the digit '1' starting a year
A neuronOne of thousands of tiny dials inside each layer. It reads the text so far and produces a number, its activation, that nudges what the model writes next. in layerOne step in the stack the text passes through. Qwen3-0.6B has 28 layers, each with 3,072 neurons. 23 of Qwen3-0.6B. Its strongest snippets are very consistent: they mostly peak on the same token. It is featuredA neuron picked for the full treatment: steering outputs and a written explanation. About 3,000 of the 86,000 neurons are featured., so it has steering outputs.
What this neuron does
The neuron fires on the digit '1' when it begins a year from the 1600s to 1700s, such as 1762 or 1775, in historical text. Its negative side fires on the letter J in Java terms. Turning it up gave odd, unrelated text about Java and repeated nonsense words, so the steering does not show the year idea clearly.
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 activationThe number a neuron produces for a token. Large positive means it fired hard; negative means it pushed the other way. was highest. Amber marks positive activation; the outlined tokenA piece of text the model reads at once: often a word, sometimes part of a word or a punctuation mark. is the peak. Hover a token for its strength.
-
on a wall as decoration.⏎Georges Keating (Irish, 1762–1842), after Joshua Reynolds (English, 1723–179
-
Journal of Business Research, 67(8), 1595-1600. http://dx.doi.org/10.1016/j.jbusres.2013.10.
-
key audiences (graduate students, faculty, postdoctoral scholars, administrators, organizations) served.⏎John Locke ‘s work Some Thoughts Regarding Education was written in 1693 and still reflects conventional education priorities in the Western globe. Every youngster
-
Undergraduate and Master 's degrees. After receiving his degrees he became a faithful lawyer and was chosen to help draft the Declaration of Independence in the year of 1774 while also serving on the first Continental Congress. In the year
-
the restoration of grassland butterfly communities: a multi-site assessment. Biological Conservation, 155. 50-58. 10.1016/j.biocon.2012.05.0
-
farm in Massachusetts for the Continental Congress in Philadelphia. Doesn’t sound particularly interesting, I know but consider this.⏎John Adams left his home in mid-December 1775 to<|endoftext|>For whatever reason, a lot of people have a natural
SteeringAdding a push along a neuron while the model writes, to see how its output changes. Turning it up or down.
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.
Here is something I have been thinking about lately: A class of 24 people, with 30 J1s, 42 J2s, and 51 J3s. 11 J1s and 42 J2s are J1s
Most negative on
Where the activation went furthest below zero. Blue marks negative activation. Many neurons mean something different in each direction.
-
Java environment so a Java runtime environment like JRun 3.0 will know what to do with such tags as they're recognized. Tag libraries are usually packaged as Java archives
-
, fully updated and expanded⏎to cover Java SE 17 (JDK 17).⏎Whether you're an experienced pro or just starting out,
-
also⏎examined. The final chapter provides an⏎introduction to Swing, Java's popular GUI framework.. As an added bonus, an⏎introduction to JShell, Java's interactive programming tool, is included.⏎|Introducing JavaFX
-
⏎to Swing, Java's popular GUI framework. In addition, beans⏎are examined and servlets are introduced. As an added⏎bonus, an introduction to JShell, Java's interactive programming⏎tool, is included.⏎|Java: A
Connections
Show on mapWhich neuronsOne of thousands of tiny dials inside each layer. It reads the text so far and produces a number, its activation, that nudges what the model writes next. in earlier layersOne step in the stack the text passes through. Qwen3-0.6B has 28 layers, each with 3,072 neurons. feed this one, and which later ones it feeds. Amber links excite, blue links suppress; longer bars are stronger.
Fed by earlier layers
- L21:448 strongly excites
- L20:431 strongly suppresses
- L9:195 suppresses
- L17:343 suppresses
- L20:642 excites
- L3:88 suppresses
- L3:483 suppresses
- L21:546 excites
- L22:567 excites
- L4:368 excites
- L6:378 suppresses
- L0:1559 excites
- L2:735 suppresses colon after a metadata label
- L20:1954 weakly excites
- L20:936 weakly suppresses
- L22:2695 weakly suppresses
- L21:328 weakly excites
- L0:2573 weakly excites
- L8:166 weakly suppresses
- L16:496 weakly excites
- L13:420 weakly excites
- L22:2956 weakly excites
- L9:413 weakly excites
- L0:2794 weakly excites
Feeds later layers
- L24:1174 suppresses
- L27:855 suppresses
- L24:1850 suppresses
- L25:416 suppresses
- L26:713 suppresses
- L25:1461 suppresses
- L24:2563 suppresses
- L27:834 suppresses
- L25:1298 suppresses
- L24:1942 suppresses
- L26:1014 weakly suppresses
- L27:1689 weakly suppresses
- L24:867 weakly suppresses
- L25:1439 weakly suppresses
- L25:2374 weakly suppresses
- L26:1517 weakly suppresses
- L25:496 weakly suppresses
- L24:1234 weakly suppresses
- L25:2553 weakly suppresses
- L25:794 weakly excites
- L24:478 weakly suppresses word fragment inside a longer word
- L24:1487 weakly suppresses
- L25:133 weakly suppresses
- L27:909 weakly suppresses
Activation histogramA chart of how often a neuron takes each activation value.
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.
Logit lensReading which output tokens a neuron pushes toward or away from, straight off its output weights, before the later layers have their say.
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 layersOne step in the stack the text passes through. Qwen3-0.6B has 28 layers, each with 3,072 neurons. instead.
Promotes
- LError
- ngắn
- 触れ
- obra
- ictim
- -season
- 급
- lời
- SUP
- akah
Suppresses
- 朱
- J
- J
- jsp
- JP
- jar
- JP
- JA
- _j
- JC