Neuron Explorer

Qwen3-0.6B

"more" after verbs like 'find out'

A neuron in layer 4 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

Fires on the word "more" in calls to action like "Find out more" or "Learn more." The effect is weak. Its strongest negative cases are the word "buffer" in technical text about Emacs markers. Turning it up gave off-topic text.

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

    an invaluable guide to⏎Find out more⏎Creek Books is a⏎small publisher committed to "teaching kids to

  2. #2 peak

    like goldenrod are bumble bee magnets. So are herbs including lavendar, anise hyssop, motherwort, basil and sage.⏎Want to see more flowers -- and herbs to help bees? Check out this

  3. #3 peak

    2<|endoftext|>David Bourget (Western Ontario)⏎David Chalmers (ANU, NYU)⏎Rafael De Clercq⏎Jack Alan Reynolds⏎Learn more about PhilPapers⏎OUP Oxford (2011)⏎With more

  4. #4 peak

    , and managing some of the practical things, such as getting the right shoes or working with a personal trainer.⏎This guide’s main goal is to help you become more active, get the most from your activities, and do them safely. The key

  5. #5 peak

    track of books you want to read. Want to Read saving…. Want to Read Currently Reading Read. Other editions. Enlarge cover. Error rating book.⏎Browse more videos⏎Human Anatomy and Physiology are two most-related subjects in the field of medical

  6. #6 peak

    . It’s made to help everyone from brand new hobbyists to university Geology students, and includes an eBook, digital tools, a video tutorials. Find out more here!⏎While all obsidian meets this

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: I will have a lot of text in the current session, but I am not sure about the current time zone. So, how to make the current time zone and the current session have a lot? So, the answer is "current"

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

    make-marker)) ⇒ #<marker in no buffer>⏎m1to point between the 99th and 100th characters ;; in the current buffer: (set-marker m1 1

  2. #2 peak

    00) ⇒ #<marker at 100 in markers.texi>⏎;; Now insert one character at the beginning of the buffer: (goto-char (point-min)) ⇒ 1 (insert "Q")

  3. #3 peak

    ’s insertion⏎type (see Marker Insertion Types)—unless the insertion is done⏎insert-before-markers (see Insertion).⏎Insertion and deletion in a buffer must check all the markers and relocate them if necessary. This slows processing in a

  4. #4 peak

    position in that buffer. But the marker’s position value can change during the life of the marker, and often does. Insertion and deletion of text in the buffer relocate the marker. The idea is that a marker positioned between two characters remains between

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. L3:2840 suppresses
  2. L3:115 suppresses
  3. L3:1286 excites
  4. L3:50 excites
  5. L3:464 weakly excites
  6. L3:63 weakly excites
  7. L3:59 weakly excites
  8. L3:1936 weakly excites
  9. L0:1725 weakly excites
  10. L3:1505 weakly suppresses
  11. L3:691 weakly excites
  12. L3:1685 weakly excites
  13. L2:1194 weakly suppresses
  14. L3:139 weakly suppresses
  15. L3:272 weakly excites
  16. L3:2439 weakly excites
  17. L3:1581 weakly excites "the" after "percent of" or "a lot of"
  18. L3:294 weakly excites
  19. L3:133 weakly suppresses
  20. L0:861 weakly suppresses
  21. L1:1464 weakly excites
  22. L2:977 weakly excites
  23. L3:1922 weakly suppresses
  24. L2:111 weakly suppresses

Feeds later layers

  1. L5:355 suppresses
  2. L20:2638 suppresses
  3. L5:1906 suppresses
  4. L6:1501 suppresses
  5. L5:1360 suppresses
  6. L21:998 suppresses
  7. L24:2675 suppresses
  8. L6:2856 suppresses
  9. L24:2622 suppresses
  10. L5:1468 weakly excites
  11. L8:1339 weakly suppresses
  12. L21:3055 weakly suppresses
  13. L6:2619 weakly suppresses
  14. L11:727 weakly excites
  15. L23:2267 weakly suppresses
  16. L9:1034 weakly suppresses
  17. L18:732 weakly suppresses
  18. L24:2550 weakly suppresses
  19. L21:1249 weakly suppresses
  20. L10:813 weakly suppresses
  21. L20:23 weakly excites
  22. L17:2339 weakly suppresses
  23. L5:69 weakly excites
  24. L18:1073 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 -1.24 and -1.181 values between -0.52 and -0.473 values between -0.47 and -0.412 values between -0.41 and -0.3611 values between -0.36 and -0.3018 values between -0.30 and -0.2540 values between -0.25 and -0.19111 values between -0.19 and -0.14273 values between -0.14 and -0.08555 values between -0.08 and -0.031029 values between -0.03 and 0.03841 values between 0.03 and 0.09486 values between 0.09 and 0.14262 values between 0.14 and 0.20143 values between 0.20 and 0.2569 values between 0.25 and 0.3140 values between 0.31 and 0.368 values between 0.36 and 0.427 values between 0.42 and 0.473 values between 0.47 and 0.531 values between 0.53 and 0.582 values between 0.58 and 0.641 values between 0.75 and 0.801 values between 0.91 and 0.97offnegativepositiveoften

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. elkaar
  2. MBER
  3. pheres
  4. Ellis
  5. thers
  6. pher
  7. irma
  8. validated
  9. erialize
  10. isContained

Suppresses

  1. 一个职业
  2. Autom
  3. {text
  4. ;text
  5. resume
  6. .GetText
  7. !/
  8. !)⏎⏎
  9. fullscreen
  10. 壁纸