unclear: mixed word fragments, no shared theme
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. 27 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 does not respond to one clear thing. Its strongest moments land on scattered pieces of words in very different texts, such as a comma in a quoted phrase, a part of a medicine name, or the name of a court case. When it was turned down, the model kept repeating the letter 'v', and when it was turned up, the writing simply changed topic.
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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you tell me about what you did here?” Dana's response: “Car, vroom.”⏎Repeat and perhaps add to her comment. For example:⏎“Car, vroom! Your blue car goes vroom when you drive it. I can
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before administering the vaccine and shake the multidose vial preparation each time before withdrawing a dose of vaccine.⏎Inspect FLUVIRIN® syringes and multidose vials visually for particulate matter and/or discoloration prior to administration [
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are given by⏎v=Vm sin ωt and i = I m sin( ω t + φ)⏎where ϕ is the phase angle between υ and i. The instantaneous
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LOCAL VARIABLE vh.The lOCAL VARIABLE v is assigned this value.Thus LOCAL VAR. v is detached from the original object it was pointing to and now LOCAL VAR. v and vh point to same object.Since vh is a new obj. instance
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). Poet Sekkizhar (992-1042) describes the town as "vanga malik kadar karaikal", the 'Karaikal of many vessels on the coast' in his Peria pur
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012⏎|License||Verified by||Verified on||Notes|⏎|GPLv2||Kelly Hopkins||6 December 2011|⏎|GPLv3orlater||Kelly Hopkins||29 January 2010
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: v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v v
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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species into the skin.⏎Common diagnoses treated with iontophoresis include plantar fasciitis, bursitis, lateral and medial epicondylitis (commonly referred to as tennis elbow and golfer's elbow
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clearly; or inability to control urination.⏎Your doctor will give you the manufacturer's patient information sheet (Medication Guide) when you begin treatment with onabotulinumtoxinA injection and each time you receive
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of differently sized, shaped and colored spherical elements that need to be twisted against the central axis in order to disassemble and fully reassemble the toy. It consists of an outer round layer (
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-2 hours depending on the size of your jar. A full melt pool will reach a depth of 1/2 inch. This will allow the fragrance to disperse more completely into the room. (Remember burn your candles for a maximum of
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
- L26:1747 strongly suppresses
- L24:2792 strongly suppresses
- L22:1337 strongly excites
- L26:1248 strongly excites
- L25:515 strongly excites
- L24:795 suppresses
- L26:1634 suppresses
- L26:1564 excites
- L18:569 excites
- L24:1056 suppresses
- L24:2535 excites
- L24:469 excites
- L8:7 excites
- L23:2177 excites
- L23:2830 suppresses
- L25:225 excites
- L23:2936 weakly suppresses
- L8:14 weakly excites
- L25:526 weakly suppresses
- L20:2843 weakly excites
- L5:91 weakly excites
- L26:1273 weakly suppresses
- L11:2 weakly suppresses unclear: blank before a year or number
- L7:122 weakly excites
Feeds later layers
None: this is the last layer.
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
- �
- üler
- 榴
- ışı
- 觚
- ียม
- phanumeric
- eker
- 去买
- iffies
Suppresses
- v
- v
- (v
- [v
- =v
- $v
- )v
- >v
- →v
- *v