full stops ending informative sentences
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. 17 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 lights up on the full stop at the end of a factual sentence in explanatory writing, such as an article or guide. Turning it up made the model's output fall into loops of stock phrases like "I have I have" or "The Following", so the steering result tells us little about the neuron's meaning.
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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sense, to conflate image production with graphic design. For example, contributors to WikiPedia often edit and re-upload images used to illustrate articles on that site. But in another way, it may not. Cropping or resizing a photograph is
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cryptocurrencies?⏎For starters, cryptocurrencies are essentially digital assets that exist only in the digital realm. They do not exist in a physical form, i.e. paper or coins,
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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: and that I have been writing for a year. on a year of work for which I have been in charge of writing for... in the work and which I have been doing at work for... and that I have done on work at work
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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1863). International Journal on Stereo & Immersive Media, 2(2), 54-69. Retrieved from https://revistas.ulusofona.pt/index.php/stereo/article/view/6807<|endoftext|>
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wrote my master's thesis on an extension to the hash join. You can view a copy of this thesis on my old school's Web site (http://www.cs.ndsu.nodak.edu/~peasland/paper.doc). If you
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
- L16:432 strongly suppresses period after month or initial
- L11:132 excites period ending an opening topic sentence
- L15:403 suppresses period inside an abbreviation
- L14:422 excites periods after initials and abbreviations
- L4:7 excites
- L15:313 excites decimal point inside numbers
- L13:53 excites period ending a claim before elaboration
- L5:168 suppresses
- L5:155 suppresses
- L8:541 excites
- L8:143 suppresses
- L9:16 suppresses unclear: periods inside numbers and names
- L14:155 excites sentence-ending periods in prose
- L16:240 suppresses comma after 'sometimes' or 'the truth'
- L6:305 suppresses
- L9:2447 excites
- L16:1545 weakly excites
- L16:359 weakly excites
- L5:336 weakly suppresses
- L9:613 weakly suppresses
- L0:1670 weakly excites
- L2:9 weakly suppresses sentence-final periods vs dots in code
- L15:804 weakly suppresses
- L0:1703 weakly suppresses
Feeds later layers
- L21:1086 excites end of sentence before a claim or statistic
- L18:791 excites
- L19:384 excites
- L19:344 excites periods ending explanations
- L24:353 excites decimal point inside a number
- L20:962 excites decimal points and dots in numbers
- L22:67 excites
- L25:198 excites
- L27:133 excites
- L20:505 excites
- L21:524 excites
- L23:179 excites
- L23:509 excites
- L25:217 excites
- L19:68 excites full stops closing a claim
- L27:640 excites 'of' inside fixed titles and names
- L19:1072 excites
- L19:2097 excites sentence-end punctuation in speech
- L20:473 excites
- L23:177 excites
- L20:940 excites
- L19:330 excites
- L20:296 excites
- L18:126 suppresses comma joining a full clause
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
- However
- о
- Currently
- Consider
- ⏎⏎
- бег
- Present
- 地上
- Simple
- computations
Suppresses
- .Template
- -disable
- 化的
- 全文
- portion
- .]
- zelf
- igkeit
- .Bar
- .Dep