Neuron Explorer

Qwen3-0.6B

the word 'value'

A neuron in layer 5 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 lights up on the word 'value' in phrases like 'add value to', 'the value of' or 'diminish the value'. When turned up, the text did not clearly pick up the theme, so the link is only from the examples.

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

    anything we need would be provided for by someone other than ourselves, we wouldn’t stand up and do something; we would not acquire skills we could use to add value to others and hence earn and income for ourselves.⏎– dependence on others

  2. #2 peak

    : “Our vision is fill the feeding gap and to make ECD Centres so attractive that parents want to send their children to school. We aim to add value to the existing government feeding schemes, allowing children to receive two meals a day.

  3. #3 peak

    processor to type a story instead of handwriting it.⏎This is where technology is used to carry out learning tasks in relatively more efficient ways. the technology here only adds value to the assignment and does not transform how it is done, examples of this include

  4. #4 peak

    ers and attach them to the deck⏎How To Build Cascading Deck Stairs⏎Building cascading deck stairs can be a fun and easy project that can add value to your home. There are a few things to consider when building cascading stairs

  5. #5 peak

    to a minimum so the restored object doesn’t look like it just came from the shop? Over-zealous intervention in the course of restoration can actually diminish the value of high quality antique furniture to as little as one-tenth of its market value

  6. #6 peak

    the existing product by introducing new features in it or bring out a new product to sustain in the market.⏎(iv) Productivity Productivity is calculated by comparing the value of outputs with the value of inputs. It is used as a measure of efficiency

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: (1) 1 when both 0 and 1, for both 0 and : 2 4 5, 3 4 4 5 as 0 4 2 4 3 4

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

    with are called one-to-one functions. DEFINITION A function is called a one-to-one function if it never takes on the same value twice; that is, If a horizontal line intersects the graph of

  2. #2 peak

    three possible values: +, 0 and -⏎1997 note: This is a

  3. #3 peak

    1 FUNCTIONS AND MODELS Not all functions possess inverses. Lets compare the functions and whose arrow diagrams are shown in Figure 1. Note that never takes on the same value twice (any two inputs in have different outputs), whereas does take

  4. #4 peak

    , the exponential sum version of the Gauss sum modulo is defined by⏎and the quadratic residue version is defined by⏎where is the Jacobi symbol, which has values

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. L3:2493 suppresses
  2. L4:572 weakly suppresses
  3. L4:2401 weakly excites
  4. L3:2146 weakly excites
  5. L1:1807 weakly suppresses
  6. L4:1103 weakly suppresses
  7. L4:25 weakly excites
  8. L2:3044 weakly excites
  9. L1:1179 weakly suppresses 'in' after 'result' (results in)
  10. L1:2212 weakly suppresses
  11. L4:923 weakly suppresses
  12. L3:1834 weakly excites
  13. L4:1667 weakly suppresses
  14. L4:2745 weakly suppresses
  15. L3:2415 weakly suppresses
  16. L4:2413 weakly suppresses
  17. L1:992 weakly excites
  18. L0:65 weakly suppresses
  19. L2:2818 weakly suppresses
  20. L3:354 weakly suppresses
  21. L4:158 weakly suppresses
  22. L3:231 weakly excites
  23. L1:1962 weakly suppresses
  24. L0:1390 weakly excites

Feeds later layers

  1. L16:3030 excites
  2. L11:448 weakly suppresses
  3. L25:243 weakly excites
  4. L6:746 weakly suppresses
  5. L11:1973 weakly excites
  6. L19:1156 weakly suppresses
  7. L11:946 weakly excites
  8. L12:1828 weakly suppresses
  9. L9:1068 weakly suppresses
  10. L16:1783 weakly suppresses
  11. L9:2028 weakly excites
  12. L15:3028 weakly excites
  13. L15:2507 weakly excites
  14. L12:2969 weakly excites
  15. L10:2163 weakly excites
  16. L18:252 weakly excites
  17. L6:1746 weakly excites
  18. L8:1694 weakly excites
  19. L17:2770 weakly excites
  20. L12:411 weakly excites
  21. L9:2488 weakly excites
  22. L9:56 weakly suppresses
  23. L22:2799 weakly excites
  24. L20:2367 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 -0.85 and -0.763 values between -0.57 and -0.4716 values between -0.47 and -0.3859 values between -0.38 and -0.29223 values between -0.29 and -0.19546 values between -0.19 and -0.101263 values between -0.10 and -0.001190 values between -0.00 and 0.09399 values between 0.09 and 0.18135 values between 0.18 and 0.2835 values between 0.28 and 0.3716 values between 0.37 and 0.474 values between 0.47 and 0.562 values between 0.56 and 0.654 values between 0.65 and 0.753 values between 0.75 and 0.843 values between 0.84 and 0.941 values between 0.94 and 1.031 values between 1.41 and 1.501 values between 1.50 and 1.591 values between 1.59 and 1.691 values between 2.16 and 2.251 values between 2.82 and 2.91offnegativepositiveoften

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. 時間を
  2. OMIT
  3. tras
  4. سر
  5. .SuspendLayout
  6. AN
  7. enough
  8. ANTI
  9. ству
  10. time

Suppresses

  1. íst
  2. inox
  3. ired
  4. zioni
  5. .yahoo
  6. iring
  7. ışı
  8. acha
  9. 买到
  10. inary