Neuron Explorer

Qwen3-0.6B

unclear: mid-sentence tokens in web text

A neuron in layer 9 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 grab bag of word pieces in the middle of ordinary web sentences, such as a date, a URL slash, or the word after 'In this paper'. There is no clear shared theme, and the strongest examples are hard to tell apart. Turning it up made the text wander without a clear change of topic.

Written by Claude from this neuron's activations and steering outputs. Confidence: low. 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

    London: Volume 2. Originally published by Cassell, Petter & Galpin, London, 1878.⏎This free content was digitised by double rekeying. All rights reserved.⏎The Plague of 13

  2. #2 peak

    radiation and chemotherapy to synergise the effectiveness of cancer treatment.⏎However, various problems in treating tumours with genetically engineered bacteria need to be addressed. In this review, we focus on the role of intratumoral bacteria

  3. #3 peak

    fractals, dynamical systems, patterns of nature created through simple rules, and aesthetic graphics drawn from the universe of mathematics and art.⏎Chaos and Fractals is divided into six parts: Geometry and Nature; Attractors; Cellular Autom

  4. #4 peak

    effect as lasting? Some believe not.⏎Taken from…. The Future of Education: Tablets vs. Textbooks http://mashable.com/2012/10/05/tablets-vs-textbooks/⏎They also say…. ‘

  5. #5 peak

    portal/species/overview?p_p_spp=257336⏎This map is based on the maps available from

  6. #6 peak

    meticulously documented. Known as "quantum tunneling," this strange phenomenon had previously been thought to occur only on very small scales.⏎In this paper I report instances of the spontaneous relocation of entire cats. Though cats are arguably quantal

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: What is the value of $ \frac{1}{1 + x} $ when x is 0? What is the value of $ \frac{1}{1 + x} $ when x is 0? What is the value

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

    table with a specific name or index exists.⏎The following example retrieves a single table by name from the DataTableCollection.⏎Private Sub GetTableByName() ' Presuming a DataGrid is displaying more than one table, ' get its DataSet

  2. #2 peak

    information about survivors could not be confirmed<|endoftext|>A simple example of HTML is:⏎<HTML> <HEAD> <TITLE>This is the Title</TITLE> </HEAD> <BODY

  3. #3 peak

    of fields.⏎The project is a continuation of Suhr’s previous work on nanotubes, which was published in Nature Nanotechnology. In this previous work, Suhr tested the strength and fatigue behavior of carbon nanotubes. He collaborated with

  4. #4 peak

    >>> len(s) 13 >>> >>> len("") 0 >>>⏎>>> >>> s3 = '' # empty string using single quotes >>> s3 '' >>> >>> s4 = "" # empty string using double quotes >>> s4 '' >>> ```

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. L8:341 excites
  2. L8:639 excites
  3. L7:713 excites
  4. L7:2610 suppresses
  5. L7:1190 suppresses
  6. L3:480 suppresses
  7. L7:642 weakly suppresses
  8. L6:786 weakly excites
  9. L7:48 weakly suppresses
  10. L8:1306 weakly excites help-seeking 'I don't know' phrases
  11. L1:1690 weakly excites
  12. L3:107 weakly suppresses line breaks mid-sentence
  13. L8:2274 weakly excites
  14. L5:529 weakly suppresses
  15. L8:2011 weakly excites
  16. L4:494 weakly suppresses
  17. L8:2023 weakly excites
  18. L7:412 weakly excites
  19. L1:1042 weakly excites
  20. L6:1186 weakly excites
  21. L1:2443 weakly excites
  22. L3:553 weakly excites
  23. L2:2363 weakly excites
  24. L7:885 weakly excites

Feeds later layers

  1. L12:657 suppresses
  2. L10:280 suppresses
  3. L10:598 excites
  4. L13:205 excites
  5. L10:1471 suppresses
  6. L16:2524 excites
  7. L14:1236 suppresses
  8. L10:1272 weakly suppresses
  9. L10:1342 weakly suppresses
  10. L19:2805 weakly excites
  11. L20:2658 weakly excites
  12. L13:1606 weakly suppresses
  13. L13:2954 weakly excites
  14. L16:2063 weakly excites
  15. L16:978 weakly suppresses
  16. L13:1396 weakly suppresses
  17. L10:2000 weakly suppresses
  18. L12:1281 weakly suppresses
  19. L21:1521 weakly excites
  20. L24:1076 weakly excites
  21. L10:40 weakly suppresses
  22. L12:2921 weakly excites
  23. L10:1245 weakly suppresses
  24. L25:465 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 -2.11 and -2.041 values between -2.04 and -1.961 values between -1.88 and -1.801 values between -1.73 and -1.652 values between -1.57 and -1.492 values between -1.49 and -1.411 values between -1.41 and -1.344 values between -1.34 and -1.263 values between -1.26 and -1.183 values between -1.18 and -1.103 values between -1.10 and -1.033 values between -1.03 and -0.951 values between -0.95 and -0.878 values between -0.87 and -0.792 values between -0.79 and -0.7211 values between -0.72 and -0.6412 values between -0.64 and -0.5629 values between -0.56 and -0.4831 values between -0.48 and -0.4049 values between -0.40 and -0.3377 values between -0.33 and -0.25144 values between -0.25 and -0.17306 values between -0.17 and -0.09729 values between -0.09 and -0.021325 values between -0.02 and 0.06632 values between 0.06 and 0.14320 values between 0.14 and 0.22137 values between 0.22 and 0.3047 values between 0.30 and 0.3712 values between 0.37 and 0.453 values between 0.45 and 0.532 values between 0.53 and 0.612 values between 0.61 and 0.683 values between 0.68 and 0.761 values between 0.92 and 0.99offnegativepositiveoften

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. angep
  2. '^$',
  3. ối
  4. leet
  5. ện
  6. jee
  7. ale
  8. CIÓN
  9. وية
  10. Rise

Suppresses

  1. 感受到
  2. 畹
  3. rica
  4. клад
  5. uffer
  6. chia
  7. ….
  8. holds
  9. 这条路
  10. 浆