'to' after verbs like prove or time
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. 7 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 responds to the word 'to' when it comes after words like 'prove', 'suggested', 'time', or 'enough', where a verb or noun is about to be followed by another verb. Its strongest negative reactions are on the same kind of 'to', so it seems to flip on one side and off on the other. Turning it up did not produce a clear theme in the sample text.
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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fact it may be the simplest building code ever to exist except for some early Roman Emperor’s rules on construction. The HUD Code basically says that if you can prove to the Department of Housing and Urban Development that you can
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to our mobile site7ads6x98y<|endoftext|>Arabic Numeral 1 in Roman Numerals converter tool is available here. This tool can prove to be very handy in the conversion of Arabic to Roman numerals. The tool basically
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standard filmmakers today still are struggling to measure up to,” he said.⏎Kaufman said the Disney film is based, in part
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images. This is based on neural networks similar to the human brain, which are fed large amounts of data.⏎A person can however understand an image and commit this to memory from just one sighting. They can add context, from each unique patient and
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opinion. It will center on research traditions that address the perception that public opinion exists and that this perception is gained by observing and communicating. If we accept as reality that perceptions of the reality of public opinion exist, then we may ask how
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magazine. It was Wennberg who came up with the man to do just that. After discussion with another friend, Wes Prichard, Prichard suggested to Wennberg<|endoftext|>Home > Preview⏎The flashcards below were created by user⏎
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: I am trying to find out about the possible applications of the results from the previous studies on the application of the results from the application of the results of the following steps. So far, the application of the results of the following steps. So
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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will help you learn how to grill. Remember to always follow a few rules:⏎- Keep your grill grate clean to prevent sticking.⏎- Give yourself plenty of time to prepare and heat up the grill before cooking.⏎- Always keep an eye on what
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-flat (Elgar wrote to his friend August Jaeger that he had “thrown over all key relationships as formerly practiced” but that he “was not silly enough to think (<|endoftext|>Meteorite hit atmosphere at 64,000 mph
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the Nazis had gained power in some parts of Germany. The Nazis wanted all Jews to be killed. Otto Frank, Anne's father, did not hestitate to wait for the Nazis to come into full power. In 1933
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foods such as ready meals, tinned goods and pasta sauces. Consider using natural foods, herbs and spices to flavour your food, and give your taste buds time to get used to the new flavours.⏎As we age our digestion can become less efficient
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
- L6:1880 excites "by" introducing a deadline or means
- L6:2359 excites
- L6:1133 excites "to" before an infinitive verb
- L6:914 excites
- L6:1684 suppresses
- L6:2057 suppresses
- L6:2287 suppresses
- L6:1679 excites 'and' joining two actions in instructions
- L6:2747 suppresses
- L6:2064 suppresses
- L6:439 suppresses
- L4:910 excites
- L5:2641 excites unclear: scattered word fragments
- L4:2890 suppresses
- L6:1103 suppresses
- L4:113 suppresses
- L3:307 suppresses
- L6:2145 excites
- L6:2000 suppresses
- L6:1621 suppresses
- L6:2882 suppresses
- L6:2020 weakly excites
- L6:794 weakly excites
- L4:518 weakly suppresses
Feeds later layers
- L8:45 excites
- L8:941 excites
- L13:1046 excites 'in' or 'on' ending a phrase
- L8:234 excites
- L10:452 excites
- L14:2405 excites
- L10:217 excites
- L14:453 weakly excites
- L14:1584 weakly excites
- L16:1484 weakly suppresses
- L22:489 weakly excites
- L8:315 weakly excites
- L8:71 weakly excites
- L14:313 weakly excites
- L11:437 weakly excites
- L13:918 weakly excites
- L8:98 weakly excites
- L14:193 weakly excites
- L9:836 weakly excites
- L16:1100 weakly suppresses
- L25:942 weakly excites
- L15:41 weakly excites
- L10:132 weakly excites
- L8:187 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
- ported
- 本报记者
- AuthProvider
- מין
- LineStyle
- BeNull
- 网小编
- pire
- reature
- riend
Suppresses
- sonras
- 批
- Right
- Right
- 起
- proper
- tri
- oby
- 三角
- 複