Neuron Explorer

Qwen3-0.6B

'by' before a deadline

A neuron in layer 20 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 'by' when it sets a deadline, as in 'by 2050' or 'gone by summer'. Turning it down made the model repeat the word 'now' over and over, which fits the neuron pushing against that word.

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

    ⏎Cities are crucial to achieving the SDGs: more than half of the world’s population lives in cities, with this number predicted to increase to over two-thirds by 2050. Mapping out an implementation strategy for SDG 1

  2. #2 peak

    well as a similar study did find that cats decl<|endoftext|>With the human population expected to grow by billions over the new few decades, the United Nations is predicting that by 2050 our food production must double

  3. #3 peak

    measured in the metric tonne that are verifiable and tradeable. Bloomberg predicts that the price of carbon credits may rise by 3,000 percent by 2029 with better regulations.⏎The ever increasing demand for carbon credits

  4. #4 peak

    not be surpassed by the end of the century. That level, they say, is 2 metres: sea levels are unlikely to rise more than 2 metres by 2100.⏎Setting an upper limit on how fast seas will rise

  5. #5 peak

    future legislative action and planning.⏎Research included in the report suggests Hawaii will see nearly a one-meter rise in ocean levels by the end of this century. It predicts that more than 6,00

  6. #6 peak

    ers Final Test Phase⏎The COVID-19 pandemic seemingly has no expiration date. At its onset, we thought there was a decent chance it would be gone by summer. Fast forward and we see more cases in the summer than we ever did

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: Now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now

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

    half.⏎- Starting and ending at the fold, make the first stroke of the number 2.⏎- Cut along the line.⏎- Unfold the paper.⏎- Use the same method to make more hearts. Use construction paper, gift wrapping paper

  2. #2 peak

    . First the cardinal numbers. That's only one 3. This will remain. And then the numerators. 1 + 3 = 4. We then get the answer 345 Can we simplify? No, that's

  3. #3 peak

    up to 1.⏎- Now select the ball, and go to the Physics tab. Also click ‘Rigid Body’ and leave the type as ‘Active’. We want this to simulate animation. Now change the bounciness to 1

  4. #4 peak

    right, 256 units up). Now rotate it 90 degrees. We have now created the beginning and end of the 90 degree pipe.⏎Now, lets create the splines to

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. L19:1807 suppresses
  2. L19:1756 suppresses
  3. L17:1255 excites
  4. L19:2475 suppresses
  5. L19:1750 suppresses
  6. L19:2161 suppresses
  7. L14:2875 suppresses
  8. L19:1687 suppresses
  9. L19:1072 weakly excites
  10. L7:2302 weakly excites
  11. L19:894 weakly suppresses
  12. L0:2864 weakly suppresses
  13. L19:1001 weakly suppresses "be" after will or you'll
  14. L8:1331 weakly excites
  15. L10:1481 weakly excites
  16. L14:2533 weakly suppresses
  17. L7:827 weakly suppresses
  18. L19:564 weakly excites
  19. L19:1394 weakly excites
  20. L18:2713 weakly suppresses
  21. L17:427 weakly excites
  22. L19:553 weakly excites
  23. L19:2536 weakly suppresses
  24. L2:2219 weakly suppresses

Feeds later layers

  1. L27:1163 strongly suppresses
  2. L21:1414 strongly suppresses
  3. L26:886 strongly suppresses
  4. L21:1093 suppresses
  5. L23:790 suppresses
  6. L22:1428 suppresses
  7. L24:2184 suppresses
  8. L23:1516 suppresses
  9. L21:2845 suppresses
  10. L22:2751 suppresses
  11. L21:2334 suppresses
  12. L23:2340 weakly suppresses
  13. L24:1932 weakly suppresses
  14. L25:740 weakly suppresses
  15. L23:565 weakly excites 'is' right after a name or subject
  16. L23:1189 weakly suppresses
  17. L24:1770 weakly suppresses
  18. L21:2807 weakly suppresses
  19. L21:2207 weakly suppresses
  20. L21:2125 weakly suppresses
  21. L27:1387 weakly suppresses
  22. L21:1153 weakly excites
  23. L22:1493 weakly suppresses
  24. L23:1177 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 -5.43 and -5.091 values between -4.75 and -4.411 values between -3.73 and -3.392 values between -3.39 and -3.053 values between -2.71 and -2.382 values between -2.38 and -2.045 values between -2.04 and -1.705 values between -1.70 and -1.3614 values between -1.36 and -1.0248 values between -1.02 and -0.68341 values between -0.68 and -0.341496 values between -0.34 and 0.001575 values between 0.00 and 0.34312 values between 0.34 and 0.6850 values between 0.68 and 1.0212 values between 1.02 and 1.3615 values between 1.36 and 1.707 values between 1.70 and 2.042 values between 2.04 and 2.384 values between 2.38 and 2.722 values between 2.72 and 3.061 values between 3.06 and 3.401 values between 3.40 and 3.741 values between 4.08 and 4.413 values between 4.41 and 4.751 values between 5.09 and 5.431 values between 6.79 and 7.131 values between 7.13 and 7.471 values between 7.81 and 8.15offnegativepositiveoften

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. aña
  2. thereof
  3. akh
  4. else
  5. 透过
  6. 另外一个
  7. 的话
  8. eldo
  9. 反之
  10. 意义上的

Suppresses

  1. now
  2. now
  3. Now
  4. 现在
  5. Now
  6. -now
  7. →now
  8. (now
  9. ahora
  10. agora