unclear: document boundary 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. 7 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 is weak and hard to pin down. Its strongest responses fall on the invisible end-of-document marker where one web page joins another, plus a few random words. Turning it up gave unrelated text, so there is no clear story here.
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|>Copyright © University of Cambridge. All rights reserved.⏎'Aim High' printed from
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:<|endoftext|>The hamstring muscles – the biceps femoris, semitendinosus,
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0 International<|endoftext|>POWER IN AC CIRCUITS⏎Power of a circuit is defined as the
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compost in a<|endoftext|>In the Middle East and North Africa (MENA), women have made progress toward
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Louisiana back to France (1800). U.S.A. purchases Louisiana (1
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0 mph)<|endoftext|>Today I wondered which NT books relied more heavily on the Septuagint vs
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: the number of, and the number of, and the number of, and the number of, and the number of, and the number of, and the number of, and the number of, and the number of, and the number of
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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send, (and hopefully, receive), a valentine.⏎In the 1840
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xA4;⏎P0 = 0xB0;⏎P0 = 0x99;⏎P0
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easy as with other programming languages.⏎To create a list of integers, create a new variable and:⏎That’s simple right? And that wasn’t possible with Record Lists.⏎Creating a list of a single compound type like Entity or Structure, was
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table with a specific name or index exists.⏎The following example retrieves a single table by name from the DataTableCollection.⏎Private Sub GetTableByName() ' Presuming a DataGrid is displaying more than one
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
- L3:385 suppresses
- L5:1007 suppresses
- L2:635 suppresses
- L5:164 suppresses
- L3:119 suppresses
- L3:377 suppresses
- L3:685 suppresses
- L3:228 excites
- L5:693 weakly suppresses
- L4:1918 weakly suppresses
- L4:1 weakly excites
- L4:19 weakly suppresses
- L3:437 weakly suppresses
- L5:2270 weakly excites
- L1:280 weakly excites
- L3:2076 weakly excites
- L2:1982 weakly suppresses
- L5:398 weakly excites
- L5:176 weakly suppresses
- L2:779 weakly excites
- L5:169 weakly excites
- L1:148 weakly suppresses
- L2:448 weakly excites
- L2:2774 weakly suppresses
Feeds later layers
- L9:110 excites
- L17:848 excites
- L10:1020 weakly excites
- L12:1242 weakly excites
- L15:544 weakly excites
- L18:467 weakly excites
- L25:554 weakly excites
- L8:1803 weakly suppresses
- L21:2839 weakly suppresses
- L21:1483 weakly suppresses
- L21:2420 weakly excites
- L23:106 weakly excites
- L16:444 weakly excites
- L21:2318 weakly excites
- L11:2573 weakly suppresses
- L13:1449 weakly excites
- L22:2325 weakly excites
- L19:1497 weakly suppresses
- L22:3066 weakly suppresses
- L23:937 weakly excites
- L12:1689 weakly excites
- L14:2239 weakly suppresses
- L10:914 weakly excites
- L8:426 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
- ermo
- being
- 有两个
- 政府采购
- PR
- 亚军
- rado
- ndon
- 照
- гос
Suppresses
- queryInterface
- HING
- .HttpContext
- .createTextNode
- ��
- oup
- 此文
- klä
- OCK
- ensions