surname pieces split across tokens
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. 0 of Qwen3-0.6B. Its strongest snippets are fairly consistent: several of them 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 strongly on the middle of a person's surname when the name is split into several pieces, such as 'Lo[ew]en', 'Ru[hr]' and 'Gu[ite]au'. Its strongest negative values appear on name pieces too, so it looks like a name-fragment detector with the sign depending on the case. Turning it up or down did not give a clear story in the 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 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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eyes and a narrow short snout, similar to its relative T. rex, which lived 10-12 million years later. Dr. Mark Loewen, the study’s lead author, noted, “The width of the back
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Salt Lake City, Utah.⏎The study, funded in large part by the Bureau of Land Management and the National Science Foundation, was led by Dr. Mark Loewen, research associate at the Natural History Museum of Utah, and adjunct assistant professor
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words.⏎Professor Onur Güntürkün, one of the co-investigators from of Ruhr University’s Department of Biopsychology, says “that pigeons — separated
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can learn to distinguish real words from non-words by visually processing their letter combinations, surprising new research from the University of Otago in New Zealand and Ruhr University in Germany shows.⏎The researchers found
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the last months of WWII) against fnatical German resistance while US forces were encircling the Ruhr and preparing to cross the⏎Now a political war began. Churchill wanted Ike to
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easy for Charles J Guiteau to assassinate Garfield in 1881. Guiteau used a British Bull Dog revolver to shoot the President point blank in the back
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 am a young person, I have been thinking about lately, I am in my own way, I am in my own way, I am in my own way, I am in my own way, I am in my own way
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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make<|endoftext|>Amaziah of Judah⏎Amaziah of Judah (אמציה) was a king of Judah, the son and successor of Joash, and the father of Azariah (Uzziah
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in any rush to get there.⏎Meet future woman: shorter, plumper, more fertile [New Scientist]<|endoftext|>MERCIER, JEAN ° (Joannes Mercerus ; d. 1570), French Hebraist.
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NC, Julio Cárcamo. However the current status of the project is unclear.⏎Main policy lessons⏎Tulio Monterroso, AMUPROLOAGO: mailto:firstname.lastname@example.org; email@example.com ;⏎Cohen,
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with coagulopathy have a threefold to fourfold greater mortality, and are up to
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
None: this is the first layer.
Feeds later layers
- L10:948 excites
- L1:2314 weakly excites
- L16:631 weakly excites
- L2:2572 weakly excites
- L13:2015 weakly excites
- L9:1350 weakly excites
- L5:208 weakly suppresses
- L15:2104 weakly excites
- L7:2998 weakly suppresses
- L22:2266 weakly excites
- L19:2809 weakly excites comma after 'in this blog post'
- L26:1209 weakly excites
- L25:1634 weakly excites
- L16:1813 weakly suppresses
- L17:1562 weakly excites
- L19:2521 weakly excites
- L22:296 weakly suppresses
- L10:953 weakly suppresses
- L9:1512 weakly suppresses
- L8:527 weakly excites
- L15:2663 weakly excites
- L16:2044 weakly excites
- L22:1389 weakly suppresses
- L8:2802 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
- rient
- 吠
- nier
- erset
- 出汗
- .class
- ndef
- 成立以来
- кри
- ijd
Suppresses
- nhắc
- 桃
- thủ
- thẩm
- Step
- Pax
- :x
- +x
- atorial
- ally