Neuron Explorer

Qwen3-0.6B

lesson text on code and fractions

A neuron in layer 10 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 tends to fire inside tutorial-style text about programming and math, such as Python examples and fraction lessons. Turning it up made the output wander into unrelated subjects, so the link is loose.

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

    "a long string" >>> >>> len(s) 13 >>> >>> len("") 0 >>>⏎>>> >>> s3 = '' # empty string using single quotes >>> s3 '' >>> >>> s4 = "" # empty string using double quotes

  2. #2 peak

    either 5 or 7. To count, a number must not be simultaneously⏎divisible by either 5 or 7.⏎Now you try. Finish the code with %

  3. #3 peak

    the Java Development Kit (JDK)⏎and write your first Java program.⏎Next, it's on to the Java fundamentals, including data types,⏎operators, control statements, classes, objects, and methods.⏎You'll then progress to more advanced topics

  4. #4 peak

    ycetica<|endoftext|>Adding Fractions Level 3⏎The 5-step planIn Adding Fractions Level 3 we'll practice addition with equivalent fractions with cardinal numbers. You will need to add the cardinal numbers together and the fractions

  5. #5 peak

    to decimals and decimals to fractions. Includes practice problems.⏎Decimal Review - Add - Subtract - Multiply⏎Click on this straight forward review lesson about reading, writing, adding, subtracting, and multiplying decimals without confusion.⏎Dividing with Decimals⏎

  6. #6 peak

    same denominators. So click and give it a try. It's easier than you think.⏎Fractions - Subtracting Mixed Numbers⏎Do you wonder how to subtract mixed numbers? Do you know how to handle unlike denominators or how to reg

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: the "level" of being a person, the "level" of being a number, and the "level" of being an object. So that's a lot of things... (a level of being, a number, a level of

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

    arn). It is estimated neutrino cross-section for interaction increases linearly with energy of incident neutrino.⏎Reference: Griffiths, David, Introduction to Elementary Particles, Wiley, 1987.⏎See also: Antineutrino

  2. #2 peak

    value , given by⏎For composite moduli , the two sums are in general different, and the above formula does not necessarily hold.⏎It was shown by Lehmer that the exponential Gauss walk always has the spiral-type shape observed in the visualization

  3. #3 peak

    mathematical induction, a powerful method of mathematical proof.⏎An introduction to the sort of algebra studied at university, focussing on groups.⏎An introduction to some beautiful results of Number Theory<|endoftext|>The value of regular physical activity to a person’s well-being

  4. #4 peak

    1 m). Calculate the position of the nodes for this wave function. Write the expected electron configurations for the following atoms: Sc, Fe, P, Cs, Eu, Pt, Xe, and Br. Give possible values for the quantum numbers

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. L9:958 excites
  2. L9:1063 suppresses
  3. L3:153 suppresses
  4. L9:364 excites
  5. L3:146 excites
  6. L6:370 suppresses
  7. L4:120 excites
  8. L7:2781 excites
  9. L4:40 weakly excites
  10. L7:368 weakly suppresses
  11. L8:2344 weakly excites
  12. L8:280 weakly suppresses
  13. L5:598 weakly suppresses
  14. L8:2809 weakly suppresses
  15. L9:128 weakly suppresses
  16. L3:266 weakly excites
  17. L8:1624 weakly suppresses
  18. L9:184 weakly excites
  19. L8:1193 weakly suppresses
  20. L7:480 weakly suppresses
  21. L1:2739 weakly suppresses
  22. L9:42 weakly excites
  23. L8:2851 weakly suppresses
  24. L6:1140 weakly suppresses

Feeds later layers

  1. L15:520 suppresses
  2. L12:2817 excites
  3. L12:2083 suppresses
  4. L11:1906 suppresses
  5. L11:2299 weakly excites
  6. L14:2443 weakly excites
  7. L16:848 weakly suppresses
  8. L12:776 weakly suppresses
  9. L11:2911 weakly excites
  10. L13:577 weakly suppresses
  11. L16:1042 weakly excites
  12. L19:1231 weakly suppresses
  13. L22:492 weakly suppresses
  14. L13:1227 weakly excites
  15. L14:796 weakly suppresses
  16. L21:2227 weakly excites
  17. L18:2681 weakly excites
  18. L12:913 weakly suppresses
  19. L16:2374 weakly excites
  20. L18:1546 weakly suppresses
  21. L21:1913 weakly excites
  22. L11:1854 weakly excites
  23. L11:191 weakly suppresses
  24. L12:2703 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 -2.50 and -2.422 values between -2.42 and -2.341 values between -1.18 and -1.101 values between -1.02 and -0.942 values between -0.77 and -0.693 values between -0.69 and -0.615 values between -0.61 and -0.5311 values between -0.53 and -0.4437 values between -0.44 and -0.3684 values between -0.36 and -0.28167 values between -0.28 and -0.20384 values between -0.20 and -0.11771 values between -0.11 and -0.031328 values between -0.03 and 0.05598 values between 0.05 and 0.14298 values between 0.14 and 0.22129 values between 0.22 and 0.3050 values between 0.30 and 0.3816 values between 0.38 and 0.479 values between 0.47 and 0.556 values between 0.55 and 0.631 values between 0.63 and 0.714 values between 0.71 and 0.80offnegativepositiveoften

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. IU
  2. vọng
  3. ��
  4. oug
  5. ears
  6. 越是
  7. 期待
  8. 枞
  9. iores
  10. чер

Suppresses

  1. ations
  2. uler
  3. acje
  4. iners
  5. ation
  6. arcer
  7. asjon
  8. 酺