Neuron Explorer

Qwen3-0.6B

'be' after modal verbs like 'can'

A neuron in layer 1 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 'be' right after a modal such as 'can', 'need to' or 'can also', as in 'can be used'. Turning it up did not produce an obvious change in what the model wrote about.

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

    building cascading deck stairs, it is important to make sure that the stairs are properly supported and that each stair is of the same height. The following steps can be used to build cascading deck stairs: 1. Decide on the height of

  2. #2 peak

    renewable sources to (1) convert CO2 into carbon-neutral fuels, such as methanol, or (2) transform biomass into long-chain molecules that can also be used as fuel.⏎CO2 is kinetically and thermodynamically inert, making

  3. #3 peak

    not include the transfer of personal property on that real property.⏎Determining Real vs. Personal Property⏎The following methodology can be used to determine whether an asset is personal or real property.⏎Method of attachment:

  4. #4 peak

    high incidence of asbestos exposure. More specific information about asbestos exposure on the following jobs can be found at Asbestos Network.⏎Insulators, or people that worked extensively around asbestos

  5. #5 peak

    /AIDS requires an increased amount of investment. These investments need to be amplified in both sexual education and innovative HIV/AIDS prevention programs.⏎Both the Executive Director

  6. #6 peak

    atmosphere. This project aims to prove the feasibility of using a technique to separate water vapor from the exhaust stream to generate a high energy steam cycle, which can then be used to help reduce the engine's fuel consumption.⏎Commenting on the projects,

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: I have been thinking about the idea of a well-known, well-known, and well-known as a number of well-known and well-known in the same area, and the same area, and the same area. I think this is the first

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

    should watch the talk and decide whether the sentences are true or false according to the video. This general English lesson plan finishes with a short

  2. #2 peak

    . incompatible persons or things.⏎2. an incompatible drug or the like.⏎3. incompatibles, Logic.⏎a. two or more propositions that cannot be true simultaneously.⏎b. two or more attributes that cannot simultaneously belong to the same object

  3. #3 peak

    in character; discordant: incompatible colors.⏎3. that cannot coexist or be conjoined.⏎a. (of two or more propositions) unable to be true simultaneously.⏎b. (of two or more attributes of an object) unable to

  4. #4 peak

    ) to assume several births simultaneously.⏎The philosophy of pariśeṣanyāya or the doctrine of elimination of the alternatives accepts only the last alternative to be true. Among the accumulated karmas, one major karma along with several minor k

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. L0:418 weakly suppresses
  2. L0:313 weakly suppresses "and" joining paired nouns
  3. L0:2528 weakly suppresses
  4. L0:6 weakly suppresses
  5. L0:2093 weakly suppresses
  6. L0:2699 weakly suppresses
  7. L0:1730 weakly excites
  8. L0:2035 weakly excites
  9. L0:942 weakly suppresses
  10. L0:282 weakly excites
  11. L0:673 weakly suppresses
  12. L0:485 weakly suppresses
  13. L0:1572 weakly suppresses
  14. L0:3 weakly excites
  15. L0:2808 weakly excites
  16. L0:988 weakly excites
  17. L0:1514 weakly suppresses
  18. L0:924 weakly suppresses
  19. L0:909 weakly suppresses
  20. L0:568 weakly suppresses
  21. L0:2886 weakly excites
  22. L0:624 weakly excites
  23. L0:1875 weakly suppresses
  24. L0:1581 weakly suppresses

Feeds later layers

  1. L7:1462 suppresses
  2. L26:1731 weakly suppresses
  3. L4:1934 weakly suppresses
  4. L21:1625 weakly suppresses
  5. L7:21 weakly suppresses
  6. L2:1778 weakly suppresses
  7. L14:121 weakly excites
  8. L21:208 weakly excites
  9. L21:2838 weakly suppresses
  10. L14:1309 weakly excites
  11. L15:1364 weakly excites
  12. L2:327 weakly excites
  13. L18:865 weakly excites
  14. L18:1379 weakly suppresses
  15. L20:2908 weakly excites
  16. L4:284 weakly suppresses
  17. L20:482 weakly suppresses
  18. L14:29 weakly excites
  19. L16:2143 weakly suppresses
  20. L22:1237 weakly suppresses
  21. L23:1139 weakly excites
  22. L17:248 weakly excites
  23. L5:1322 weakly excites
  24. L10:726 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 -0.88 and -0.754 values between -0.75 and -0.614 values between -0.61 and -0.4814 values between -0.48 and -0.3561 values between -0.35 and -0.21471 values between -0.21 and -0.082335 values between -0.08 and 0.05838 values between 0.05 and 0.18121 values between 0.18 and 0.3219 values between 0.32 and 0.457 values between 0.45 and 0.583 values between 0.58 and 0.726 values between 0.72 and 0.856 values between 0.85 and 0.986 values between 0.98 and 1.111 values between 1.11 and 1.252 values between 1.25 and 1.382 values between 1.78 and 1.911 values between 1.91 and 2.041 values between 2.31 and 2.441 values between 3.24 and 3.371 values between 3.37 and 3.502 values between 3.50 and 3.641 values between 4.30 and 4.43offnegativepositiveoften

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. over
  2. mişti
  3. ove
  4. strar
  5. submitted
  6. ARC
  7. jected
  8. RR
  9. written
  10. .ImageField

Suppresses

  1. annya
  2. ropriate
  3. onom
  4. 大哥
  5. doctype
  6. osterone
  7. 그것은
  8. rằng
  9. sophistic
  10. appropriate