Neuron Explorer

Qwen3-0.6B

middle pieces of split names

A neuron in layer 22 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 fires on the middle fragment of a longer word or name that has been split into pieces, such as 'on' in Yoneda, 'oin' in Koinobori or 'un' in Eunomia. Its steering results were unrelated to these names, so its role is hard to read from the outputs.

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

    generalized elements where the equivalence is generated by⏎and it is immediate that every generalized element is equivalent to the universal generalized element ; in spirit, this is the Yoneda lemma in disguise.⏎Now we prove the proposition on colimits of nerves.⏎

  2. #2 peak

    www.visitmatagorda.com.<|endoftext|>A universal element of a functor is an element , where is some object of , which exhibits representability of via the Yoneda lemma. That is, any element induces, in natural bijective fashion,

  3. #3 peak

    Japanese Constitution, the birth of Showa Emperor (why is the last emperor’s birthday green? It’s a mystery), and the safe-growing of children.⏎Koinobori, or Carp Streamers⏎Among these three holidays, children’s day

  4. #4 peak

    2th century in Japan, Children’s day’s symbol, Koinobori and May Doll, came to today’s shape.⏎If you have boys

  5. #5 peak

    set forth in the International Decade of Ocean Exploration in the 1970s, the concerns raised by scientists at the ISBW 1 in Kominato,<|endoftext|>Student Health Checklist⏎Being a college student warrants a quick health check

  6. #6 peak

    data.⏎The methodology and data sources used for the calculation of the European steel recycling rate have been once again reviewed independently by the energy and waste specialist consultancy, Eunomia, who confirm the credibility of APEAL’s methodology and data.⏎

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 a 44444444444444444444444444444444444444444444

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

    and the operation of convolution define a commutative ring. Any ring without divisors of zero can be extended to a quotient

  2. #2 peak

    a differentiable symmetry of nature. For example, the conservation of energy follows from the time-invariance of physical systems, and the conservation of angular momentum arises from the fact that physical systems behave the same regardless of how they are oriented in space

  3. #3 peak

    . However, an intriguing set of open universe models shows an apparent entropy decrease when black holes disappear over the cosmological event horizon. We anticipate that this apparent violation of the generalized second law will disappear when solutions are available for black holes embedded in arbitrary

  4. #4 peak

    in Electronics/Confined Particles/Parity⏎Parity in 1 Dimension⏎Parity is one dimension is essentially a statement of the wavefunction's "evenness" or

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. L9:2658 suppresses
  2. L9:1288 suppresses
  3. L21:573 suppresses
  4. L15:2574 suppresses
  5. L8:1772 weakly suppresses
  6. L4:2870 weakly suppresses
  7. L20:1046 weakly excites
  8. L15:1216 weakly suppresses
  9. L9:2936 weakly suppresses
  10. L14:1654 weakly excites
  11. L16:172 weakly suppresses
  12. L19:1965 weakly suppresses
  13. L6:2348 weakly excites
  14. L7:2021 weakly suppresses
  15. L11:2496 weakly excites
  16. L13:2198 weakly excites
  17. L8:2665 weakly suppresses
  18. L1:814 weakly excites
  19. L5:362 weakly suppresses
  20. L6:2121 weakly suppresses
  21. L17:105 weakly suppresses
  22. L17:423 weakly excites
  23. L11:1398 weakly excites
  24. L9:34 weakly suppresses

Feeds later layers

  1. L24:1583 suppresses
  2. L23:469 suppresses
  3. L27:1531 suppresses
  4. L24:2739 suppresses
  5. L24:2085 suppresses
  6. L27:1074 suppresses
  7. L24:2126 weakly suppresses
  8. L26:1865 weakly suppresses
  9. L24:307 weakly suppresses
  10. L25:1420 weakly suppresses
  11. L26:173 weakly suppresses end-of-document boundary token
  12. L25:1779 weakly suppresses
  13. L23:2801 weakly excites
  14. L24:263 weakly suppresses
  15. L26:30 weakly suppresses
  16. L23:640 weakly suppresses
  17. L25:1991 weakly suppresses
  18. L24:804 weakly suppresses
  19. L23:2989 weakly suppresses
  20. L27:1689 weakly suppresses
  21. L27:2040 weakly suppresses
  22. L25:2608 weakly suppresses
  23. L25:1093 weakly suppresses
  24. L24:1189 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 -5.47 and -5.271 values between -4.46 and -4.261 values between -3.04 and -2.842 values between -2.84 and -2.641 values between -2.23 and -2.036 values between -1.42 and -1.222 values between -1.22 and -1.027 values between -1.02 and -0.8227 values between -0.82 and -0.6195 values between -0.61 and -0.41260 values between -0.41 and -0.211564 values between -0.21 and -0.011426 values between -0.01 and 0.20315 values between 0.20 and 0.40132 values between 0.40 and 0.6044 values between 0.60 and 0.8112 values between 0.81 and 1.013 values between 1.01 and 1.213 values between 1.21 and 1.413 values between 1.41 and 1.622 values between 1.62 and 1.821 values between 2.42 and 2.63offnegativepositiveoften

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. Brigham
  2. raised
  3. عش
  4. /engine
  5. mdb
  6. 平原
  7. rooting
  8. ugh
  9. .contentView
  10. RAND

Suppresses

  1. Associ
  2. associative
  3. comm
  4. 线
  5. itivity
  6. Associ
  7. trans
  8. (trans
  9. associ
  10. Closure