end-of-document boundary token
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. 15 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 responds most strongly at the invisible marker that signals the end of one web document and the start of another, especially when the text before it stops mid-thought. Turning it up gave wandering, unrelated text, so the steering does not show a clear effect.
Written by Claude from this neuron's activations and steering outputs. Confidence: medium. Think it is wrong? Flag it (opens an email to the maker with the neuron already filled in).
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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an error has occurred. The ldexpf() and ldexpl() functions do not set errno.⏎See attributes(5) for descriptions of the following attributes:<|endoftext|>The
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phases to spot malfunctions and flaws. It often includes stress testing to estimate the product lifespan. The end goal is a product that meets the client’s quality specifications<|endoftext|>Diploid cells (WI-38, M
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enforcement agencies keep of known "disapproved" files that they want to search people's hard drives for. In these applications, keeping a database of the actual file<|endoftext|>Pre
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formulas and the questions. Students who work on their concepts would always excel in mathematical problems when compared to those who focus on memorizing solutions. Problems like limit solving<|endoftext|>How to dissolve polygons spatially in GRASS GIS⏎You can use the v
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, the range and severity of symptoms and physical characteristics may vary from case to case.⏎LEOPARD is an acronym for the characteristic abnormalities associated with the disorder: L stands for (L)entigines (multiple black or dark brown spots on
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mann machines (RBM), deep belief networks (DBN) and deep convolutional neural networks (CNN). In recent years, CNN-based methods have gained popularity<|endoftext|>typedef unsigned int size_t;⏎This type is used to hold size definitions. Since
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 can be 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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I walk to the beach.⏎- Action 2⏎We collect all sorts of seashells.⏎- Action 3⏎I spot something dark and shiny.⏎- Action 4⏎I grab it before a wave splashes it away.⏎-
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5 to 88 percent. MENA achieved the same increase in 2 decades: from 1990 to 2010.2⏎However, it is important to note that regional statistics tend to mask the fact that
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those declarations. Humans read code, and humans infer things from the code that will be necessary to work on it.⏎typedef void * model_3d_ref;⏎model_3d_ref load_model_from_disk(const char * absolute_file_path);⏎void
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modification, would have been destructive of the theory and would have made the<|endoftext|>It is not good for the health of a tree to build up soil around its base.⏎In the Chicago suburbs and near Galena, I have planted trees in soil that
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
- L10:984 excites
- L11:45 suppresses
- L7:52 suppresses
- L14:2884 excites
- L12:73 weakly excites
- L12:605 weakly excites
- L14:89 weakly suppresses
- L13:81 weakly suppresses
- L9:891 weakly suppresses
- L10:36 weakly excites
- L1:87 weakly excites
- L13:1727 weakly suppresses
- L13:1707 weakly excites
- L6:17 weakly excites
- L1:1735 weakly suppresses
- L14:374 weakly suppresses
- L9:25 weakly excites
- L8:2 weakly excites
- L13:370 weakly suppresses
- L10:38 weakly suppresses
- L13:271 weakly suppresses
- L2:1737 weakly suppresses
- L9:2447 weakly excites
- L13:121 weakly excites
Feeds later layers
- L16:225 excites
- L19:31 suppresses
- L17:114 suppresses
- L18:138 suppresses
- L18:325 weakly excites
- L17:363 weakly excites
- L19:232 weakly suppresses
- L16:860 weakly suppresses
- L25:156 weakly suppresses
- L17:717 weakly excites
- L25:670 weakly excites
- L16:196 weakly excites
- L16:1341 weakly excites
- L21:118 weakly excites
- L17:1054 weakly suppresses
- L16:402 weakly suppresses
- L19:1797 weakly excites
- L18:1715 weakly excites
- L16:2972 weakly excites
- L21:1059 weakly excites
- L19:1326 weakly excites
- L16:1829 weakly suppresses
- L20:36 weakly suppresses
- L22:1590 weakly excites
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
- allee
- NOW
- .removeEventListener
- �
- 都被
- występ
- 鹃
- были
- były
- 끔
Suppresses
- (**
- plies
- 蚁
- JK
- idel
- anst
- super
- ants
- ortion
- inaire