the word 'enough' and similar
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. 15 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 fires strongly on the word 'enough', and on similar words like 'do' or 'fine' when they mean 'is good enough'. Turning it up made the model write about things being enough or sufficient.
Written by Claude from this neuron's activations and steering outputs. Confidence: high. 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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our assurance that he is a real person.⏎Now read John 14:8"Philip said, 'Lord, show us the Father and that will be enough for us.' "⏎What did he mean by that? Was Philip so dense that
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the voice of leadership. His short message, “Why don’t you guys go to the right” was indeed minimal. The smallest example of top-down control was enough to release
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(“asanas” in Sanskrit), breathing exercises (“pranayamas”) and meditation (“Pratyahara”). But these three branches are already enough to bring to all practitioners many benefits.⏎In yoga, the breath and the breath
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are the materials needed to grow your own plant:⏎-African violet leaf*⏎-Bottle or jar⏎-Water-soluble plant fertilizer (an general fertilizer will do, we used 20-20-20)⏎*Other plants
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presumably steady rate. For birds, that's about 2% change (for mitochondrial DNA) in a million years -- not a whole lot! But it's enough to figure out their evolutionary history. What have we found out?⏎The DNA samples
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need access to technology and research materials to produce PSAs or social media campaigns⏎- This resource is only available on an unencrypted HTTP website. It should be fine for general use, but don’t use it to share any personally identifiable information⏎
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 have a 3D object (a cube, 1x1x1, with the other numbers (like the numbers) and the numbers that the numbers in the cubes, the numbers that (I have another number in the number
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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,0<|endoftext|>Guidelines for Restoring Cultural Landscapes⏎Identifying, retaining and preserving
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959,<|endoftext|>Everyone's heard about an apple a day. As one of the most popular fruits
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are minuscule — the atomic jitters that pass through our world when two black holes bash together in a distant galaxy are on the order of a billionth of a billionth the diameter of an atom. LIGO detects them by proxy,
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718<|endoftext|>Women who have kids later on in life may live longer, according to the findings
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
- L11:2364 suppresses
- L12:408 suppresses
- L11:2354 suppresses
- L12:911 suppresses
- L14:536 suppresses
- L6:1681 weakly excites
- L14:2605 weakly excites
- L8:1778 weakly suppresses
- L14:1809 weakly suppresses
- L14:803 weakly excites
- L12:946 weakly excites
- L8:3066 weakly suppresses
- L12:1982 weakly suppresses
- L12:1249 weakly suppresses
- L11:953 weakly suppresses
- L8:2136 weakly suppresses
- L9:2453 weakly excites
- L7:830 weakly suppresses
- L8:1427 weakly suppresses
- L12:2951 weakly suppresses
- L11:1819 weakly suppresses
- L14:258 weakly excites
- L13:2109 weakly excites
- L1:1770 weakly suppresses
Feeds later layers
- L23:2383 excites
- L18:1736 excites
- L17:2999 excites
- L20:2188 excites "enough" before "to"
- L17:780 excites
- L18:2883 excites
- L25:1264 excites
- L21:2208 excites
- L17:2479 excites
- L18:2488 excites
- L20:425 weakly excites
- L24:1858 weakly excites
- L16:2967 weakly excites
- L27:778 weakly excites
- L17:2863 weakly excites
- L18:390 weakly excites
- L17:2887 weakly excites
- L18:539 weakly excites
- L16:1867 weakly suppresses
- L16:1332 weakly excites
- L17:2446 weakly excites
- L20:2349 weakly excites
- L18:1369 weakly suppresses
- L19:1419 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
- 足够
- sufficient
- enough
- suffice
- 足够的
- sufficiently
- 够
- Enough
- 足以
- suff
Suppresses
- �s
- 你怎么
- ửa
- hotter
- #c
- )V
- 'autres
- .addComponent
- &p
- ngthen