the scene. Glycemic index (GI) classifies foods based on how quickly they elevate blood sugar levels, taking pure glucose as the comparison point.⏎F

  6. #6 peak

    take immediate action.⏎If you suspect that your computer is infected, speed is key. How quickly you respond to your computer's symptoms can make all the difference in how critically it is affected. Initially, don't worry about identifying the culprit. That

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: that the following for what is not in the project as a project as a possible future as a possible as a future as a future as a possible as possible as a possible as possible as a possible as possible as a possible as possible as a

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

    asset base to satisfy its creditors, owners, and other providers of capital, or they will invest elsewhere.⏎The following is the WACC calculation formula:⏎WACC = E/V × Re + D/V × Rd × (1 - Tc)⏎

  2. #2 peak

    25% should take the following relationship: 1.000×25 / 100 , or , 1.000×0.25 .⏎Giving us a result of 250, this

  3. #3 peak

    (C, higher concentration less lower concentration) divided by the thickness of the membrane. At steady state, Fickian diffusion can be approximated by:⏎J = D (CWaterlooEmitter - groundwater) / membrane wall thickness⏎where D

  4. #4 peak

    $\quad+\quad$ or $\quad-$<|endoftext|>Design for a Mosaic⏎Albert Joseph Moore

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. L4:532 suppresses
  2. L15:1704 suppresses
  3. L15:280 excites
  4. L13:91 weakly excites
  5. L1:2396 weakly excites
  6. L15:604 weakly suppresses
  7. L2:126 weakly excites
  8. L6:1058 weakly suppresses
  9. L2:111 weakly suppresses
  10. L7:512 weakly suppresses
  11. L0:1192 weakly excites
  12. L16:282 weakly excites
  13. L13:416 weakly excites
  14. L15:850 weakly excites
  15. L11:265 weakly excites
  16. L16:222 weakly suppresses
  17. L4:1591 weakly excites
  18. L13:2402 weakly excites
  19. L2:1734 weakly excites
  20. L15:136 weakly suppresses
  21. L15:627 weakly excites
  22. L16:284 weakly excites
  23. L6:1892 weakly excites
  24. L16:1876 weakly excites

Feeds later layers

  1. L18:2652 excites
  2. L19:1535 excites
  3. L22:1046 excites
  4. L26:1849 excites
  5. L20:1295 excites
  6. L21:2777 excites
  7. L23:530 excites
  8. L19:1612 excites
  9. L19:2331 excites
  10. L21:1047 excites
  11. L24:2431 excites
  12. L21:916 excites
  13. L24:546 weakly excites
  14. L23:2619 weakly suppresses
  15. L20:1978 weakly excites
  16. L24:568 weakly excites
  17. L20:2712 weakly suppresses
  18. L23:711 weakly excites
  19. L18:538 weakly excites
  20. L21:2320 weakly excites
  21. L18:2395 weakly excites
  22. L19:2474 weakly excites
  23. L20:1733 weakly excites
  24. L26:1601 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.

320 values between -1.53 and -0.543542 values between -0.54 and 0.4424 values between 0.44 and 1.438 values between 1.43 and 2.424 values between 2.42 and 3.401 values between 3.40 and 4.391 values between 4.39 and 5.381 values between 12.28 and 13.271 values between 18.20 and 19.191 values between 23.14 and 24.121 values between 29.06 and 30.041 values between 30.04 and 31.031 values between 32.02 and 33.001 values between 34.98 and 35.961 values between 36.95 and 37.94offnegativepositiveoften

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. rones
  2. getTable
  3. thumbs
  4. ismatic
  5. Handy
  6. atholic
  7. ategorized
  8. 滉
  9. _tables
  10. lename

Suppresses

  1. as
  2. uz
  3. div
  4. cáo
  5. 并非
  6. As
  7. uç
  8. cul
  9. covering