zeros inside decimal numbers
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. 13 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 to the digit 0 when it sits inside a number, especially after a decimal point, as in "0.03 g" or "172.16.0.0". When it was turned up, the model drifted into number-heavy output, which fits the theme.
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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5.9 g in 125 ml pot of plain whole yogurt and 0.03 g in 25 g of cheddar cheese.⏎Cow’s milk allergy
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0⏎network => 172.16.0.0/15 IP: 10101100.00010000.00000000.
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. This is not the case with a feature that has a specific tolerance i.e.: a nominal and an upper and lower spec limit (1.0 +/- .005). It might be tempting to put a quality stamp on a feature if
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starting from 86. It is easy to check that the sequence doesn’t contain any more numbers below 200 and our estimate predicts 0.07 members after 200. As we continue checking larger numbers and see
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in the right hand by 2 and to say 'OK' when he has the answer.⏎- Ask him to multiply the coin in the left hand by 17 and to say 'OK' when he has the answer. If it took
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also sometimes called step size) to determine the next point. For example, if the gradient magnitude is 2.5 and the learning rate is 0.01, then the gradient descent algorithm will pick the next point 0.0
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: in the world, what is the meaning of? (e.g.,, for example, "home" as a meaning in "a" as a word) (e.g.,) (e.g.) "a" as a word,
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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some strange ideas about “blackbodies”. The only difference between a blackbody and a non-blackbody is that the emissivity of a blackbody = 1, and the emissivity of a non-blackbody is less than 1.
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in reverse order, we need to create a sequence for the for loop using range with:⏎- start as length of the array - 1⏎- stop as -<|endoftext|>12 April 1953: An air battle between Soviet Air
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on geographic location, caste, class and gender.⏎This is underlined by the disparity between India’s best and worst performing states. For every 1,000 children born in Madhya Pradesh, 82 are lost to preventable
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"independent expenditure" loophole permitted under a 1976 U.S. Supreme Court ruling. Although federal law restricted political action committees' expenditures to $10,000 per candidate, an organization could spend unlimited amounts of money
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
- L11:1852 excites the digit 1 in numbers
- L11:871 excites zero and infinity as values
- L10:1284 excites
- L10:726 suppresses
- L10:2192 suppresses
- L9:1373 suppresses
- L11:241 suppresses
- L10:848 suppresses
- L7:382 suppresses
- L7:873 suppresses
- L7:662 excites
- L9:977 weakly suppresses the last digit of a decade year
- L4:961 weakly excites
- L12:1413 weakly suppresses
- L6:457 weakly excites
- L12:641 weakly excites
- L5:547 weakly suppresses
- L8:318 weakly suppresses
- L12:1296 weakly suppresses
- L9:620 weakly excites
- L11:1361 weakly suppresses
- L12:834 weakly excites
- L11:3058 weakly suppresses
- L8:1130 weakly excites
Feeds later layers
- L16:1695 weakly excites
- L14:357 weakly suppresses
- L19:2527 weakly excites
- L16:1534 weakly suppresses
- L14:528 weakly excites
- L14:934 weakly excites
- L15:974 weakly suppresses
- L16:2492 weakly excites
- L25:332 weakly excites
- L18:749 weakly suppresses
- L20:560 weakly excites
- L17:2455 weakly suppresses
- L25:1911 weakly excites
- L18:1719 weakly suppresses
- L15:2651 weakly excites
- L14:752 weakly suppresses
- L17:1165 weakly excites
- L15:1198 weakly excites
- L15:457 weakly suppresses
- L16:1146 weakly excites
- L23:1697 weakly excites
- L14:518 weakly excites
- L21:2039 weakly suppresses
- L17:660 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
- ort
- ymce
- ivil
- oste
- RICT
- iks
- avis
- vertime
- pong
- Advisor
Suppresses
- rooting
- жить
- Believe
- 哉
- clam
- bable
- 大宗
- 囫
- 合一
- 通行