end of document marker
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. 19 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
This neuron fires on the invisible marker that tells the model one document has ended and a new, unrelated one begins. Its negative side reacts to odd line breaks and quotes, and steering did not produce a clear pattern.
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.
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0240.<|endoftext|>By 2003, plenty of data suggested that pesticide drift was harming
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can be used to<|endoftext|>| Trisha Sertori |⏎JUGA, Indonesia (Reuters) –
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rounded to the nearest integer.<|endoftext|>Christine Duncan, MD⏎Sickle cell disease (SCD) affects as
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,0<|endoftext|>Guidelines for Restoring Cultural Landscapes⏎Identifying, retaining and preserving
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, 2012<|endoftext|>Some weather conditions are simply not fun to ride in. As cyclists, windy days
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. On<|endoftext|>Anatoly Lunacharsky On Literature and Art 1909
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: "I don't know where I'm going, but I know that I'm getting somewhere." That is, I'm not necessarily *knowing* where I'm going, but *knowing that I'm getting somewhere." That's a
Most negative on
Where the activation went furthest below zero. Blue marks negative activation. Many neurons mean something different in each direction.
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example sentence"⏎- S: (n) fire (the event of something burning (often destructive
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True, obj)". If it failed, you might return "(False, reason)". Often, you can
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a matrix⏎Find nth maximum⏎Matlab Basics II - Create a vector with a repeated entry
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for<|endoftext|>- Agriculture in NSW is important for the state’s economy, industries, communities and
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
- L18:1288 excites
- L16:211 suppresses
- L18:1128 excites
- L8:18 weakly suppresses
- L18:2790 weakly suppresses
- L17:36 weakly excites
- L9:1788 weakly suppresses
- L17:1237 weakly suppresses
- L2:3058 weakly excites
- L8:26 weakly excites
- L8:562 weakly suppresses
- L7:1022 weakly excites
- L15:1914 weakly suppresses
- L12:227 weakly excites
- L1:313 weakly excites
- L18:738 weakly excites
- L12:2870 weakly suppresses
- L17:1205 weakly excites
- L16:2775 weakly suppresses
- L6:1619 weakly suppresses
- L13:1411 weakly excites
- L15:1121 weakly suppresses
- L12:465 weakly suppresses
- L4:85 weakly suppresses
Feeds later layers
- L20:508 excites
- L20:431 excites
- L27:267 weakly suppresses
- L27:2157 weakly suppresses
- L20:2214 weakly excites
- L21:2895 weakly suppresses
- L22:2524 weakly suppresses
- L23:1481 weakly excites
- L23:1713 weakly suppresses
- L22:1633 weakly suppresses
- L22:355 weakly excites
- L20:2602 weakly suppresses
- L25:931 weakly suppresses
- L27:874 weakly suppresses
- L27:34 weakly excites
- L25:67 weakly suppresses
- L27:1571 weakly suppresses
- L25:113 weakly suppresses
- L20:1599 weakly suppresses
- L27:1238 weakly suppresses
- L27:669 weakly suppresses
- L24:1722 weakly suppresses
- L22:2310 weakly suppresses
- L27:662 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
- ------------
- 』
- ism
- uddy
- opr
- essor
- esso
- ov
- 同事
- Invoker
Suppresses
- .Optional
- RecognitionException
- bones
- ilha
- OfWork
- icates
- plied
- (Int
- *)&
- →catch