Neuron Explorer

Qwen3-0.6B

unclear: word after 'just', 'full' or 'whole'

A neuron in layer 2 of Qwen3-0.6B. Its strongest snippets are mixed: they peak on different tokens. It is featured, so it has steering outputs.

What this neuron does

This neuron is hard to pin down. It often lights up on the word that finishes a set phrase like 'just war', 'full swing' or 'full houses', and it goes quiet or negative on 'only to' as in 'only to discover'. Turning it up did not produce any clear change in what the model wrote.

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

    <|endoftext|>“There shall never be any bond slaverie, villinage or Captivitie amongst us unles it be lawfull Captives taken in just warres, and such strangers as willingly selle themselves or are sold to us.

  2. #2 peak

    weapons—to commit such crimes. (CCC 2314; cf. Gaudium et Spes 80)⏎Under the Catholic understanding of just war, not only must the cause of

  3. #3 peak

    , Our Explosive Sun and The Northern Lights: A Guide. One of the foremost scholars on the subject, Mr. Brekke has spoken multiple times to full houses at the Smithsonian and elsewhere. In this interactive session,

  4. #4 peak

    read in a sustained way, then a chunk of his schooling should be focused on helping him squeeze meaning and richness out of the experience. You may remember the whole-language ideas about exposing kids to print through fiction, poetry, newspapers, and drama?

  5. #5 peak

    news HTTP/1.1 Host: www.example.ai Accept-Language: fr⏎HTTP/1.1 <|endoftext|>The move to electronic health records is in full swing for most of the medical community, but holdouts point to security issues as an

  6. #6 peak

    bu Anne mau makan di KFC.⏎Harimau tidak mau berlari ke rumah.⏎Perempuan muntah pohon.⏎Students then choose words from the above lists to make a sentence

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 am going to be thinking about lately, and I have been thinking about lately. So I am going to be thinking about lately, and I have been thinking about lately. So I am going to be thinking about lately. So I am

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

    the festivities. But when a blizzard shuts down the highway, Phil finds himself trapped in Punxsutawney. He wakes up the next day, only to discover that it's not the next day at all. It's Groundhog Day

  2. #2 peak

    the most efficient processing of the data; there is an old joke we like to tell our students about a meteorologist who wrote a weather prediction program, only to find out that it took one week to predict tomorrow's weather! (He later

  3. #3 peak

    influential funder who told me told me that parents were a sinkhole, only to tell me a bit later that parents were essential. Whiplash!⏎Funny thing

  4. #4 peak

    912, stretched a canvas for it in 1913, and returned to the composition late in 1915, only to start again on a new canvas in early 1916. Black is

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. L1:1960 weakly excites
  2. L1:545 weakly suppresses
  3. L0:1224 weakly suppresses
  4. L1:861 weakly suppresses
  5. L0:178 weakly suppresses
  6. L1:1199 weakly excites
  7. L0:1301 weakly suppresses
  8. L1:2160 weakly suppresses
  9. L1:1244 weakly suppresses "of" after "board" or "off"
  10. L1:1387 weakly suppresses
  11. L1:189 weakly suppresses
  12. L1:2739 weakly suppresses
  13. L1:2355 weakly excites
  14. L1:18 weakly suppresses
  15. L1:2914 weakly suppresses
  16. L0:2200 weakly excites
  17. L1:922 weakly excites
  18. L1:1485 weakly excites
  19. L0:1075 weakly excites
  20. L0:385 weakly excites
  21. L0:1580 weakly excites
  22. L0:1781 weakly excites
  23. L1:1102 weakly suppresses
  24. L1:340 weakly suppresses

Feeds later layers

  1. L4:607 excites
  2. L10:874 weakly excites
  3. L21:1268 weakly suppresses
  4. L8:448 weakly excites
  5. L13:2030 weakly excites
  6. L12:223 weakly suppresses
  7. L10:2236 weakly excites
  8. L10:751 weakly excites
  9. L6:477 weakly suppresses
  10. L23:2263 weakly suppresses
  11. L5:2921 weakly suppresses
  12. L3:1259 weakly excites
  13. L20:173 weakly suppresses
  14. L4:1097 weakly excites
  15. L7:2291 weakly suppresses
  16. L23:1883 weakly excites
  17. L7:2000 weakly suppresses
  18. L11:573 weakly suppresses
  19. L21:1280 weakly excites
  20. L15:1296 weakly suppresses commas inside citation author names
  21. L24:1697 weakly suppresses
  22. L8:2091 weakly excites
  23. L21:3017 weakly suppresses
  24. L24:1591 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 -0.59 and -0.561 values between -0.56 and -0.531 values between -0.45 and -0.424 values between -0.39 and -0.362 values between -0.36 and -0.344 values between -0.34 and -0.312 values between -0.31 and -0.285 values between -0.28 and -0.2516 values between -0.25 and -0.2217 values between -0.22 and -0.2030 values between -0.20 and -0.1749 values between -0.17 and -0.1478 values between -0.14 and -0.11125 values between -0.11 and -0.08222 values between -0.08 and -0.06333 values between -0.06 and -0.03710 values between -0.03 and -0.00914 values between -0.00 and 0.03515 values between 0.03 and 0.06352 values between 0.06 and 0.08177 values between 0.08 and 0.11124 values between 0.11 and 0.1472 values between 0.14 and 0.1757 values between 0.17 and 0.2034 values between 0.20 and 0.2229 values between 0.22 and 0.2512 values between 0.25 and 0.2815 values between 0.28 and 0.312 values between 0.31 and 0.331 values between 0.33 and 0.361 values between 0.36 and 0.391 values between 0.42 and 0.451 values between 0.47 and 0.501 values between 0.50 and 0.53offnegativepositiveoften

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. iface
  2. 人口
  3. dine
  4. chế
  5. Geschichte
  6. EEE
  7. society
  8. apartheid
  9. quốc
  10. .trace

Suppresses

  1. gars
  2. getPost
  3. 库里
  4. ильно
  5. leasing
  6. 系数
  7. 公证
  8. LObject
  9. CLE
  10. ],'