Neuron Explorer

Qwen3-0.6B

period after 'Dr' abbreviation

A neuron in layer 16 of Qwen3-0.6B. Its strongest snippets are very consistent: they mostly peak on the same token. It is featured, so it has steering outputs.

What this neuron does

This neuron fires on the full stop in 'Dr.' when it comes before a person's name, as in news articles about doctors. Its negative side appears inside programming identifiers, but steering it did not change the writing in an obvious way.

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

    , including the germs that cause cholera and other diarrheal illnesses.⏎Data now suggest that the locations where certain pathogens are found have changed, said Dr. Glenn Morris, the director of the Emerging Pathogens Institute at the University of Florida

  2. #2 peak

    13 Desktop News | Troubled youth in the custody of the Alabama Department of Youth Services have been given a second chance at success thanks to the work of Dr. Randy Salekin, a professor in the Department of Psychology

  3. #3 peak

    10 percent of those people surveyed - including physical therapists, speech and hearing therapists and social workers - were confident in their training in these issues. According to Dr. E. Virginia Laph

  4. #4 peak

    breathalyzer that can test people for COVID-19.⏎The test identifies the disease within 15 seconds. The standard PCR test can take days.⏎Dr. Pelagia-Irene (Perena) Gouma, professor

  5. #5 peak

    be increasingly common -- because evolution's "survival-of-the-fittest" theory should dictate that we'd lose such disabling traits.⏎But an analysis by Dr. Atul Butte and graduate student Erik Corona of Stanford University's School of Medicine

  6. #6 peak

    , the grants are designed to help institutions expand opportunities for student research projects.⏎"We're seeing a profound change in the way many college students learn science," said Dr. Joseph G. Perpich, the institute's vice president for grants and special

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: what is the value of $x^2 - x + 10^x - 10^x + 10^x = x^2 - x + 10^x = x^2 - x =

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

    typedef void * model_3d_ref;⏎model_3d_ref load_model_from_disk(const char * absolute_file_path);⏎void draw_model(model_3d_ref the_model, float

  2. #2 peak

    where_x, float where_y, float where_z);⏎void deallocate_model(model_3d_ref kill_this);⏎The compiler doesn't read your code like this - the following

  3. #3 peak

    as an int, but the function performs a byte per byte search using the unsigned char conversion of this value.⏎- Number of bytes to be analyzed.⏎sizet is an unsigned integral type.⏎A pointer to the first occurrence of value in the

  4. #4 peak

    ⏎But here thing is, save() method can return that primary key id value which is generated by hibernate and we can see it by⏎long s = session.save(k);⏎In this same case, persist() will never give

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. L14:1715 strongly suppresses
  2. L12:1393 excites
  3. L14:1584 suppresses
  4. L14:2023 suppresses
  5. L12:2335 suppresses
  6. L15:574 suppresses
  7. L9:1678 suppresses
  8. L12:1662 suppresses
  9. L8:51 excites
  10. L9:214 excites
  11. L15:1885 suppresses
  12. L14:1636 excites
  13. L14:1796 suppresses
  14. L13:1979 excites
  15. L15:2985 excites
  16. L14:1229 excites
  17. L15:48 weakly excites
  18. L14:1474 weakly excites
  19. L12:2947 weakly excites
  20. L13:2311 weakly suppresses
  21. L8:1503 weakly excites
  22. L1:1365 weakly suppresses
  23. L4:1597 weakly excites
  24. L14:166 weakly excites

Feeds later layers

  1. L17:1158 strongly suppresses
  2. L17:1230 strongly excites
  3. L20:1028 suppresses
  4. L20:2317 suppresses
  5. L19:842 suppresses
  6. L21:1347 suppresses
  7. L18:1092 suppresses
  8. L19:1512 suppresses
  9. L19:1218 suppresses
  10. L17:2715 suppresses
  11. L18:2544 suppresses
  12. L22:918 suppresses
  13. L19:2241 suppresses
  14. L19:1640 suppresses
  15. L19:2038 suppresses
  16. L18:831 suppresses
  17. L20:3046 suppresses
  18. L22:1534 suppresses
  19. L17:1943 suppresses
  20. L21:2053 excites
  21. L21:1664 suppresses
  22. L18:2944 suppresses
  23. L18:1422 suppresses
  24. L18:754 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 -2.43 and -2.1150 values between -0.82 and -0.50705 values between -0.50 and -0.182364 values between -0.18 and 0.14723 values between 0.14 and 0.4653 values between 0.46 and 0.794 values between 0.79 and 1.111 values between 1.11 and 1.432 values between 1.43 and 1.752 values between 1.75 and 2.071 values between 3.36 and 3.681 values between 9.79 and 10.111 values between 10.11 and 10.43offnegativepositiveoften

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. 不排除
  2. felt
  3. ussian
  4. 的脚步
  5. che
  6. ubuntu
  7. ple
  8. iti
  9. application
  10. usses

Suppresses

  1. temp
  2. current
  3. =result
  4. نع
  5. morning
  6. kald
  7. chase
  8. MOVED
  9. =temp
  10. aved