Neuron Explorer

Qwen3-0.6B

animals, plants and natural hazards

A neuron in layer 12 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 nouns for living things and natural hazards, such as tornadoes, moths, bees, trees, crayfish and sand. It goes quiet or slightly negative on words that end definitions and examples, like 'that', commas and full stops. Turning it up did not produce a clear nature theme in the sample outputs.

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

    present their research findings on hazard preparedness and mitigation. The research teams, which receive some of their funding from the National Science Foundation, study disasters as varied as tornadoes, hurricanes, oil spills, earthquakes and landslides. They showcased projects

  2. #2 peak

    and most are comparatively benign. Have some introduced species caused changes that most everyone would agree have been very harmful? Absolutely. By killing timber trees, gypsy moths and the emerald ash borer have caused, and continue to cause, enormous

  3. #3 peak

    if you have a history of allergies to insect bites. Some insect bites or stings can provoke very serious allergic reactions (most of the time these are insects like bees or wasps), and nausea may be

  4. #4 peak

    As it moves around us, its viewpoint is changing, and that motion is reflected in the asteroid. Try this: look at some distant object like a mountain or tree, hold your thumb at arm’s length, close one eye, and then move

  5. #5 peak

    ,000 other forest products. Trees are renewable, biodegradable and recyclable. Wood by-products become such products as vitamins, plastics, vanilla flavoring, photographic film, toothpaste and medicines.⏎By planting 20 million trees

  6. #6 peak

    that electric eels use their energy charge in a number of ways, and not just for stunning their prey. When tackling a larger prey, like a large crayfish, the eel will curl

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 want to be a teacher. I need to teach a class. I want to teach a class. I want to teach a class. I want to teach a class. These are all examples of what I can do. The teacher is

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

    .⏎- Onus Item — Definition,⏎- A payment that is deposited at the same financial institution in which it was drawn or written. For example, a cheque that is written by one bank customer and deposited by

  2. #2 peak

    .⏎Container. Containers are the elements that “hold” the source. For example, if a television episode is watched on Netflix, Netflix is the container.⏎Number and volume. Note that the abbreviations vol.

  3. #3 peak

    its effect through logical demonstration. (157)⏎a "system" of signs whose elements receive meaning through and within this system. Morse code is an example. (159)⏎superficial and mistaken definition of rhetoric, as a

  4. #4 peak

    WikiPedia often edit and re-upload images used to illustrate articles on that site. But in another way, it may not. Cropping or resizing a photograph is different from using the language of graphic design to convey a message.⏎I'm a

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. L8:867 suppresses
  2. L7:255 excites
  3. L10:2157 suppresses
  4. L7:231 excites
  5. L11:2809 excites
  6. L9:2548 suppresses
  7. L7:1108 excites
  8. L7:1402 excites
  9. L3:1579 excites
  10. L10:927 weakly excites
  11. L10:2645 weakly excites
  12. L7:2400 weakly excites
  13. L10:1094 weakly excites
  14. L9:833 weakly excites
  15. L2:1311 weakly suppresses
  16. L10:2303 weakly suppresses
  17. L7:1997 weakly excites
  18. L10:850 weakly suppresses
  19. L11:662 weakly suppresses
  20. L11:742 weakly excites
  21. L11:175 weakly excites
  22. L6:2819 weakly suppresses
  23. L10:2079 weakly excites
  24. L9:358 weakly suppresses

Feeds later layers

  1. L13:2550 suppresses
  2. L13:2330 suppresses
  3. L20:2988 suppresses
  4. L14:1604 suppresses
  5. L14:2472 weakly suppresses
  6. L14:1539 weakly suppresses
  7. L13:2697 weakly suppresses
  8. L15:1348 weakly suppresses
  9. L21:2718 weakly suppresses
  10. L13:1266 weakly excites
  11. L13:2610 weakly excites
  12. L13:567 weakly excites
  13. L14:2555 weakly suppresses
  14. L13:252 weakly excites
  15. L15:776 weakly suppresses
  16. L16:2939 weakly suppresses
  17. L18:2720 weakly excites
  18. L13:2396 weakly suppresses
  19. L14:1562 weakly suppresses
  20. L15:1760 weakly suppresses
  21. L13:2488 weakly suppresses
  22. L20:1052 weakly excites
  23. L15:2865 weakly excites
  24. L17:2754 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 -3.90 and -3.771 values between -1.60 and -1.472 values between -1.47 and -1.343 values between -1.34 and -1.225 values between -1.22 and -1.091 values between -1.09 and -0.965 values between -0.96 and -0.838 values between -0.83 and -0.706 values between -0.70 and -0.5823 values between -0.58 and -0.4569 values between -0.45 and -0.32180 values between -0.32 and -0.19607 values between -0.19 and -0.061854 values between -0.06 and 0.06891 values between 0.06 and 0.19200 values between 0.19 and 0.3236 values between 0.32 and 0.4511 values between 0.45 and 0.582 values between 0.58 and 0.702 values between 0.70 and 0.831 values between 1.09 and 1.22offnegativepositiveoften

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. ployment
  2. ellidos
  3. ITTLE
  4. 越好
  5. 区委
  6. 时报
  7. &e
  8. adena
  9. positories
  10. lest

Suppresses

  1. defer
  2. دور
  3. 스크
  4. اض
  5. example
  6. ding
  7. [hash
  8. 爬
  9. would
  10. gibi