Neuron Explorer

Qwen3-0.6B

middle pieces of split longer words

A neuron in layer 2 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 the later pieces of long words that the model had to split into several chunks, such as 'reconfiguration', 'comparability' and 'grouping'. Turning it up did not produce a clear visible change in the writing.

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

    reconfiguration region are governed by field-aligned currents, which in their turn generate auroral bright

  2. #2 peak

    any reconfiguration event, there is no need to run this command interactively.⏎For more information

  3. #3 peak

    and texture of materials on the surface. Areas with a redder color appear red, and areas with a grayer color appear blue. Young crater rays, arrayed radially around fresh impact craters

  4. #4 peak

    certain services category definition. For such reasons, compilers should have in-depth knowledge of the methodologies and definitions used by the other data sources to ensure consistency and the comparability of the resulting merged data. Various dimensions of the different sources must be analysed by

  5. #5 peak

    data points, it mainly deals with unlabelled data.⏎Some applications of unsupervised machine learning techniques include: clustering (grouping coherent segments of customers, or grouping logistic pick-up points), anomaly detection (detect

  6. #6 peak

    asymmetric radial transects are fixed. We provide a new method that eliminates edge-effect bias for designs that use asymmetric radial transects with fixed orientation, but a workable solution for polygonal transects remains elusive.⏎Keywords: Boundary overlap, Inclusion

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: this is the time when I have been thinking about lately, I need to think about this is the time when I have been thinking about lately, I need to think about this is the time when I have been thinking about lately, I need

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

    al and differentiation.⏎"This research will be a big boost for stem cell scientists. The protein phosphorylation sites identified in this study are freely available to the broader research community, and researchers can use these data to study the cells in greater depth and determine

  2. #2 peak

    a threat from “possible large” earthquakes, studies on active fault zones in Thailand are still minimal.⏎With all of these in mind, the Geophysics research group at Faculty of Science, Mahidol University is founded with the objective of being

  3. #3 peak

    drivers of plant redistribution in mountainous areas under global change.⏎Personal links. ResearchGate | GoogleScholar | Research Group Website⏎Institute. Department of Agronomy, Food, Natural resources, Animals

  4. #4 peak

    0, 1, depending on the quadratic residue properties of modulo .⏎A famous result of Gauss states that when the modulus is a prime , the two sums and have

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. L1:719 weakly suppresses
  2. L1:585 weakly excites
  3. L1:2382 weakly excites
  4. L1:935 weakly excites
  5. L1:223 weakly excites unclear: 'selected' and 'shape' in mixed text
  6. L0:2833 weakly suppresses
  7. L0:234 weakly excites
  8. L1:1667 weakly excites
  9. L0:281 weakly excites
  10. L0:1848 weakly excites
  11. L0:1051 weakly suppresses
  12. L0:2808 weakly suppresses
  13. L1:936 weakly suppresses
  14. L1:525 weakly excites
  15. L1:951 weakly excites
  16. L0:378 weakly excites
  17. L0:1150 weakly suppresses
  18. L1:1977 weakly excites
  19. L1:756 weakly suppresses
  20. L1:152 weakly suppresses
  21. L0:530 weakly excites
  22. L1:2723 weakly suppresses mid-word syllables in capitalised names
  23. L1:1092 weakly suppresses
  24. L1:897 weakly suppresses

Feeds later layers

  1. L11:452 suppresses
  2. L20:2090 suppresses
  3. L10:276 suppresses
  4. L20:2200 weakly excites
  5. L21:2417 weakly excites
  6. L15:1548 weakly excites
  7. L24:767 weakly suppresses
  8. L21:2700 weakly excites
  9. L13:1115 weakly suppresses
  10. L12:2225 weakly excites
  11. L4:2919 weakly suppresses
  12. L8:77 weakly suppresses
  13. L9:65 weakly suppresses
  14. L6:1129 weakly suppresses
  15. L25:2047 weakly excites
  16. L13:681 weakly suppresses
  17. L24:2207 weakly suppresses
  18. L22:234 weakly suppresses
  19. L13:1218 weakly excites
  20. L14:3046 weakly excites
  21. L25:873 weakly suppresses
  22. L22:1940 weakly suppresses
  23. L17:2864 weakly excites
  24. L4:2978 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 -1.50 and -1.431 values between -0.93 and -0.861 values between -0.65 and -0.581 values between -0.58 and -0.514 values between -0.51 and -0.445 values between -0.44 and -0.3721 values between -0.37 and -0.3051 values between -0.30 and -0.23122 values between -0.23 and -0.16375 values between -0.16 and -0.08991 values between -0.08 and -0.011568 values between -0.01 and 0.06508 values between 0.06 and 0.13152 values between 0.13 and 0.2062 values between 0.20 and 0.2722 values between 0.27 and 0.3411 values between 0.34 and 0.414 values between 0.41 and 0.483 values between 0.48 and 0.553 values between 0.62 and 0.691 values between 0.77 and 0.841 values between 1.26 and 1.33offnegativepositiveoften

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. éri
  2. ью
  3. 旬
  4. mpr
  5. 十字
  6. mighty
  7. antino
  8. 宣言
  9. 冷静
  10. erty

Suppresses

  1. COPYING
  2. ::⏎
  3. inceton
  4. Contrib
  5. mum
  6. PLICATE
  7. Foo
  8. ypes
  9. Islamabad
  10. 벽