Neuron Explorer

Qwen3-0.6B

'of' or 'per' after a quantity noun

A neuron in layer 15 of Qwen3-0.6B. Its strongest snippets are fairly consistent: several of them peak on the same token. It is featured, so it has steering outputs.

What this neuron does

This neuron responds to the small word that links an amount to what it counts, such as 'number of', 'list of', 'goals per match' or 'index starts from'. It is mostly quiet elsewhere, and turning it up or down did not give a clear theme in the sample text.

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

    we will be joining two tables. We read the first table and apply a hash function to each record in the table. These records are then stored in a number of buckets. We then take the records from the second table and apply the same hash

  2. #2 peak

    define adjacent nodes of a given node as alternate nodes of incident edges. Adjacent nodes are sometimes referred to as neighbors. The degree of a node is the number of its incident edges or

  3. #3 peak

    has three parts, Part A, short answer, has 1 problem (12 points). Part B has 5 shorter problems (7 points each, so 35 points). Part C has 3 traditional problems (15 points each

  4. #4 peak

    is time. Each box shows the level of the analogue signal at that instant. Each box is called a 'sample'.⏎A PCM recording is simply a long list of the values of those boxes. Two important determinants of the accuracy of that recording

  5. #5 peak

    tournament for the first time as Dutch East Indies, now Indonesia, qualified while other Asian teams withdrew.⏎The tournament saw an average of 4.66 goals per match much to the delight of the spectators across the 10 host cities.⏎

  6. #6 peak

    scale.⏎Items you will need⏎- Calculator with statistical functions⏎Define the bel and decibel. The bel is defined mathematically as LB = log10 (P1/P0) where P1 and P0 are two quanties

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: I have a time where I was in a different kind of situation. I want to describe this situation, and I want to describe the time, and I have a time of similar to this. What is the time is a time of which

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

    The development of hydraulic fracturing (‘fracking’) has raised US gas production dramatically, and helped double the estimated global gas resource to around 800 trillion cubic meters (tcm), or 250 years’ consumption at current

  2. #2 peak

    εσT4⏎where ε=emissivity, a material property; σ = 5.67×10-8 ; T = temperature in Kelvin (absolute

  3. #3 peak

    |⏎|Year||Equals: Net earnings by place of residence ($000)||% Total||Plus: Dividends, interest, and rent ($000)||% Total||Plus: Transfer payments ($000)||% Total

  4. #4 peak

    .…⏎Mic level, line level, and speaker level.…Mic level is the weakest of the three.…This is usually somewhere around…a few thousandths of a volt.…Very low voltage.…It can't shock you

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. L11:2534 suppresses
  2. L13:1782 suppresses
  3. L14:2805 excites
  4. L10:1055 suppresses
  5. L12:1540 excites
  6. L8:2207 excites
  7. L12:1726 suppresses
  8. L6:1407 weakly suppresses
  9. L14:1989 weakly excites
  10. L14:1913 weakly excites
  11. L14:1083 weakly excites
  12. L10:2333 weakly suppresses
  13. L12:1141 weakly suppresses
  14. L8:2413 weakly suppresses
  15. L14:2008 weakly excites
  16. L10:659 weakly excites
  17. L12:2364 weakly excites
  18. L14:2379 weakly suppresses
  19. L12:244 weakly excites
  20. L12:1676 weakly suppresses
  21. L14:2401 weakly excites
  22. L11:2380 weakly excites
  23. L9:2642 weakly excites
  24. L6:3064 weakly suppresses

Feeds later layers

  1. L17:2137 suppresses
  2. L17:2057 suppresses
  3. L19:2383 suppresses
  4. L16:1259 suppresses
  5. L17:2487 suppresses
  6. L19:862 suppresses
  7. L17:1168 suppresses
  8. L16:1121 suppresses
  9. L16:813 suppresses
  10. L19:2176 suppresses
  11. L19:1249 weakly suppresses
  12. L16:1089 weakly suppresses
  13. L17:1151 weakly suppresses
  14. L17:2628 weakly suppresses
  15. L16:1820 weakly suppresses
  16. L16:1514 weakly suppresses
  17. L16:963 weakly suppresses
  18. L20:2148 weakly suppresses
  19. L18:2551 weakly suppresses
  20. L16:403 weakly excites
  21. L16:2108 weakly excites
  22. L19:1428 weakly suppresses
  23. L17:1336 weakly excites
  24. L17:168 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 -4.81 and -4.671 values between -2.45 and -2.321 values between -2.18 and -2.041 values between -1.62 and -1.481 values between -1.48 and -1.341 values between -0.93 and -0.792 values between -0.79 and -0.6510 values between -0.65 and -0.5125 values between -0.51 and -0.37143 values between -0.37 and -0.23569 values between -0.23 and -0.101527 values between -0.10 and 0.041027 values between 0.04 and 0.18443 values between 0.18 and 0.32130 values between 0.32 and 0.4621 values between 0.46 and 0.605 values between 0.60 and 0.74offnegativepositiveoften

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. .nextLine
  2. bote
  3. 突
  4. Majority
  5. .random
  6. groupName
  7. groupName
  8. Motorola
  9. ún
  10. ellungen

Suppresses

  1. 他们
  2. 楔
  3. icas
  4. 这是一种
  5. 他们在
  6. 说着
  7. etri
  8. 影音
  9. agram
  10. oram