Neuron Explorer

Qwen3-0.6B

file names, logs and figures

A neuron in layer 9 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 responds to technical words about files and data, such as file name, log file, figure, and GeoTIFF files, especially in instructions about how to name or handle them. Turning it up gave text about numbers and tables, but the link is loose.

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

    extension to be appended to the rectified images.⏎The next prompt will ask you whether a camera angle map should be produced and if yes, what should be its name.⏎After that you are asked if overwriting existing maps in the target location and

  2. #2 peak

    using the same data directory and they have logging enabled, you must use the appropriate options to specify log file names that are unique to each server. Otherwise, the servers try to log to

  3. #3 peak

    which should be peer-reviewed articles.⏎Write a 7–8-page paper in Word format. Apply APA standards to citation of sources. Use the following file naming convention: LastnameFirstInitial_M5_A1.doc.⏎By Saturday, August

  4. #4 peak

    you will use over and over it is more convenient to create a script that can run all the commands with one message from you.⏎% filename simple.m⏎ini=1.; %set the variable ini to 1.⏎y0=ini;

  5. #5 peak

    is one area of stem cell biology where human and mouse differ significantly and we never would have discovered this if we had limited our studies to using only mouse embryonic stem cells. Adult human stem cells just didn’t work for this.”⏎The research team

  6. #6 peak

    ,y0)%call the routine ode45⏎plot(t,y) %plot the solution⏎The results give a table of values for t and y and a figure.⏎Next consider the following two ordinary differential equations, which are a model of turbulence

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: How do I know if the expression (a^2) * (b^2) is true? I think that I know. To be a complex function, I need to know the value of the function. So, if I have

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

    illustrated using a single, simple differential equation:⏎Integrate this equation from t = 0 to t = 1. The exact solution can be found by quadrature and is⏎To use MATLAB we first construct an M-file that defines the equation

  2. #2 peak

    . You have a set of candidate solutions (called a population). The best solutions get combined with each other (mating) and also some of those get randomly mutated. This lets the algorithm both use good solutions, but also explore new options.

  3. #3 peak

    LocalSeed. This means that the same random numbers are drawn for the blocks. However, the random numbers are differently transformed according to the selected distributions (uniform and truncated normal distribution), and therefore the blocks have different output values. Simulation results are shown

  4. #4 peak

    theory.⏎Genetic algorithms and neural networks⏎Genetic algorithms used to be a popular method for training neural networks. For example, one

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. L8:2350 weakly suppresses
  2. L5:319 weakly excites
  3. L8:360 weakly suppresses
  4. L7:2033 weakly suppresses
  5. L1:2343 weakly suppresses
  6. L5:76 weakly suppresses
  7. L6:2658 weakly suppresses
  8. L0:794 weakly excites
  9. L7:2417 weakly excites
  10. L8:2969 weakly suppresses
  11. L6:2728 weakly excites
  12. L3:2286 weakly suppresses
  13. L2:2534 weakly suppresses
  14. L6:1996 weakly suppresses
  15. L7:399 weakly excites laboratory and research facility terms
  16. L7:2600 weakly suppresses
  17. L1:1818 weakly suppresses
  18. L2:302 weakly excites
  19. L6:1840 weakly suppresses
  20. L7:829 weakly suppresses
  21. L7:1286 weakly excites
  22. L8:5 weakly suppresses
  23. L8:28 weakly excites
  24. L6:753 weakly suppresses

Feeds later layers

  1. L11:2676 excites
  2. L21:1599 excites
  3. L10:1001 excites
  4. L12:1708 excites
  5. L22:1132 excites
  6. L10:1106 weakly excites
  7. L10:2369 weakly excites
  8. L13:3039 weakly excites
  9. L11:1768 weakly suppresses
  10. L11:1668 weakly excites
  11. L16:2140 weakly excites
  12. L16:3025 weakly excites
  13. L11:2768 weakly excites
  14. L13:1624 weakly excites
  15. L21:1080 weakly excites
  16. L17:1702 weakly excites
  17. L12:1802 weakly suppresses
  18. L15:2767 weakly excites
  19. L13:2176 weakly suppresses
  20. L11:1133 weakly excites
  21. L11:527 weakly excites
  22. L12:2809 weakly suppresses
  23. L10:1249 weakly excites
  24. L11:549 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 -1.04 and -0.991 values between -0.79 and -0.751 values between -0.75 and -0.711 values between -0.71 and -0.671 values between -0.63 and -0.594 values between -0.42 and -0.386 values between -0.38 and -0.3419 values between -0.34 and -0.3030 values between -0.30 and -0.2664 values between -0.26 and -0.22127 values between -0.22 and -0.18160 values between -0.18 and -0.14224 values between -0.14 and -0.10358 values between -0.10 and -0.06403 values between -0.06 and -0.02596 values between -0.02 and 0.03462 values between 0.03 and 0.07449 values between 0.07 and 0.11319 values between 0.11 and 0.15256 values between 0.15 and 0.19175 values between 0.19 and 0.23111 values between 0.23 and 0.2759 values between 0.27 and 0.3138 values between 0.31 and 0.3525 values between 0.35 and 0.395 values between 0.39 and 0.436 values between 0.43 and 0.472 values between 0.47 and 0.524 values between 0.52 and 0.561 values between 0.56 and 0.60offnegativepositiveoften

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. udies
  2. ournal
  3. papers
  4. Files
  5. ashes
  6. /history
  7. udios
  8. .setTitle
  9. ampp
  10. ulg

Suppresses

  1. znal
  2. 动感
  3. irty
  4. 这两种
  5. coercion
  6. enforcement
  7. 有效性
  8. virt
  9. 机动
  10. →Token