unclear: word before a count or quantity
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. 22 of Qwen3-0.6B. Its strongest snippets are fairly consistent: several of them 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 on the word just before a number or amount, such as 'eight' valence electrons or 'two' electrons. It is hard to pin down more precisely. When it was turned down, the model kept coming back to the word 'character'. When it was turned up, the text drifted into unrelated everyday topics.
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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right, contains common non-metals such as chlorine and helium. The noble gases, in the column on the right, almost never react, since they have eight valence electrons, which makes it very stable. The halogens, directly to
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in the elements. This is because the elements are listed in part by their electron configuration. The Alkali metals and Alkaline earth metals have one and two valence electrons (electrons in the outer shell) respectively. These elements lose
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a garden hose.⏎There are only two known coolants compatible with natural uranium. Heavy water – because it contains an isotope of hydrogen that has already absorbed an extra neutron and has no real affinity for absorbing another one – and one of several different
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store their data using sub-atomic particles known as qubits, instead of silicone chips used in traditional machines. Silicon chips store data in binary code – a series of ones and zeros – while qubits represent a range of values simultaneously, which allows
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common causes of syncope are relatively benign, "aborted<|endoftext|>Recall that the conjugate base of an acid is simply the compound formed when an acid loses a hydronium ion (H+).⏎Which the following represent conjugate acid
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- n. - A narrow fissure in rock⏎- n. - A personal or social separation (as between opposing factions)⏎Rifts is worth 8
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 order of a character in a book. How do you get a character to be in the order of the story? One letter, one character, one cirtch, which is a single letter, is a character. How do you
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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?⏎1) Hyphens Already Mean Something⏎Hyphens and dashes are actually slightly different, but in practice everybody just uses the same character, ASCII number 45, the hyphen-minus. So let’s just pretend they’re the
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far:⏎Originally Posted by MrFujin⏎Enter a 5-charcter string: hgout⏎String#1: tough ; ASCII code decremented by 32⏎String#2: HGUOT ; reverse order⏎mov char
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are some examples of how an escape could be used.⏎This next example is similar to the first example because the period is also a wild card, which represents any single character. So, if you need match a period and not every character you must
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with a backslash ( `` ). Some common escape sequences are: - `n` Newline - Prints a newline character - `t ` Tab - Prints a tab character - `\ ` Backslash - Prints a backslash ( \ ) - `'
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
- L17:2262 strongly suppresses
- L9:2350 excites
- L10:1170 suppresses
- L7:1772 suppresses
- L20:1577 suppresses
- L7:2276 excites
- L10:2107 excites
- L7:2605 excites
- L9:2240 excites
- L4:2762 excites
- L21:1642 excites
- L16:2848 suppresses
- L17:2580 suppresses
- L11:619 suppresses
- L6:1617 suppresses
- L6:13 excites
- L20:1269 suppresses
- L19:1531 weakly suppresses
- L7:2743 weakly excites
- L6:1327 weakly excites
- L12:1004 weakly excites
- L11:2389 weakly suppresses
- L21:1593 weakly excites
- L12:2224 weakly suppresses
Feeds later layers
- L24:1841 suppresses
- L27:1623 suppresses
- L26:2130 suppresses
- L27:1922 suppresses
- L25:893 suppresses
- L25:2060 suppresses
- L23:650 suppresses
- L26:2351 suppresses
- L27:779 suppresses
- L25:2470 suppresses
- L25:961 suppresses years and numbers before the digits
- L27:1442 weakly suppresses
- L23:1994 weakly suppresses
- L23:189 weakly suppresses
- L23:2081 weakly excites
- L24:2091 weakly suppresses
- L23:1923 weakly suppresses
- L24:2328 weakly suppresses
- L27:355 weakly suppresses
- L25:771 weakly excites
- L25:2738 weakly excites
- L23:1451 weakly suppresses
- L25:1299 weakly suppresses
- L23:395 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
- 口气
- ện
- IDS
- .Tool
- IDS
- logan
- jobId
- wage
- <Model
- nant
Suppresses
- character
- Character
- Character
- characters
- _character
- character
- 字符
- Characters
- _char
- char