Neuron Explorer

Qwen3-0.6B

'of' after a title or position noun

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 responds to the word 'of' when it comes right after a job title, rank or position, as in 'the 38th president of' or 'the 27th state of'. Turning it up did not produce text that clearly showed this, so the effect is unclear.

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

    January, 2009), a German canton of Switzerland (March, 2009), and the Piemonte-Toscana region of northern/central Italy (images only, February-March, 200

  2. #2 peak

    Secretary of State Hillary Clinton, the world would have laughed or stood in horror as it listened to a metaphorical dragon.⏎Just how can a candidate to the leadership of the political party, express such<|endoftext|>Presentation on theme: "Case Review What does

  3. #3 peak

    in his job, from being the Commander-In-Chief to the Guardian of the Economy.⏎Brief biography of Gerald R. Ford, the 38th president of the United States of America.⏎Timeline of the life of former U.S. President

  4. #4 peak

    superparamagnetic iron oxide nanoparticles (SPIONs), attached to therapeutic cells, show promise. Magnetic devices could be used to move such cells to diseased areas of the body. But current ways of attaching SPIONs to therapeutic cells are difficult

  5. #5 peak

    in exchange for the American renunciation of any claims on Texas. On March 3, 1845, Florida became the 27th state of the United States of America.⏎Until the mid-twentieth century Florida was the least

  6. #6 peak

    descent today."⏎On 25 March 2007, bicentenary of the Abolition of the Slave Trade, then Prime Minister Tony Blair of Britain publicly stated that slavery was "a crime against humanity" and offered "deep

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 have been using a technique to create a model of a specific segment of the segment of the segment, where is that technique based on the segment of the segment of the segment is, I have used the segment of the segment of the segment

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

    its children. Visiting a node usually involves transforming it in some way or collecting data from it.⏎In "pre-order traversal", a node is visited __before__ its children. In "post-order" traversal, a node is visited __after

  2. #2 peak

    This tiny difference of starting values caused the equations to spit out a completely different set of answers. To mathematicians this effect is known as sensitive dependence on initial conditions and it is one of the hallmarks of mathematical chaos. It means

  3. #3 peak

    and a 38.7% improve in depression throughout the 90 days following lively shooter drills, in contrast with the 90 days earlier than the drills.⏎The authors of this research, together with Mai ElSherief, PhD

  4. #4 peak

    For each value in the dataset, we pair its right-most digit to the corresponding stem that matches it's left digit. For example, if we had a⏎76 in our dataset, we would pair it with the⏎7 leaf in the

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. L14:1785 excites
  2. L15:1476 suppresses
  3. L15:447 excites
  4. L15:1594 suppresses
  5. L15:408 excites
  6. L14:739 suppresses 'the' or 'your' before a body part or place
  7. L15:1627 weakly excites
  8. L14:543 weakly suppresses
  9. L15:673 weakly excites
  10. L13:2925 weakly excites
  11. L12:162 weakly excites
  12. L14:1865 weakly suppresses
  13. L13:289 weakly excites
  14. L15:1375 weakly excites unclear: mid-sentence function words
  15. L13:902 weakly excites
  16. L15:2014 weakly suppresses
  17. L11:629 weakly excites
  18. L15:558 weakly suppresses
  19. L15:1458 weakly suppresses
  20. L4:669 weakly suppresses
  21. L11:21 weakly suppresses
  22. L15:2295 weakly suppresses
  23. L15:2404 weakly excites
  24. L7:2232 weakly excites

Feeds later layers

  1. L17:1888 excites
  2. L17:2126 excites
  3. L17:2749 excites
  4. L20:1038 excites
  5. L19:2602 suppresses
  6. L17:2022 excites
  7. L17:1193 excites
  8. L17:655 weakly excites
  9. L17:1460 weakly suppresses
  10. L20:584 weakly suppresses
  11. L21:965 weakly excites
  12. L19:2279 weakly suppresses
  13. L17:138 weakly excites
  14. L20:731 weakly suppresses
  15. L21:666 weakly excites
  16. L19:1344 weakly excites
  17. L21:971 weakly suppresses
  18. L17:1796 weakly suppresses
  19. L21:920 weakly excites
  20. L17:489 weakly excites
  21. L21:2706 weakly excites
  22. L17:2014 weakly suppresses
  23. L17:1060 weakly excites
  24. L18:2108 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.

2 values between -2.06 and -1.912 values between -1.91 and -1.773 values between -1.77 and -1.632 values between -1.63 and -1.483 values between -1.48 and -1.344 values between -1.34 and -1.2016 values between -1.20 and -1.0528 values between -1.05 and -0.9128 values between -0.91 and -0.7751 values between -0.77 and -0.63111 values between -0.63 and -0.48185 values between -0.48 and -0.34383 values between -0.34 and -0.20783 values between -0.20 and -0.051472 values between -0.05 and 0.09367 values between 0.09 and 0.23166 values between 0.23 and 0.38115 values between 0.38 and 0.5251 values between 0.52 and 0.6649 values between 0.66 and 0.8023 values between 0.80 and 0.9523 values between 0.95 and 1.0916 values between 1.09 and 1.237 values between 1.23 and 1.385 values between 1.38 and 1.523 values between 1.66 and 1.802 values between 1.80 and 1.952 values between 1.95 and 2.093 values between 2.09 and 2.231 values between 2.23 and 2.381 values between 2.81 and 2.951 values between 3.52 and 3.66offnegativepositiveoften

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. world
  2. world
  3. -world
  4. ORLD
  5. life
  6. orld
  7. Big
  8. .invokeLater
  9. _world
  10. big

Suppresses

  1. 贝壳
  2. =form
  3. ?」
  4. =out
  5. :class
  6. oref
  7. ---</
  8. orna
  9. essian
  10. `t