Neuron Explorer

Qwen3-0.6B

adjectives right before a noun

A neuron in layer 1 of Qwen3-0.6B. Its strongest snippets are mixed: they peak on different tokens. It is featured, so it has steering outputs.

What this neuron does

This neuron fires on a single descriptive word, like 'lengthy', 'ordinary' or 'strange', that comes right before the thing it describes. Turning it up made the model's writing drift into random topics, so it does not seem to change the style much.

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

    lengthy work experience in space. As part of three space missions, he has spent

  2. #2 peak

    insulting when we imagine countries outside the West have something unique in their history where they crave

  3. #3 peak

    refine, and establish clinical uses for liquid biopsy tests.⏎Different liquid biopsy<|endoftext|>Biology

  4. #4 peak

    undeniable that teaching mathematics in the bilingual classroom, when the language of learning and teaching (

  5. #5 peak

    ordinary panel. This manufacturing method significantly reduces the weight of the concrete panel, without any

  6. #6 peak

    ordinary)⏎→ The two countries have resumed normal diplomatic relations.⏎→ Traffic was normal for

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 idea of a system that can be used in the real world. It is not just a computer, but it is more than a computer. It is a system, not just a program, but it is a program. It is a

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

    patient’s ‘kidney history’ and leads into a description of the natural history and

  2. #2 peak

    Server, loading data onto the LASR Analytic Server, and manipulating data on the

  3. #3 peak

    Math and computer science teacher Myra Deister of Sunny Hills High in Fullerton didn

  4. #4 peak

    server. Examples stock feeds, system status, weather conditions or other monitoring applications. This

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. L0:2672 excites
  2. L0:2844 excites
  3. L0:2582 suppresses
  4. L0:1947 suppresses
  5. L0:2972 excites
  6. L0:2107 suppresses
  7. L0:1780 suppresses
  8. L0:552 excites
  9. L0:2877 suppresses
  10. L0:2415 suppresses
  11. L0:1838 suppresses
  12. L0:2425 excites
  13. L0:2242 excites
  14. L0:2857 suppresses
  15. L0:2440 suppresses
  16. L0:2293 excites
  17. L0:1905 suppresses
  18. L0:2785 suppresses
  19. L0:2918 suppresses
  20. L0:1224 suppresses
  21. L0:2639 excites unclear: weak hits on 'holiday' and 'playing', negative on 'Ada-fruit'
  22. L0:1793 weakly excites
  23. L0:1301 weakly suppresses
  24. L0:861 weakly suppresses

Feeds later layers

  1. L2:55 suppresses
  2. L2:1489 suppresses
  3. L2:128 suppresses
  4. L2:321 suppresses
  5. L2:646 suppresses
  6. L27:2172 excites
  7. L27:456 weakly excites
  8. L2:46 weakly suppresses
  9. L27:2863 weakly excites
  10. L16:2650 weakly suppresses
  11. L27:862 weakly excites
  12. L27:46 weakly excites
  13. L27:1458 weakly excites
  14. L18:191 weakly suppresses
  15. L27:2225 weakly excites
  16. L27:2573 weakly excites
  17. L2:816 weakly excites
  18. L27:28 weakly suppresses
  19. L2:2981 weakly excites
  20. L4:160 weakly suppresses
  21. L27:823 weakly excites
  22. L27:1175 weakly excites
  23. L15:419 weakly excites
  24. L3:763 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 -3.12 and -2.931 values between -2.75 and -2.561 values between -2.38 and -2.191 values between -1.64 and -1.452 values between -1.45 and -1.271 values between -1.27 and -1.084 values between -1.08 and -0.903 values between -0.90 and -0.712 values between -0.71 and -0.535 values between -0.53 and -0.3465 values between -0.34 and -0.163269 values between -0.16 and 0.03529 values between 0.03 and 0.2113 values between 0.21 and 0.401 values between 0.40 and 0.582 values between 0.95 and 1.141 values between 1.51 and 1.691 values between 2.06 and 2.251 values between 2.25 and 2.431 values between 2.62 and 2.801 values between 3.17 and 3.361 values between 3.91 and 4.102 values between 4.10 and 4.28offnegativepositiveoften

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. modifies
  2. [].
  3. кри
  4. 臾
  5. edException
  6. mutating
  7. 季后
  8. pronounce
  9. unarmed
  10. evolves

Suppresses

  1. work
  2. ail
  3. id
  4. term
  5. apple
  6. Company
  7. atti
  8. water