Neuron Explorer

Qwen3-0.6B

'character' or 'letter' in text-symbol talk

A neuron in layer 19 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 lights up on words like 'character' or 'letters' when the text is about strings of symbols, such as a 5-character password or a set of letters and digits. Turning it up made the model drift into unrelated topics like weather and math problems, so the steering did not show a clear theme.

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

    program please!!:⏎Write an assembly program called hw3_ex1 , stored in file hw3_ex1.asm , that prompts the user to type a 5-character string. The program reads the first 5 characters of that string and prints two

  2. #2 peak

    |sweet)$/igm ^ asserts position at start of a line Non-capturing group (?:chocolate|sweet) 1st Alternative chocolate chocolate matches the characters chocolate literally (case insensitive) 2nd Alternative sweet sweet matches the characters sweet

  3. #3 peak

    did so far:⏎Originally Posted by MrFujin⏎Enter a 5-charcter string: hgout⏎String#1: tough ; ASCII code decremented

  4. #4 peak

    anding Role for Genetics Counselors⏎The completion of the human genome sequencing launches a new era in genetic medicine. With the decoding of the letters that make up the recipe of human life an opportunity now exists for scientists to discover

  5. #5 peak

    32. Match the example shown below, where user input is shown in bold face:⏎Enter a 5-character string: hguot⏎String #1: tough⏎String #2:

  6. #6 peak

    AS), is the first to identify a non-primate species as having “orthographic” abilities.⏎In the experiment, pigeons were trained to peck four-letter English words as they came up on a screen, or to instead peck a

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 number of the first three points of the $ \left( 3 \cdot 3 \ \frac{ \cdot \ 3 \ \cdot 3 \ \cdot 3 \ \cdot 3 \ \cdot 3

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

    that is returned by the function and can be a type C data.⏎- Function name. This is the identifier in which you can use to call your function.⏎- Argument. Parameters that are passed to function of any type of C data.⏎You

  2. #2 peak

    the result is formated the same way as is it usually used with manual calculations.⏎Your task is to write the core part of this calculator. Given two numbers and the requested operation, you are to compute the result and print it in the form

  3. #3 peak

    or functions in Python.⏎Assign strings to variables and use them to print output at certain stages of the program. For example:⏎>>> >>> s1 = 'String in single quotes' >>> s1 'String in single quotes' >>> >>> s2

  4. #4 peak

    need to use the range function to iterate over the array using a for loop. The range() function returns a sequence of numbers starts from a given number to another given number with a given step.⏎It is defined as like below:⏎range(start,

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. L8:1146 suppresses
  2. L11:2835 excites
  3. L6:531 suppresses
  4. L11:619 suppresses
  5. L10:1178 excites
  6. L14:2980 excites
  7. L10:1790 excites
  8. L11:701 suppresses
  9. L9:628 excites
  10. L6:1254 suppresses
  11. L15:1763 excites unclear: pieces of longer words and analogies
  12. L9:1788 suppresses
  13. L13:1582 suppresses
  14. L8:622 excites
  15. L16:1403 suppresses
  16. L7:1811 excites
  17. L9:2688 weakly suppresses
  18. L17:1531 weakly suppresses
  19. L12:1778 weakly suppresses
  20. L7:3041 weakly excites
  21. L6:2254 weakly excites
  22. L6:1518 weakly excites
  23. L6:2793 weakly excites
  24. L12:2213 weakly suppresses

Feeds later layers

  1. L21:1389 excites
  2. L23:626 excites
  3. L20:766 excites
  4. L20:2651 excites
  5. L21:2418 excites
  6. L21:1553 excites
  7. L22:2114 weakly excites
  8. L27:1469 weakly excites
  9. L24:712 weakly excites 'long' in 'long-term' and 'long-lasting'
  10. L24:535 weakly excites
  11. L24:1841 weakly excites
  12. L20:2609 weakly excites
  13. L22:985 weakly excites
  14. L21:2044 weakly suppresses
  15. L21:1101 weakly suppresses
  16. L21:2898 weakly suppresses
  17. L24:2432 weakly suppresses
  18. L21:2751 weakly suppresses
  19. L25:2038 weakly suppresses
  20. L23:1471 weakly excites
  21. L20:1787 weakly excites
  22. L21:960 weakly excites
  23. L23:1008 weakly suppresses
  24. L26:1334 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 -2.45 and -2.121 values between -1.80 and -1.472 values between -1.47 and -1.1528 values between -1.15 and -0.82445 values between -0.82 and -0.501747 values between -0.50 and -0.171443 values between -0.17 and 0.15169 values between 0.15 and 0.4832 values between 0.48 and 0.8014 values between 0.80 and 1.134 values between 1.13 and 1.453 values between 1.45 and 1.784 values between 1.78 and 2.102 values between 2.10 and 2.434 values between 2.43 and 2.751 values between 3.08 and 3.402 values between 3.40 and 3.732 values between 3.73 and 4.051 values between 4.05 and 4.381 values between 4.70 and 5.031 values between 5.03 and 5.351 values between 10.23 and 10.56offnegativepositiveoften

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. 字符串
  2. concatenate
  3. sequence
  4. (CharSequence
  5. string
  6. ="__
  7. sequence
  8. concaten
  9. :String
  10. string

Suppresses

  1. دار
  2. 息
  3. Cove
  4. brows
  5. ga
  6. heet
  7. grad
  8. reen
  9. innacle
  10. ularity