unclear: punctuation before an explanation
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. 17 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 small connecting marks, like a colon, bracket or comma, that come right before a detail, a value or a definition. It is hard to pin down more precisely, and turning it up mostly made the text drift into strings of numbers and maths.
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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are also used to underline the superlative condition of an adjective (very nice) or it is also possible to turn an adjective into an adverb suffix mind (rapidisamamente).⏎A form of advertising comparisonThe superlative is used
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as an example. It has the following data:⏎- Face value: $1,000⏎- Annual coupon rate: 5%⏎- Coupon Frequency: Annual⏎- Years to maturity: 10 years⏎- Yield to maturity
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Lesson 9⏎9-9.A recognize positions of sides and angles in triangles⏎For any triangle KLM, name:⏎a. the side that is opposite angle KLM.⏎b. the angle whose sides are KL and KM .⏎9-
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attractor describes the conditions that make the system behave chaotically.⏎The equations that govern the Lorenz attractor are⏎Lorenz attractor with r = 28, σ = 10, β<|endoftext|>Parshas Sh
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for actual Olive (with the exception of the porous grain structure, which gives its true identity away easily), because both Ash and Olive are placed in the same family: Oleaceae.⏎The dark-on-light stripes of Olive Ash are also vaguely reminiscent
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,329 m (10,922 ft) high, though this varies with summit eruptions. It is the highest mountain in Italy south of the Alps. Etna covers an area of 1
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 1,400,000,000,000,000,00,0,0,0,0, 0, 0, 0, 0,
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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voltage 48V at the data inputs) can be programmed.⏎Our model no. is 884 with a serial no. of 00139⏎Kindly Donated by Callum Wilson.⏎With
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77, the sequence of numbers such that their powers of 2 do not contain zeros: 1, 2, 3, 4, 5
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|Number Of Pages||408|⏎Write a review⏎Note: HTML is not translated!⏎Rating: Bad Good⏎Enter the code in the box below:<|endoftext|>Everyday researchers around the world look for new therapies in hopes to shape the
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would be "A". Number 2 would be "B". 3 is "C", and so on.⏎- Now, think of a country that starts with the letter you're thinking of<|endoftext|>
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
- L16:2204 excites
- L16:1704 excites
- L15:659 excites
- L15:1235 suppresses
- L13:484 excites
- L16:2375 excites
- L16:2837 excites
- L12:277 excites
- L14:3003 suppresses
- L16:1081 suppresses
- L15:2183 suppresses
- L15:1982 suppresses end-of-text token and answer-stating phrases
- L14:1104 excites
- L15:560 suppresses
- L14:1272 excites
- L16:2178 suppresses
- L15:577 suppresses
- L14:636 excites
- L15:2482 suppresses
- L16:1801 suppresses
- L15:1239 suppresses
- L16:2886 excites
- L16:1124 suppresses
- L16:2370 excites
Feeds later layers
- L18:1106 suppresses
- L20:805 suppresses
- L19:2204 suppresses
- L20:1001 suppresses
- L19:1169 suppresses
- L18:1699 suppresses
- L18:995 suppresses
- L19:1329 suppresses
- L18:2361 suppresses
- L20:769 suppresses
- L18:2768 suppresses
- L18:2042 suppresses
- L20:2298 suppresses
- L18:790 suppresses
- L19:248 excites
- L18:2843 suppresses
- L20:947 suppresses
- L20:796 suppresses
- L18:2513 suppresses
- L20:1406 suppresses
- L20:2648 suppresses
- L19:2382 suppresses
- L24:411 suppresses
- L19:1934 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
- OLF
- AppComponent
- 鹘
- 报记者
- 侘
- EIF
- Married
- 的人来说
- hurry
- ED
Suppresses
- ront
- MISS
- ilter
- iron
- ilities
- ilen
- posure
- empty
- _TEXTURE
- nic