Neuron Explorer

Qwen3-0.6B

'of' after publisher or division nouns

A neuron in layer 10 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 word 'of' when it follows words like 'reprint', 'imprint' or 'division', as in 'an imprint of Perseus Books'. It was quiet or negative on words that name a relation like 'replicas' or 'counterpart'. Turning it up gave text with no clear link to this pattern.

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

    fact and to convinced ideas of rationality, its scope, and its effects for motion and enterprise.⏎This Elibron Classics publication is a facsimile reprint of a 1901 variation by means of the Macmill<|endoftext|>This question

  2. #2 peak

    arian publication is a facsimile reprint of the unique. because of its age, it could comprise imperfections similar to marks

  3. #3 peak

    -reactive chemicals used the well-known actinic light activated cross linking or hardening<|endoftext|>Quick Guide to AI and ML Universe for Business Leaders⏎This article was published as a part of the Data Science Blogathon.⏎Ever since the advent of Globalisation

  4. #4 peak

    (36 pairs)⏎Box size: 3 x 3 x 4¼ in.⏎This item is published by PomegranateKids®, an imprint of Pomegranate Communications, and is CPSIA compliant.⏎⚠️WARNING:

  5. #5 peak

    observatory in a site monitoring mode. The sensor array includes advanced conductivity, turbidity, pressure (depth) and current. Aanderaa, a division of ITT Analytics, has

  6. #6 peak

    counter to Charlotte Brontë's “Jane Eyre” — is considered an integral part of the literary canon, but what makes her so great?⏎In celebration of the Royal Society of Literature’s 200th birthday, and the

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: the role of a small in the world. The small of a small. The small of a small, etc. But this is a small. The small. The small. The small. The small. The small. The small. The

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

    dynamic automated tutor. This tutor helps students, in a dialogue, to think through arguments in a strategic and systematic fashion.⏎In the TruthLab, the semantic counterpart to the ProofLab, students practice techniques for semantic analysis of formulae and arguments.

  2. #2 peak

    between the divine and the human realm.⏎As expected, the goddess was given the name Iris, which is the Greek word for “rainbow.” Her name lives on in English in words like “ir<|endoftext|>Insight from WFPD: Defensive

  3. #3 peak

    technologies (PAT) can help digitally clone manufacturing processes⏎There are a plethora of definitions and interpretations of what digital twins are. At their simplest, they are virtual replicas of physical entities that can be used to understand or predict real-world outcomes. These

  4. #4 peak

    reveals how “ghost glaciers” have protected Greenland’s ancient bedrock landscapes from the island’s ice sheet.⏎“These ghost glaciers come and go, and leave very little evidence of their presence,” Corbett tells LiveScience. “There are indications that these

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. L8:968 excites
  2. L5:1721 excites
  3. L9:750 excites
  4. L8:2256 suppresses
  5. L9:2885 excites
  6. L4:1536 suppresses
  7. L9:2035 excites
  8. L7:2869 excites
  9. L8:3067 suppresses
  10. L8:1359 excites
  11. L7:2458 weakly excites
  12. L8:2100 weakly excites
  13. L5:1427 weakly suppresses
  14. L3:1651 weakly suppresses "on" in "went on to"
  15. L8:1502 weakly suppresses
  16. L8:2519 weakly excites
  17. L8:1156 weakly suppresses
  18. L4:934 weakly suppresses
  19. L9:2200 weakly excites
  20. L4:1379 weakly suppresses
  21. L4:1133 weakly excites
  22. L8:825 weakly excites
  23. L9:1228 weakly excites
  24. L5:1633 weakly suppresses

Feeds later layers

  1. L16:1159 suppresses
  2. L16:961 suppresses
  3. L20:926 suppresses
  4. L19:1410 suppresses
  5. L11:1383 excites
  6. L20:3010 suppresses
  7. L22:1993 suppresses
  8. L11:2949 suppresses
  9. L20:1478 suppresses
  10. L17:460 suppresses unclear: word before 'first', 'last' or 'further'
  11. L13:1971 suppresses
  12. L11:2349 weakly suppresses
  13. L13:2536 weakly suppresses
  14. L13:71 weakly excites
  15. L11:1040 weakly suppresses
  16. L15:2284 weakly excites
  17. L14:1020 weakly suppresses
  18. L14:832 weakly suppresses modifier words before their head word
  19. L16:2270 weakly excites
  20. L12:1666 weakly excites
  21. L14:2458 weakly excites
  22. L17:488 weakly suppresses
  23. L15:1892 weakly suppresses
  24. L13:311 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 -3.89 and -3.771 values between -3.18 and -3.072 values between -2.60 and -2.482 values between -1.78 and -1.661 values between -1.66 and -1.551 values between -1.55 and -1.431 values between -1.43 and -1.313 values between -1.31 and -1.192 values between -1.19 and -1.084 values between -1.08 and -0.9612 values between -0.96 and -0.847 values between -0.84 and -0.7315 values between -0.73 and -0.6111 values between -0.61 and -0.4929 values between -0.49 and -0.3856 values between -0.38 and -0.26161 values between -0.26 and -0.14649 values between -0.14 and -0.021462 values between -0.02 and 0.09935 values between 0.09 and 0.21411 values between 0.21 and 0.33116 values between 0.33 and 0.4419 values between 0.44 and 0.564 values between 0.56 and 0.683 values between 0.68 and 0.80offnegativepositiveoften

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. ago
  2. anker
  3. AGO
  4. ẫu
  5. 所
  6. cleanliness
  7. ANGO
  8. samp
  9. sandbox
  10. -rated

Suppresses

  1. rets
  2. fcn
  3. .pref
  4. prêt
  5. 预制
  6. OperationException
  7. Peripheral
  8. chants
  9. ッグ
  10. httpRequest