Neuron Explorer

Qwen3-0.6B

programming languages and software names

A neuron in layer 1 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 the names of programming languages, number crunching tools and software, such as NumPy, Octave, Clojure, R and GIMP. Turning it up made the model's writing drift into repetitive, rambling text rather than anything about code.

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

    programming language for statistics computing. These tools, especially their open source packages, have gained popularity among programmers in scientific and statistic computing. A fundamental component in both NumPy/SciPy and R are the linear algebra functions, which rely on the standard

  2. #2 peak

    time.⏎You should give examples from dynamic typed languages instead, e.g. clojure.⏎Here's one example:<|endoftext|>The commerce of the ancient world was gathered into

  3. #3 peak

    language. Octave has extensive tools for solving common numerical linear algebra problems, finding the roots of nonlinear equations, functions written in Octave's own language, or by using dynamically loaded modules written in C, C++,

  4. #4 peak

    then move into more advanced topics such as neural networks and tree-based methods.⏎Finally, you’ll delve into the frontier of machine learning, using the caret package in R. Once you develop a familiarity with topics such as the difference between regression and classification

  5. #5 peak

    will give a quick tutorial on how to extend NumPy/SciPy and R by writing a wrapper for an Intel MKL function and then use it from NumPy/SciPy or R programs.<|endoftext|>by Roman Davidov⏎(revisited from

  6. #6 peak

    |⏎|Required to build||libstdc++|⏎|Required to build||fortran|⏎This entry (in part or in whole) was last reviewed on 4

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:1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.

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

    Line using Visual C++ 2008⏎- Select Start >> All Programs >> Microsoft Visual C++ 2008 Express Edition >> Visual Studio Tools >> Visual Studio 2008 Command Prompt (Run with administrator privileges)⏎

  2. #2 peak

    by DomainKeys Identified Mail (DKIM), before adding this signature to any outgoing mail it is essential that you generate a domain key which will be utilized by Google Apps to produce encrypted mail headers which are distinctive to your domain. Further, the mail

  3. #3 peak

    you in the DNS records for your domain.⏎If your domain already has a DMIK domain key, then another key has to be produced to use with Google Apps and the domain key for Google apps can be differentiated from other keys through selector prefix

  4. #4 peak

    kinds of powerful, dynamic applications and services. For more information on this great topic, please consult:⏎- Sun's Java Server Pages home (http://java.sun.com/products/jsp/)⏎- Westy Rockwell's article on "JSP Tag

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. L0:737 excites
  2. L0:1301 suppresses
  3. L0:9 suppresses
  4. L0:1384 suppresses
  5. L0:25 excites
  6. L0:178 suppresses
  7. L0:1224 suppresses
  8. L0:1420 excites
  9. L0:2041 excites
  10. L0:1658 excites
  11. L0:398 suppresses
  12. L0:2489 suppresses
  13. L0:882 suppresses
  14. L0:2906 suppresses
  15. L0:991 weakly excites
  16. L0:1559 weakly excites
  17. L0:834 weakly suppresses
  18. L0:413 weakly suppresses
  19. L0:2019 weakly suppresses
  20. L0:2163 weakly suppresses
  21. L0:1292 weakly suppresses
  22. L0:97 weakly excites
  23. L0:1046 weakly excites
  24. L0:321 weakly excites

Feeds later layers

  1. L2:232 excites
  2. L2:1289 excites
  3. L2:795 excites
  4. L14:462 weakly excites
  5. L2:862 weakly suppresses
  6. L2:260 weakly suppresses
  7. L2:2174 weakly suppresses
  8. L6:56 weakly excites
  9. L4:369 weakly excites
  10. L7:2866 weakly excites
  11. L2:127 weakly excites
  12. L17:3048 weakly suppresses
  13. L9:2808 weakly excites
  14. L2:691 weakly suppresses
  15. L2:1789 weakly suppresses
  16. L24:1908 weakly excites
  17. L2:29 weakly suppresses
  18. L10:407 weakly excites
  19. L5:893 weakly excites
  20. L2:367 weakly excites
  21. L2:1387 weakly excites
  22. L22:2273 weakly excites
  23. L21:2327 weakly excites
  24. L15:544 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.

12 values between -0.46 and -0.3837 values between -0.38 and -0.30129 values between -0.30 and -0.22350 values between -0.22 and -0.131116 values between -0.13 and -0.052132 values between -0.05 and 0.03111 values between 0.03 and 0.1213 values between 0.12 and 0.202 values between 0.20 and 0.281 values between 0.28 and 0.362 values between 0.36 and 0.451 values between 0.53 and 0.611 values between 2.27 and 2.351 values between 2.76 and 2.85offnegativepositiveoften

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. wi
  2. offsets
  3. ERN
  4. 的重大
  5. slack
  6. 了自己的
  7. 为之
  8. scor
  9. awi
  10. owering

Suppresses

  1. :⏎⏎
  2. [ ⏎
  3. asin
  4. ?⏎⏎
  5. -plan
  6. { ⏎ ⏎
  7. ([ ⏎
  8. 磙
  9. ; ⏎ ⏎
  10. !⏎⏎