Neuron Explorer

Qwen3-0.6B

token before a statistic or number

A neuron in layer 23 of Qwen3-0.6B. Its strongest snippets are fairly consistent: several of them peak on the same token. It is featured, so it has steering outputs.

What this neuron does

This neuron fires on the symbol or blank just before a number in research and measurement text, like the '<' in 'p < .001' or the '$' before '2' in a list of bills. When turned up, the model often produced long strings of dots, so the steering effect is not very informative.

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

    had resolved (mean difference –0.71 pg/mL, 95% CI, –1.09 to –0.34; P < .001)

  2. #2 peak

    national sample. Lifetime use rates for American Indian students were higher than those of the national sample for all substance measures except tranquilizers and amphetamines (all P <.05). The greatest discrepancy was observed for 8th grade students:

  3. #3 peak

    ($0.50), and the dollar ($1). Paper money or banknotes are printed by the Federal Reserve. Today, they come in $1, $2, $5, $10, $20, $50

  4. #4 peak

    attended one of 2 DST performances. Participants demonstrated increased knowledge on 5 fact-based questions (mean improvement range, 19%- 35%; p < .001 for all), and favorable changes on

  5. #5 peak

    10 items addressing perceptions regarding substance use risk (paired t test range, 3.9-9.4; p < .001 for all). Through iterative thematic analysis we developed an alliter

  6. #6 peak

    are not IC rated fixtures should be installed in such a manner that they are at least 1/2 inch away from any rafters and no closer than 1/2 inch from ceiling insulation. When selecting bulbs for either type of fixture

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: In the world, many things are true, and many things are false, and many things are true in some cases and false in other cases, and also, in the world, the number of false and true in total to be equal in

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

    son of the Tordesillas who was put to death by the Comuneros at Seville. He studied in Spain and Italy, and became secretary to Vespasiano Gonzaga, a brother of the Duke of Mantua, who was

  2. #2 peak

    Since the beginning of July 2021, VAUDE’s organic canteen has been completely vegetarian or vegan. Chef Rainer Seibold has always sourced his ingredients regionally, organically and fairly – and he

  3. #3 peak

    is also prevalent in sections of Central America, northern Australia and North America, specifically in sections of Mexico and the state of Florida in the United States.⏎The Köppen Climate Classification subtype for this climate is "Aw". (Tropical Sav

  4. #4 peak

    burg-Western Pomerania, tensions in the CDU-CSU union have intensified. Bavarian State Premier Horst Seehofer has blamed Merkel's refugee policy for the AfD's success.

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. L22:1638 strongly excites
  2. L21:1104 excites
  3. L22:342 suppresses
  4. L21:1735 excites
  5. L20:509 excites
  6. L22:2003 suppresses
  7. L16:1466 suppresses
  8. L12:1098 excites
  9. L19:248 excites
  10. L19:2062 excites
  11. L20:805 suppresses
  12. L22:1177 excites
  13. L14:1152 excites
  14. L21:2187 weakly excites
  15. L9:2915 weakly suppresses
  16. L12:2491 weakly suppresses
  17. L18:569 weakly excites
  18. L20:1682 weakly suppresses
  19. L19:1033 weakly excites
  20. L21:2169 weakly excites
  21. L8:2821 weakly suppresses
  22. L18:3051 weakly suppresses
  23. L20:387 weakly suppresses
  24. L9:18 weakly excites text boundary after a document break

Feeds later layers

  1. L27:1182 strongly excites
  2. L25:725 strongly excites
  3. L26:887 strongly excites
  4. L25:656 excites
  5. L27:703 excites
  6. L26:1825 excites the first dot of an ellipsis
  7. L25:1135 excites
  8. L24:2879 excites
  9. L26:481 suppresses
  10. L26:316 weakly excites
  11. L25:2924 weakly excites
  12. L25:1826 weakly excites
  13. L26:11 weakly suppresses
  14. L24:462 weakly excites
  15. L26:167 weakly excites
  16. L24:2816 weakly suppresses
  17. L26:373 weakly suppresses
  18. L25:412 weakly excites
  19. L25:229 weakly excites
  20. L24:1960 weakly excites
  21. L24:1194 weakly excites
  22. L25:1987 weakly excites
  23. L24:556 weakly excites
  24. L24:1532 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 -21.22 and -20.416 values between -2.57 and -1.76122 values between -1.76 and -0.952053 values between -0.95 and -0.141672 values between -0.14 and 0.6731 values between 0.67 and 1.484 values between 1.48 and 2.295 values between 2.29 and 3.102 values between 3.10 and 3.911 values between 3.91 and 4.731 values between 4.73 and 5.542 values between 5.54 and 6.352 values between 6.35 and 7.162 values between 7.97 and 8.782 values between 8.78 and 9.592 values between 10.40 and 11.21offnegativepositiveoften

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. .
  2. (.
  3. (.
  4. `.
  5. ','.
  6. /.
  7. [.
  8. ,.
  9. -.
  10. ".

Suppresses

  1. uten
  2. Establishment
  3. igung
  4. .Assertions
  5. FAILURE
  6. ixo
  7. bru
  8. .Execute
  9. 马来
  10. すぎ