'to' or verb after 'what' or 'whether'
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. 10 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 fires on the word that comes right before a verb in a decision or intention phrase, such as the 'to' in 'what to believe', 'whether to enroll', or 'choose to take'. Turning it up made the model repeat phrases, and turning it down made it drift into maths word problems.
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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deciding that Howard, the fittest of the three, should continue alone to find help, leaving Mick to care for Cleve who by then,
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. It means analyzing the choice of what to believe or do. You must be thinking, how is it different from the usual
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needed. While it may sound trivial, the decision on what to produce/grow has important ramifications. If you plan to grow/produce a new
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-- Umberto Eco⏎More information about the Python-list<|endoftext|>Jayne Davis: “I am not convinced in my area of research there are less females choosing to take their career in this direction.”⏎We spoke to Jay
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and this comes from close observation of the plants plus a large application of common sense. Just take one step at a time. Master the fundamentals before you succumb to the excitement of special techniques or the challenge of difficult subjects
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were to succeed each other, moving up the hierarchy of cities toward Kiev, filling vacancies left by the advancement or death of an elder brother. The youngest brother was to be succeeded as grand prince by his eldest nephew whose father had been a grand prince
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: a) a) the number of possible choices for a and a single choice from the top row (a) a x a x 100 (a) 100 (a) 100 is not possible,
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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outer ear infection.⏎You should tilt your head to the side so the affected ear looks up when applying the drops. To widen the ear canal:⏎- Gently pull the earlobe back and down.⏎- Before using the ear drops, give the
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oving tool. Grasp the tick as close to the skin as possible. If you don’t have tweezers, protect your fingers with a tissue.⏎- Pull the<|endoftext|>Listen to radar echoes from satellites and meteors, live on listener
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were transported to the shore. The press was desperate to find out what exactly happened out there.⏎Thousands of people gathered on the streets of New York, patiently waiting for the arrival of RMS Carpathia…⏎This doesn’t come as a surprise,
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was standing. I managed to move behind the trunk of a spruce tree. My trembling fingers clutched the cannister of bear spray fastened to my beltóa last resort in case of
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
- L9:556 suppresses
- L7:417 suppresses
- L6:885 excites
- L8:2526 suppresses
- L6:385 suppresses
- L9:701 suppresses
- L9:712 excites
- L5:2008 excites
- L6:291 suppresses
- L7:1968 suppresses
- L7:531 excites
- L9:2276 excites
- L7:208 suppresses
- L9:2063 suppresses
- L6:2385 excites
- L7:1360 excites
- L7:655 excites
- L7:521 suppresses
- L7:1875 weakly suppresses
- L7:823 weakly excites
- L7:2892 weakly excites
- L7:577 weakly excites
- L7:1278 weakly excites
- L8:2913 weakly suppresses
Feeds later layers
- L11:1469 excites
- L12:1604 excites
- L13:3038 excites
- L12:19 excites
- L13:1711 suppresses
- L14:2214 excites
- L11:2296 suppresses
- L17:2534 suppresses
- L11:1745 suppresses
- L12:2946 excites
- L13:3022 suppresses
- L12:1982 excites
- L15:2053 excites
- L11:2274 suppresses
- L11:1606 suppresses
- L15:2392 excites
- L14:1717 weakly suppresses
- L13:844 weakly excites 'of' after a percentage or share
- L14:462 weakly suppresses
- L12:1041 weakly suppresses
- L11:346 weakly excites
- L13:1311 weakly suppresses
- L22:192 weakly excites
- L13:2210 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
- ochen
- (pass
- \|
- OSH
- atitude
- listening
- pass
- history
- ầ
- .extent
Suppresses
- erator
- NewProp
- RequestId
- 一个是
- 新颖
- 适合自己
- RequestMethod
- бег
- üns
- axis