Neuron Explorer

Qwen3-0.6B

pair and compound words in technical text

A neuron in layer 22 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 responds to words in technical or list-like writing, especially the word 'pair' and words that join two parts together. Turning it up made the model repeat the word 'pair' over and over in some runs.

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

    ity Task Cards (CC) 9-13 Numbers and Operations in Base Ten Task Cards (NBT) 14-18 Operations and Algebraic Thinking Task Cards (OA) 19-23 Geometry Task Cards (

  2. #2 peak

    with warrantedly valid pointers. In contrast, C allows cyclic graphs of pointers, but pointers can be uninitialized, which is a nasty security hole. An eagerly evaluating functional language without hacks, would only allow for acyclic graphs of pointers.<|endoftext|>The

  3. #3 peak

    (distributed and heterogeneous) using explicit links known as trace links. The software development process often incorporates varied software artifacts like design documents, bug reports, test suites, functional specs, these artifacts can be located on different machines (servers). Software traceability

  4. #4 peak

    the German composer<|endoftext|>PANS and PANDAS are clinical diagnosis and based on whether presenting symptoms meet the diagnostic criteria, as set forth by National Institute of Mental Health (NIMH). Recognizing PANS/PANDAS

  5. #5 peak

    2⏎The word "focus" uses 5 letters: C F O S U.⏎No direct anagrams for focus found in this word list.⏎List shorter words within focus, sorted by length⏎All words formed from focus by changing one

  6. #6 peak

    elements of the array in reverse, we need to use a loop that will iterate from length - 1 to 0. We can use a for loop or a while loop to write this.⏎Let’s try this with a while loop:⏎given

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: a pair of tups – a pair of tups as a pair of pairs – a pair of tups as a pair of tups. A pair of tups as a pair of tups. A pair of tups as a

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

    predefined attributes, which are name/value pairs inside an element's start tag. The following example specifies two attributes (version

  2. #2 peak

    Understand how to create parallelized collections and external datasets⏎- Work with Resilient Distributed Dataset (RDD) operations⏎- Utilize shared variables and key-value pairs⏎Module 3 - Spark application programming⏎- Understand the purpose and usage

  3. #3 peak

    with respect to the Fourier transform. From the convolution product he goes on to define what in other contexts is called the field of fractions or a quotient field. These ordered pairs of functions Mikusinski calls operators.⏎The set of functions

  4. #4 peak

    development; women<|endoftext|>There are many cases in which you need a 2 dimensional lookup. That means, if you want to get a value from a specific row-column combination with neither rows or

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. L10:1457 weakly suppresses
  2. L19:2262 weakly excites
  3. L4:1802 weakly suppresses
  4. L20:2786 weakly excites
  5. L6:12 weakly excites
  6. L16:2518 weakly excites
  7. L18:2294 weakly excites
  8. L18:569 weakly excites
  9. L11:1750 weakly suppresses
  10. L18:859 weakly suppresses
  11. L6:13 weakly excites
  12. L17:1389 weakly excites
  13. L1:2949 weakly excites
  14. L15:2595 weakly excites
  15. L7:1686 weakly suppresses the word 'directly'
  16. L9:2010 weakly excites
  17. L3:189 weakly excites
  18. L20:951 weakly suppresses
  19. L10:2272 weakly excites
  20. L18:999 weakly suppresses
  21. L10:2600 weakly suppresses
  22. L16:1269 weakly suppresses
  23. L20:244 weakly excites
  24. L17:2751 weakly excites

Feeds later layers

  1. L27:2365 suppresses
  2. L24:2720 suppresses
  3. L26:367 suppresses
  4. L26:977 weakly suppresses
  5. L26:1617 weakly suppresses
  6. L23:573 weakly suppresses
  7. L27:2338 weakly excites capital P starting a name
  8. L27:1275 weakly suppresses
  9. L23:1960 weakly excites
  10. L25:407 weakly excites
  11. L26:1457 weakly suppresses
  12. L24:440 weakly excites
  13. L23:357 weakly suppresses
  14. L27:346 weakly suppresses
  15. L25:856 weakly suppresses
  16. L23:1347 weakly excites
  17. L27:654 weakly suppresses
  18. L24:2390 weakly suppresses 'poly' word-start fragment
  19. L27:1648 weakly suppresses
  20. L25:2629 weakly excites 'scripting' before 'language', language loops
  21. L25:298 weakly suppresses
  22. L23:1772 weakly excites
  23. L26:2395 weakly suppresses
  24. L26:2059 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 -2.55 and -2.221 values between -2.22 and -1.901 values between -1.58 and -1.264 values between -1.26 and -0.9440 values between -0.94 and -0.61238 values between -0.61 and -0.292172 values between -0.29 and 0.031277 values between 0.03 and 0.35141 values between 0.35 and 0.6728 values between 0.67 and 1.003 values between 1.00 and 1.321 values between 2.93 and 3.251 values between 10.01 and 10.33offnegativepositiveoften

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. imiento
  2. 发展阶段
  3. 发展中
  4. 手里
  5. nét
  6. heid
  7. icont
  8. ignon
  9. neh
  10. setback

Suppresses

  1. pairs
  2. pair
  3. pairs
  4. pair
  5. _pair
  6. Pair
  7. Pair
  8. _pairs
  9. Pairs
  10. .pair