Neuron Explorer

Qwen3-0.6B

'that' after a thing being defined

A neuron in layer 2 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 "that" right after a noun that is being described, as in "a server-side scripting language that..." or "a computer code that...". It is a signal that a description is about to follow. Turning it up did not give a clear, consistent change in the output.

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

    every databases – MySQL, ORACLE, PostgreSQL, Sybase, Informix to name a few.⏎What is PHP?⏎- PHP is a server-side scripting language that means its code is executed on

  2. #2 peak

    ynthesis.⏎Both tree species showed significant drought effects<|endoftext|>Introduction to Kubernetes⏎Kubernetes, also known as K8s or Kube, is an open-source system that helps organizations manage cloud-based containerized applications. The system helps with

  3. #3 peak

    Regional Broadband Global Area Network (RBGAN) mean?⏎Regional broadband global area network (RBGAN) is a now-defunct IP-based, shared carrier service that was offered on a regional basis. The service was provided by network mobile satellite company

  4. #4 peak

    we be willing to change who we are and how we behave in order to meet the expectations of those around us?⏎Summary: Animal Behaviour is an animated short film that centres around a group therapy session attended by animals, who are attempting to overcome their

  5. #5 peak

    alin. Amygdalin is a chemical ingredient found in the pits of many fruits, raw nuts, and plants (see Question

  6. #6 peak

    identified in several microorganisms. In these cases the self-assembly of the protein is advantageous to the organism. For example, hydrophobins are fungal proteins that self-assemble in an ordered manner into amphipathic films at air:

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 will be thinking about that. "I will be thinking about that." So I will be thinking about that." So I will be thinking about that. So I will be thinking about that. So I will be thinking about that.

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

    .⏎Edmund was educated at Wakefield Grammar School and University College, Oxford. He became a fellow of Magdalen College and while there, made a name for himself as a

  2. #2 peak

    , is stranger: they became symbols.⏎There is a single species of palm native to the entire state of California, the California fan palm, which is a big one with what looks like a fuzzy beard of brown leaves underneath its green fronds.

  3. #3 peak

    it, including the numerous myths and confusing science that pervade the online world.⏎Strauss is a distinguished professor in the Oregon State College of Forestry and a fellow

  4. #4 peak

    . Today, that number is 25 percent—still half the rate of the global average.1⏎Gayle Tzemach Lemmon, a senior fellow at the Council on Foreign Relations as well as a journalist and the best-selling author

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. L1:1100 weakly suppresses
  2. L1:1064 weakly suppresses
  3. L1:2254 weakly suppresses
  4. L1:584 weakly excites
  5. L1:1090 weakly excites
  6. L0:2144 weakly suppresses
  7. L1:1857 weakly suppresses
  8. L1:1464 weakly excites
  9. L0:1748 weakly excites
  10. L1:290 weakly excites
  11. L1:201 weakly excites
  12. L1:719 weakly excites
  13. L1:1490 weakly suppresses
  14. L1:917 weakly excites
  15. L1:248 weakly excites
  16. L0:1289 weakly suppresses
  17. L0:340 weakly suppresses
  18. L0:2180 weakly suppresses
  19. L1:139 weakly suppresses
  20. L1:1340 weakly suppresses
  21. L0:2539 weakly suppresses
  22. L0:809 weakly suppresses
  23. L0:325 weakly suppresses
  24. L1:580 weakly suppresses

Feeds later layers

  1. L3:710 excites
  2. L4:139 excites
  3. L4:406 excites
  4. L6:168 weakly suppresses
  5. L10:724 weakly excites
  6. L6:1579 weakly suppresses
  7. L12:422 weakly excites
  8. L7:1743 weakly excites
  9. L18:788 weakly suppresses
  10. L5:898 weakly excites
  11. L15:2632 weakly excites
  12. L6:580 weakly excites
  13. L22:2827 weakly suppresses
  14. L9:2591 weakly suppresses
  15. L10:2920 weakly excites
  16. L6:1449 weakly excites
  17. L7:2770 weakly suppresses
  18. L17:2287 weakly suppresses
  19. L7:198 weakly suppresses
  20. L15:1604 weakly suppresses
  21. L15:1098 weakly excites
  22. L10:3053 weakly suppresses
  23. L6:1404 weakly suppresses
  24. L4:419 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 -1.51 and -1.412 values between -1.31 and -1.204 values between -1.10 and -0.992 values between -0.79 and -0.683 values between -0.68 and -0.586 values between -0.58 and -0.4722 values between -0.47 and -0.3747 values between -0.37 and -0.27147 values between -0.27 and -0.16565 values between -0.16 and -0.062117 values between -0.06 and 0.05706 values between 0.05 and 0.15178 values between 0.15 and 0.2557 values between 0.25 and 0.3616 values between 0.36 and 0.4612 values between 0.46 and 0.578 values between 0.57 and 0.674 values between 0.67 and 0.772 values between 0.77 and 0.882 values between 0.88 and 0.983 values between 0.98 and 1.091 values between 1.29 and 1.401 values between 1.40 and 1.501 values between 1.61 and 1.711 values between 2.54 and 2.65offnegativepositiveoften

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. mente
  2. ány
  3. �
  4. Schwartz
  5. กรรม
  6. ちな
  7. ^
  8. dimensions
  9. Fragment
  10. ly

Suppresses

  1. ISTIC
  2. mata
  3. Sql
  4. Mata
  5. -Cal
  6. eti
  7. egration
  8. rô
  9. istica
  10. ifu