'that' in 'so that' purpose clauses
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. 3 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 lights up on the word 'that' when it follows 'so' to mean 'in order that', as in 'arrange them so that they made sense'. Turning it up did not make the model write about purposes or goals; its replies just wandered.
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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then you are done. b. If you get , then you have to algebraically manipulate your function so that you can use direct substitution: i. Factoring ii. Rationalizing (Rad
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, model year numbers (cars), and other useful numbers there. Once they had their numbers, they needed to arrange them on the paper so that they made sense (no dividing by zero) and so that they can be solved
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a carpenter's square.⏎Find the area of the shaded region created by the two overlapping⏎triangles in terms of a and b?⏎Position the lines so that they are perpendicular to each other.⏎What can you say about the equations of perpendicular
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and don’t forget to limit the amount of them per day.⏎When the animals are first fed with strawberries, you should consider a small amount before leveling up so that their stomach can get used to it. Their digestive system is so sensitive that it
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pieces, it is possible to fit the pieces together again to make a new shape. How many new shapes can you make?⏎Can you shunt the trucks so that the Cattle truck and the Sheep⏎truck change places and the Engine is back
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. Using the rules, where⏎are they all sitting? Can you find all the possible ways?⏎What is the best way to shunt these carriages so that each train⏎can continue its journey?⏎If you split the square into these
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: because of the other (the other one, the other one, the other one, the other one, the other one, etc. etc. etc. etc. etc. etc. etc. etc. etc. etc. etc. etc
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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or less ornamented, laid over the saddle or furniture of a horse, especially of a sumpter-horse or horse of state.⏎- Clothing, especially sumptuous clothing; equipment; outfit.⏎- To cover with a caparison,
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me, but it never lasts longer than five minutes (32).⏎6: Correct Usage 7 | Mind you, the spare room is pretty sumptuous too; in fact, I think he gets his own Jacuzzi, so
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have strep throat. The cause of these signs and symptoms could be a viral infection or some other illness. That's why your doctor generally tests specifically for strep throat.⏎It's also possible for you to be exposed to a person who carries
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6: William Faulkner's Absalom, Absalom!,⏎1938: John Dos Passos's U.S.A. trilogy,⏎19<|endoftext|>A view can become a page
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
- L0:2130 weakly suppresses
- L0:1806 weakly excites
- L2:829 weakly excites
- L0:1506 weakly excites
- L2:2692 weakly excites
- L0:1974 weakly suppresses
- L2:1826 weakly excites
- L2:2423 weakly suppresses
- L1:146 weakly excites
- L2:2817 weakly suppresses
- L1:2094 weakly suppresses
- L1:2801 weakly excites
- L1:1430 weakly excites
- L0:2825 weakly suppresses
- L1:2142 weakly suppresses
- L0:2908 weakly suppresses
- L0:2536 weakly excites
- L0:2857 weakly suppresses
- L2:2912 weakly excites
- L1:1666 weakly excites
- L2:493 weakly suppresses
- L1:2005 weakly suppresses
- L1:2060 weakly excites
- L1:638 weakly suppresses
Feeds later layers
- L6:1199 excites
- L4:653 excites "that" after "such" or "such a way"
- L4:2528 weakly excites
- L17:2834 weakly excites
- L8:132 weakly excites
- L20:2850 weakly suppresses
- L4:1035 weakly excites 'for' after verbs like 'fall' or 'click'
- L7:1673 weakly excites
- L23:2567 weakly suppresses
- L6:1395 weakly excites
- L4:2825 weakly excites "to" after 'for X' infinitives
- L4:1460 weakly excites
- L16:2549 weakly excites
- L10:2471 weakly excites
- L4:227 weakly excites
- L18:1588 weakly suppresses
- L27:951 weakly suppresses
- L6:2462 weakly excites
- L20:2080 weakly excites
- L20:2940 weakly suppresses
- L16:2427 weakly suppresses
- L17:1803 weakly suppresses
- L18:813 weakly excites
- L12:2136 weakly 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
- upply
- ẗ
- mask
- 的任务
- sidelined
- 产业结构
- あげ
- Cursor
- MASK
- hai
Suppresses
- imest
- elman
- ainer
- omens
- iaz
- 所在
- engu
- itos
- iais
- bour