'to' after a verb of sharing or using
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. 18 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 fires on the word 'to' when it follows a verb or noun phrase that asks for an action, such as 'Share and remix collections to collaborate' or 'Sign up to view'. Turned up, the model's writing got stuck in repetitive loops.
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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caused<|endoftext|>This preview shows pages 1–2. Sign up to view the full content.⏎This preview has intentionally blurred sections. Sign up to view the full version.View Full Document⏎Unformatted text preview: 60
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まだ⏎J-GO EDITED BY: まだ⏎If you're going to give this activity⏎a low-rating, please post a useful⏎comment to
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.<|endoftext|>Start a 10-Day Free Trial to Unlock the Full Review⏎Why Lesson Planet?⏎Find quality lesson planning resources, fast!⏎Share & remix collections to collaborate.⏎Organize your curriculum with collections. Easy!⏎Have time to be more
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Why Lesson Planet?⏎Find quality lesson planning resources, fast!⏎Share & remix collections to collaborate.⏎Organize your curriculum with collections. Easy!⏎Have time to be more
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� of the bottle with vegetable oil, and fill the remaining part with water. Next, add 10 drops of food coloring (any color will work) to the bottle. Finally, break the Alka-Seltzer tablet into a few
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flipped and rotated in all directions. This may help to make some furtive words more readable. Also the spacing, boldness and capital case can be switched to make the letters stand out from their context. Optionally a handy hover-cross is shown
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: A)) 1) 1) 1) 1) 1) 1) 1) 1) 1) 1) 1) 1) 1) 1) 1)
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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, tsunami, earthquake or forest fire. Reliable and clean sources of water are on the list of top resources to find and secure.⏎Significant droughts, an increase in demand and a low
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the attitude of a parent observing a growing child's accomplishments and successes.⏎Mudita is also traditionally regarded as the most difficult of the brahmaviharas to cultivate. To show mudita is to celebrate happiness and achievement in others even when
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. The principal producing horizon is the Bartlesville sand, found at an average depth of 1,150 feet. To January 1, 1944, the Bartlesville sand has
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actating cows prefer temperatures below about 20-21°C. To cope with warm weather, they use a variety of strategies to maintain normal body temperature
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
- L17:1297 excites
- L9:20 excites
- L17:812 excites
- L16:1168 excites 'to' after verbs of change
- L16:771 suppresses
- L8:328 excites
- L16:1137 weakly suppresses
- L11:506 weakly excites
- L17:535 weakly excites
- L10:155 weakly excites
- L16:33 weakly excites
- L15:184 weakly excites
- L3:229 weakly excites
- L16:1627 weakly excites
- L17:36 weakly excites
- L14:771 weakly excites
- L2:371 weakly excites
- L13:1237 weakly excites
- L13:132 weakly excites "of" after a noun
- L11:2117 weakly excites
- L16:489 weakly excites
- L16:1949 weakly excites
- L11:251 weakly excites
- L8:127 weakly excites
Feeds later layers
- L20:1872 excites
- L20:1214 excites
- L20:922 excites
- L21:2012 excites "to" before an infinitive or noun
- L19:2659 excites
- L20:810 excites
- L19:386 suppresses
- L20:1623 excites
- L19:1608 excites
- L19:2335 excites
- L19:2432 excites
- L19:2738 excites
- L19:2567 excites
- L20:1257 excites
- L21:1556 excites
- L19:2040 excites
- L20:1652 excites
- L20:1106 excites
- L19:385 suppresses
- L19:2932 excites
- L22:1166 excites 'to' after words needing an infinitive
- L21:1773 excites 'to' starting a purpose clause
- L19:1523 excites
- L24:2854 excites
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
- 進行
- 进行
- atures
- retrieve
- 筹集
- overcome
- 赢得
- 获取
- solve
- _der
Suppresses
- izable
- isable
- ror
- ucing
- Convertible
- ConfigureAwait
- альных
- 梅州
- UserRepository
- /AFP