person names and role nouns
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 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 responds to words that name a person or a role, like 'queen', 'manager', 'mother', a person's name, or a company's leader. When it was turned up, the model drifted toward talking about people and their situations.
Written by Claude from this neuron's activations and steering outputs. Confidence: medium. 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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about the universe is wrong. Physicist and best-selling author Brian Greene takes you on a journey that bends the rules of human experience.⏎BRIAN GREENE (Columbia University): Why don't we ever see events unfold
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Dr. Nuha El Sayed, vice president of Healthcare Improvement at the American Diabetes Association. Hi, thanks for joining me.⏎Dr. Nuha El Sayed: Thank you for having me.⏎So, today I would love to pick your
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2008 Express Edition >> Visual Studio Tools >> Visual Studio 2008 Command Prompt (Run with administrator privileges)⏎- Once you are in the command line, you can use any editor such as edit to write a C program.⏎
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reaction to this paragraph?⏎The king neglected his wife?⏎He drank too much?⏎The Queen was busy with her own guests?⏎Put yourself in the place of the queen if you are female, the place of the king if you are male. How
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synopsis of the first video in the series written by a guest on this blog, Roni Jacobson, a science journalist based in New York City.⏎By Roni Jacobson⏎Being unable to recall a word on
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maximising costs and power savings - a critical challenge to overcome for the success of any exascale initiative," said Vishal Dhupar, managing director of NVIDIA India.⏎The development of exascale computing systems, which would be hundreds of
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 "f" and "f" in the context of the "f" and "f" in the context of the "f" and "f" in the "f" and "f" in the "f" and "
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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however freely search the site and view the abstracts and keywords for each book and chapter.⏎If you think you should have access to this title, please contact your librarian.⏎To troubleshoot, please check our FAQs, and if you can't find
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of Frank Churchill’s secret engagement to Jane.⏎“Good God!” cried Emma, “Jane actually on the point of going as governess! What could he mean by such horrible indelicacy? To suffer her to engage herself — to
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abstracts and keywords for each book and chapter.⏎If you think you should have access to this title, please contact your librarian.<|endoftext|>Information on creating school gardens for outdoor learning, compiled by the National Clear
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is for Gertrude to try and determine what is going on with Hamlet. She starts out on the offensive, accusing him of offending his (step-) father, Claudius. Hamlet is already in high spirits having just missed an opportunity
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
- L13:1448 excites
- L0:620 suppresses
- L13:1119 weakly excites
- L13:1680 weakly excites
- L13:1635 weakly excites
- L10:2135 weakly suppresses
- L12:1946 weakly suppresses
- L7:36 weakly suppresses
- L11:1264 weakly suppresses
- L12:2999 weakly excites
- L13:770 weakly excites
- L13:2201 weakly suppresses
- L14:2260 weakly excites
- L11:751 weakly suppresses numbers and quantities mid-phrase
- L13:1765 weakly suppresses
- L0:698 weakly excites
- L1:865 weakly suppresses
- L14:468 weakly suppresses
- L3:972 weakly excites
- L14:2618 weakly suppresses
- L11:1704 weakly suppresses
- L5:1111 weakly excites
- L12:710 weakly excites
- L5:1214 weakly suppresses
Feeds later layers
- L16:1182 excites
- L16:2819 excites
- L16:2803 weakly suppresses
- L17:1070 weakly excites
- L17:570 weakly excites
- L23:1624 weakly excites
- L21:1699 weakly suppresses
- L22:1646 weakly excites
- L21:3041 weakly suppresses
- L19:2961 weakly suppresses
- L18:756 weakly excites
- L18:3060 weakly suppresses
- L25:1668 weakly excites
- L26:820 weakly excites
- L23:823 weakly suppresses
- L25:2351 weakly suppresses
- L17:2098 weakly suppresses
- L22:2664 weakly suppresses
- L16:2657 weakly excites
- L17:1245 weakly excites
- L16:877 weakly suppresses
- L20:821 weakly suppresses
- L18:2086 weakly suppresses
- L20:161 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
- ception
- cep
- 听着
- 绉
- ze
- cej
- pii
- admins
- 当たり
- acie
Suppresses
- amb
- chaft
- oolStrip
- 洲
- ServiceProvider
- iom
- probante
- =yes
- gren
- vestment