Neuron Explorer

Qwen3-0.6B

'to' after verbs like prove or time

A neuron in layer 7 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 responds to the word 'to' when it comes after words like 'prove', 'suggested', 'time', or 'enough', where a verb or noun is about to be followed by another verb. Its strongest negative reactions are on the same kind of 'to', so it seems to flip on one side and off on the other. Turning it up did not produce a clear theme in the sample text.

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

    fact it may be the simplest building code ever to exist except for some early Roman Emperor’s rules on construction. The HUD Code basically says that if you can prove to the Department of Housing and Urban Development that you can

  2. #2 peak

    to our mobile site7ads6x98y<|endoftext|>Arabic Numeral 1 in Roman Numerals converter tool is available here. This tool can prove to be very handy in the conversion of Arabic to Roman numerals. The tool basically

  3. #3 peak

    standard filmmakers today still are struggling to measure up to,” he said.⏎Kaufman said the Disney film is based, in part

  4. #4 peak

    images. This is based on neural networks similar to the human brain, which are fed large amounts of data.⏎A person can however understand an image and commit this to memory from just one sighting. They can add context, from each unique patient and

  5. #5 peak

    opinion. It will center on research traditions that address the perception that public opinion exists and that this perception is gained by observing and communicating. If we accept as reality that perceptions of the reality of public opinion exist, then we may ask how

  6. #6 peak

    magazine. It was Wennberg who came up with the man to do just that. After discussion with another friend, Wes Prichard, Prichard suggested to Wennberg<|endoftext|>Home > Preview⏎The flashcards below were created by user⏎

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 am trying to find out about the possible applications of the results from the previous studies on the application of the results from the application of the results of the following steps. So far, the application of the results of the following steps. So

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

    will help you learn how to grill. Remember to always follow a few rules:⏎- Keep your grill grate clean to prevent sticking.⏎- Give yourself plenty of time to prepare and heat up the grill before cooking.⏎- Always keep an eye on what

  2. #2 peak

    -flat (Elgar wrote to his friend August Jaeger that he had “thrown over all key relationships as formerly practiced” but that he “was not silly enough to think (<|endoftext|>Meteorite hit atmosphere at 64,000 mph

  3. #3 peak

    the Nazis had gained power in some parts of Germany. The Nazis wanted all Jews to be killed. Otto Frank, Anne's father, did not hestitate to wait for the Nazis to come into full power. In 1933

  4. #4 peak

    foods such as ready meals, tinned goods and pasta sauces. Consider using natural foods, herbs and spices to flavour your food, and give your taste buds time to get used to the new flavours.⏎As we age our digestion can become less efficient

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. L6:1880 excites "by" introducing a deadline or means
  2. L6:2359 excites
  3. L6:1133 excites "to" before an infinitive verb
  4. L6:914 excites
  5. L6:1684 suppresses
  6. L6:2057 suppresses
  7. L6:2287 suppresses
  8. L6:1679 excites 'and' joining two actions in instructions
  9. L6:2747 suppresses
  10. L6:2064 suppresses
  11. L6:439 suppresses
  12. L4:910 excites
  13. L5:2641 excites unclear: scattered word fragments
  14. L4:2890 suppresses
  15. L6:1103 suppresses
  16. L4:113 suppresses
  17. L3:307 suppresses
  18. L6:2145 excites
  19. L6:2000 suppresses
  20. L6:1621 suppresses
  21. L6:2882 suppresses
  22. L6:2020 weakly excites
  23. L6:794 weakly excites
  24. L4:518 weakly suppresses

Feeds later layers

  1. L8:45 excites
  2. L8:941 excites
  3. L13:1046 excites 'in' or 'on' ending a phrase
  4. L8:234 excites
  5. L10:452 excites
  6. L14:2405 excites
  7. L10:217 excites
  8. L14:453 weakly excites
  9. L14:1584 weakly excites
  10. L16:1484 weakly suppresses
  11. L22:489 weakly excites
  12. L8:315 weakly excites
  13. L8:71 weakly excites
  14. L14:313 weakly excites
  15. L11:437 weakly excites
  16. L13:918 weakly excites
  17. L8:98 weakly excites
  18. L14:193 weakly excites
  19. L9:836 weakly excites
  20. L16:1100 weakly suppresses
  21. L25:942 weakly excites
  22. L15:41 weakly excites
  23. L10:132 weakly excites
  24. L8:187 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 -4.39 and -4.231 values between -4.06 and -3.902 values between -3.41 and -3.252 values between -2.93 and -2.773 values between -2.60 and -2.443 values between -2.44 and -2.282 values between -2.28 and -2.123 values between -2.12 and -1.951 values between -1.95 and -1.793 values between -1.79 and -1.6310 values between -1.63 and -1.476 values between -1.47 and -1.319 values between -1.31 and -1.1419 values between -1.14 and -0.9813 values between -0.98 and -0.8232 values between -0.82 and -0.6642 values between -0.66 and -0.50100 values between -0.50 and -0.33244 values between -0.33 and -0.171071 values between -0.17 and -0.011608 values between -0.01 and 0.15432 values between 0.15 and 0.32159 values between 0.32 and 0.4874 values between 0.48 and 0.6432 values between 0.64 and 0.8014 values between 0.80 and 0.9716 values between 0.97 and 1.133 values between 1.13 and 1.291 values between 1.45 and 1.611 values between 1.78 and 1.941 values between 1.94 and 2.10offnegativepositiveoften

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. ported
  2. 本报记者
  3. AuthProvider
  4. מין
  5. LineStyle
  6. BeNull
  7. 网小编
  8. pire
  9. reature
  10. riend

Suppresses

  1. sonras
  2. 批
  3. Right
  4. Right
  5. 起
  6. proper
  7. tri
  8. oby
  9. 三角
  10. 複