unclear: word after 'just', 'full' or 'whole'
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. 2 of Qwen3-0.6B. Its strongest snippets are mixed: they peak on different tokens. 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 is hard to pin down. It often lights up on the word that finishes a set phrase like 'just war', 'full swing' or 'full houses', and it goes quiet or negative on 'only to' as in 'only to discover'. Turning it up did not produce any clear change in what the model wrote.
Written by Claude from this neuron's activations and steering outputs. Confidence: low. 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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<|endoftext|>“There shall never be any bond slaverie, villinage or Captivitie amongst us unles it be lawfull Captives taken in just warres, and such strangers as willingly selle themselves or are sold to us.
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weapons—to commit such crimes. (CCC 2314; cf. Gaudium et Spes 80)⏎Under the Catholic understanding of just war, not only must the cause of
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, Our Explosive Sun and The Northern Lights: A Guide. One of the foremost scholars on the subject, Mr. Brekke has spoken multiple times to full houses at the Smithsonian and elsewhere. In this interactive session,
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read in a sustained way, then a chunk of his schooling should be focused on helping him squeeze meaning and richness out of the experience. You may remember the whole-language ideas about exposing kids to print through fiction, poetry, newspapers, and drama?
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news HTTP/1.1 Host: www.example.ai Accept-Language: fr⏎HTTP/1.1 <|endoftext|>The move to electronic health records is in full swing for most of the medical community, but holdouts point to security issues as an
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bu Anne mau makan di KFC.⏎Harimau tidak mau berlari ke rumah.⏎Perempuan muntah pohon.⏎Students then choose words from the above lists to make a sentence
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 going to be thinking about lately, and I have been thinking about lately. So I am going to be thinking about lately, and I have been thinking about lately. So I am going to be thinking about lately. So I am
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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the festivities. But when a blizzard shuts down the highway, Phil finds himself trapped in Punxsutawney. He wakes up the next day, only to discover that it's not the next day at all. It's Groundhog Day
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the most efficient processing of the data; there is an old joke we like to tell our students about a meteorologist who wrote a weather prediction program, only to find out that it took one week to predict tomorrow's weather! (He later
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influential funder who told me told me that parents were a sinkhole, only to tell me a bit later that parents were essential. Whiplash!⏎Funny thing
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912, stretched a canvas for it in 1913, and returned to the composition late in 1915, only to start again on a new canvas in early 1916. Black is
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
- L1:1960 weakly excites
- L1:545 weakly suppresses
- L0:1224 weakly suppresses
- L1:861 weakly suppresses
- L0:178 weakly suppresses
- L1:1199 weakly excites
- L0:1301 weakly suppresses
- L1:2160 weakly suppresses
- L1:1244 weakly suppresses "of" after "board" or "off"
- L1:1387 weakly suppresses
- L1:189 weakly suppresses
- L1:2739 weakly suppresses
- L1:2355 weakly excites
- L1:18 weakly suppresses
- L1:2914 weakly suppresses
- L0:2200 weakly excites
- L1:922 weakly excites
- L1:1485 weakly excites
- L0:1075 weakly excites
- L0:385 weakly excites
- L0:1580 weakly excites
- L0:1781 weakly excites
- L1:1102 weakly suppresses
- L1:340 weakly suppresses
Feeds later layers
- L4:607 excites
- L10:874 weakly excites
- L21:1268 weakly suppresses
- L8:448 weakly excites
- L13:2030 weakly excites
- L12:223 weakly suppresses
- L10:2236 weakly excites
- L10:751 weakly excites
- L6:477 weakly suppresses
- L23:2263 weakly suppresses
- L5:2921 weakly suppresses
- L3:1259 weakly excites
- L20:173 weakly suppresses
- L4:1097 weakly excites
- L7:2291 weakly suppresses
- L23:1883 weakly excites
- L7:2000 weakly suppresses
- L11:573 weakly suppresses
- L21:1280 weakly excites
- L15:1296 weakly suppresses commas inside citation author names
- L24:1697 weakly suppresses
- L8:2091 weakly excites
- L21:3017 weakly suppresses
- L24:1591 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
- iface
- 人口
- dine
- chế
- Geschichte
- EEE
- society
- apartheid
- quốc
- .trace
Suppresses
- gars
- getPost
- 库里
- ильно
- leasing
- 系数
- 公证
- LObject
- CLE
- ],'