Neuron Explorer

Qwen3-0.6B

sentence-end punctuation in speech

A neuron in layer 19 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 full stop or question mark that closes a sentence, especially in spoken or explanatory talk. When it was pushed down the model fell into repeating filler like 'in fact' and 'indeed'.

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

    the only reason I can acturally comment on it is because I have my C book here with an example of a recursive function to calculate factorials... heh. Sadly I don't think I'm *allowed* to post the code up.

  2. #2 peak

    say I think … yeah. I think you’re talking about more than 80 million people.⏎Wow. Yeah. Okay. Can you define it for us? Can you describe what insulin resistance is in layman’s terms?⏎So to understand

  3. #3 peak

    have made a lot of cattle grazing land. And as a result their natural resources such as wallabies are disappearing off the land and they’ve got to eat something. And so sheep are easy to catch and there’s a lot of meat on them

  4. #4 peak

    you all are waiting for. Teachers, parents and student, now you will find below the Solutions of Class 10 Science Chapter 4 Environmental management questions answers. From the subject English of Maharashtra State Board.⏎Below you can see

  5. #5 peak

    -happy all of the time? No. But, they have a better chance than the children of non-authoritative parents.⏎Sounds kind of like authoritative, right? Sure. In reality this is a very different type of parenting style. Like authoritative

  6. #6 peak

    , the more vegetables kids will eat.⏎"It's not just mom or dad is making me eat it, but I want to eat it because I helped grow it. If a child pulls a carrot out of the dirt they're more likely to eat

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 fact, in fact, in fact, in fact, in fact, in fact, in fact, in fact, in fact, in fact, in fact, in fact, in fact, in fact, in fact, in fact,

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

    with are called one-to-one functions. DEFINITION A function is called a one-to-one function if it never takes on the same value twice; that is, If a horizontal line intersects the graph of in more than one point

  2. #2 peak

    what a third neural network named Eve learns from eavesdropping on the communication between Alice and Bob. We do not prescribe specific cryptographic algorithms to these neural networks; instead, we train end-to-end, adversarially. We demonstrate that the

  3. #3 peak

    important to clarify what exactly a percentage is, and this is nothing more than a quantity or number that represents a fraction of the number 100.⏎In other words, it is a part of a general number that should be considered divided by

  4. #4 peak

    izations. These unique doublets give the axoneme an inherent structural polarity. All three specializations are present in the proximal portion of the axoneme; based on their frequency in random cross-sections of isolated axonemes, 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. L14:155 excites sentence-ending periods in prose
  2. L11:132 excites period ending an opening topic sentence
  3. L13:53 excites period ending a claim before elaboration
  4. L17:347 excites full stops ending informative sentences
  5. L18:227 suppresses
  6. L9:2447 excites
  7. L10:612 excites full stop ending boilerplate or a title
  8. L9:16 suppresses unclear: periods inside numbers and names
  9. L5:168 suppresses
  10. L18:2434 excites
  11. L17:944 suppresses
  12. L6:250 weakly excites
  13. L18:2613 weakly suppresses
  14. L15:403 weakly suppresses period inside an abbreviation
  15. L16:432 weakly suppresses period after month or initial
  16. L1:1384 weakly suppresses
  17. L9:12 weakly suppresses
  18. L16:1112 weakly suppresses
  19. L16:1545 weakly excites
  20. L18:2658 weakly suppresses
  21. L14:1187 weakly suppresses
  22. L17:348 weakly excites
  23. L8:304 weakly suppresses
  24. L4:102 weakly excites

Feeds later layers

  1. L22:1416 suppresses
  2. L22:1729 suppresses
  3. L21:1490 excites
  4. L20:727 suppresses
  5. L20:173 suppresses
  6. L21:809 suppresses
  7. L20:2891 suppresses
  8. L24:887 suppresses
  9. L22:885 excites
  10. L21:1132 suppresses
  11. L20:513 weakly excites
  12. L20:1519 weakly suppresses
  13. L20:2354 weakly excites
  14. L20:2634 weakly suppresses
  15. L21:642 weakly suppresses
  16. L26:236 weakly suppresses
  17. L20:2718 weakly suppresses
  18. L20:501 weakly excites
  19. L22:2020 weakly suppresses
  20. L27:1084 weakly suppresses
  21. L21:1070 weakly suppresses
  22. L21:2135 weakly suppresses
  23. L21:1117 weakly excites
  24. L27:677 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.

2 values between -16.41 and -15.802 values between -12.19 and -11.582 values between -11.58 and -10.981 values between -10.98 and -10.381 values between -10.38 and -9.773 values between -9.77 and -9.174 values between -9.17 and -8.572 values between -8.57 and -7.963 values between -7.96 and -7.369 values between -7.36 and -6.766 values between -6.76 and -6.164 values between -6.16 and -5.5513 values between -5.55 and -4.9511 values between -4.95 and -4.3515 values between -4.35 and -3.7420 values between -3.74 and -3.1417 values between -3.14 and -2.5419 values between -2.54 and -1.9331 values between -1.93 and -1.3344 values between -1.33 and -0.73466 values between -0.73 and -0.132773 values between -0.13 and 0.48425 values between 0.48 and 1.0817 values between 1.08 and 1.696 values between 1.69 and 2.294 values between 2.29 and 2.892 values between 2.89 and 3.491 values between 3.49 and 4.102 values between 4.10 and 4.701 values between 5.91 and 6.511 values between 6.51 and 7.111 values between 7.11 and 7.72offnegativepositiveoften

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. because
  2. 因为它
  3. -purpose
  4. Purpose
  5. sie
  6. Few
  7. covering
  8. lander
  9. aña
  10. way

Suppresses

  1. indeed
  2. 实际上
  3. moreover
  4. 确实
  5. actually
  6. действительно
  7. furthermore
  8. 事实上
  9. 的实际
  10. 確實