Neuron Explorer

Qwen3-0.6B

"a" or "an" after "such" or launch verbs

A neuron in layer 6 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 small words "a" or "an" that come right after words like "such", "launched", or "creating", where a noun phrase is about to start. When it is turned down, it tends to pick up words like "such cases" instead. Steering effects were not clearly tied 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

    spaceflight program lacks a clear goal, policymakers and the public will say that NASA is adrift, even if astrophysicists and planetary scientists continue launching a panoply of missions. Moreover, scientists suffer programmatic whiplash whenever

  2. #2 peak

    to boost manufacturing productivity while reducing production costs as well.⏎That’s the short version.⏎Now you might be wondering the ins and outs of cobots and why such a solution could be

  3. #3 peak

    )⏎• A report involves a target audience (audience)⏎It is of paramount importance to remember when preparing a technical report that one of the objectives for writing such a report is to communicate to others (audience) the nature, as well as

  4. #4 peak

    credentials may not reflect specialization in this area of medical practice.⏎The skin is the largest organ of the human body, and as a result, dermatologists typically offer a wide variety of treatments for it.⏎Whether you wish to correct wrinkles, crepe

  5. #5 peak

    skype, artificial intelligence, and let’s not forget insect cyborgs. Yes, that is correct, scientists are well at work at creating an insect that is part machine and can carry out the demands from humans operating them via

  6. #6 peak

    the UK’s first permanent sculptures to honour the Windrush generation.⏎Hackney's Diverse Curriculum - the Black Contribution is also vital to understanding why we have launched a review into the naming of our public spaces and why the names of historical slave owners

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: 1.0 1.0 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1

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

    These suspended droplets won’t react much with the steel surface of the pipes.⏎Problems can arise in stretches of pipeline that run at lower pressures. In these spots, water droplets can coalesce and fall out of the oil flow. They

  2. #2 peak

    Every data-driven trend comes with vulnerabilities that, if left unchecked and unprotected, can cause great damage.⏎In such cases, no one can escape the horrifying consequences of security attacks where both businesses and their

  3. #3 peak

    For instance, did the caller ask for the data in this format or that format?⏎Sometimes you'll want all of these variations at the same time. In such cases, I have found that using a dict for your return value is a good solution

  4. #4 peak

    benefited from this Ritchie Dental Group blog article and look forward to your next visit.<|endoftext|>Sometimes it makes sense for a function to return multiple things. In such cases, it's common to just return them in a tuple: "return (count

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. L5:249 excites "a" right after "as"
  2. L5:93 suppresses
  3. L4:710 excites
  4. L5:445 weakly excites
  5. L5:483 weakly excites
  6. L3:125 weakly suppresses verb right after a subject in clauses
  7. L5:848 weakly suppresses
  8. L5:705 weakly suppresses
  9. L5:167 weakly suppresses
  10. L2:1962 weakly excites
  11. L5:246 weakly suppresses
  12. L5:1397 weakly excites
  13. L3:2632 weakly suppresses
  14. L1:886 weakly excites
  15. L4:182 weakly suppresses
  16. L0:51 weakly excites
  17. L0:2873 weakly excites
  18. L5:151 weakly suppresses
  19. L4:281 weakly suppresses
  20. L5:234 weakly excites
  21. L5:1199 weakly excites
  22. L0:602 weakly excites words right before a number
  23. L4:229 weakly excites
  24. L0:104 weakly excites

Feeds later layers

  1. L8:1056 suppresses
  2. L8:330 suppresses
  3. L8:1409 excites
  4. L14:2159 suppresses
  5. L9:55 suppresses
  6. L20:932 excites 'of' after a count or quantity
  7. L7:1830 excites
  8. L12:1990 weakly excites
  9. L10:1309 weakly suppresses
  10. L19:3042 weakly excites
  11. L10:1031 weakly excites
  12. L9:796 weakly excites
  13. L10:2416 weakly suppresses
  14. L7:561 weakly excites 'all' or 'every' before a group
  15. L7:562 weakly excites
  16. L7:1120 weakly suppresses
  17. L10:633 weakly suppresses
  18. L7:267 weakly excites
  19. L8:569 weakly excites
  20. L7:103 weakly suppresses
  21. L10:2001 weakly excites
  22. L18:2282 weakly suppresses
  23. L11:119 weakly suppresses
  24. L12:3045 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 -2.18 and -2.061 values between -2.06 and -1.942 values between -1.82 and -1.703 values between -1.70 and -1.581 values between -1.58 and -1.461 values between -1.46 and -1.345 values between -1.34 and -1.225 values between -1.22 and -1.104 values between -1.10 and -0.987 values between -0.98 and -0.8616 values between -0.86 and -0.7424 values between -0.74 and -0.6230 values between -0.62 and -0.5054 values between -0.50 and -0.38124 values between -0.38 and -0.25358 values between -0.25 and -0.131121 values between -0.13 and -0.011596 values between -0.01 and 0.11321 values between 0.11 and 0.23113 values between 0.23 and 0.3543 values between 0.35 and 0.4731 values between 0.47 and 0.5914 values between 0.59 and 0.719 values between 0.71 and 0.837 values between 0.83 and 0.957 values between 0.95 and 1.072 values between 1.07 and 1.192 values between 1.19 and 1.311 values between 1.31 and 1.441 values between 1.44 and 1.561 values between 1.68 and 1.801 values between 1.80 and 1.921 values between 2.04 and 2.161 values between 2.52 and 2.64offnegativepositiveoften

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. iterals
  2. Authority
  3. 熨
  4. ара
  5. {!
  6. 仅代表
  7. iance
  8. 仅供参考
  9. 税务总局
  10. notation

Suppresses

  1. очных
  2. 为空
  3. CPP
  4. éric
  5. OMB
  6. communic
  7. omb
  8. uber
  9. を開
  10. umb