"have" or "take" after you or they
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 lights up on the verb right after a pronoun like "you" or "they", especially the word "have" (as in "you have to" or "you have more"). Turning it up did not produce any obvious change in the topic of the writing.
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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more possibilities of surviving adverse environmental conditions. We have very extreme weather in the highlands, and if it gets very cold and you only have<|endoftext|>Poor communication in just one area can affect the entire health care experience.⏎F
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you usually get access to a command line interpreter, or shell. This takes your input and runs programs for you. If you are familiar with DOS, you already have used a command line interpreter: the COMMAND.COM program. Under DOS, your shell
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key step in the gemstone id process. By using the unaided eye and 10 power magnification to determine the visible characteristics you are able to take a large step in the right
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is an unsupervised learning problem.⏎To illustrate this, predicting whether a person will default on a loan or not. For this, you have a target variable i.e. ‘defaulting on loan’, which is a supervised
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8.5 lbs in Thailand and 15 lbs in Asia as a whole. The Burmese have traditionally snacked a lot because they have traditionally only had two meals a day—one at around 10:00am and another
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time of less than one second to be excellent, less than 5 seconds to be good, and greater than 5 seconds to be poor.⏎Sometimes you also have to consider other metrics that don't have a direct impact on the user experience.
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: of course, the problem is of the fact that of course, with the ... and so on of, of course, with ... of, of course, of, of, and of, of, of, of, of, of,
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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say that we're taking a tough stand against misbehavior, but the data say it simply hasn't been effective in improving student behavior or ensuring school safety."⏎Tell it to Congress. The Gun-Free Schools Act of 1994 mandates zero
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scientific objectivity, universality, and rational deduction over other ways of knowing the world. (148) Cogito ergo sum: "I think
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DRM protected ebook on your desktop or laptop you will need to have Adobe Digital Editions installed. It is a free software. We also strongly recommend that you sign up for an AdobeID at
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aining things like headers, etc.⏎Upon success, if you set ``parse_xml_document=True``, this will⏎contain the parsed xml_document. I'll take care of parsing errors⏎with an appropriate pylons_response for you.⏎server_response
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
- L16:1860 suppresses
- L14:1719 excites
- L10:668 suppresses
- L12:336 suppresses
- L17:1196 suppresses
- L17:2129 excites
- L9:2219 excites
- L14:526 suppresses
- L9:584 suppresses
- L8:1300 excites
- L14:1148 excites
- L3:110 weakly excites
- L11:2015 weakly suppresses
- L15:795 weakly excites
- L16:2655 weakly excites
- L4:2085 weakly excites
- L5:146 weakly excites
- L14:149 weakly suppresses
- L12:434 weakly suppresses
- L16:674 weakly excites
- L6:1114 weakly excites
- L13:1808 weakly excites
- L6:388 weakly excites
- L16:2128 weakly excites
Feeds later layers
- L24:1263 suppresses
- L19:1969 suppresses
- L24:424 suppresses
- L21:1441 weakly suppresses
- L20:645 weakly suppresses
- L19:1371 weakly excites
- L23:1814 weakly suppresses
- L23:984 weakly suppresses
- L22:823 weakly suppresses
- L22:2398 weakly suppresses
- L20:2338 weakly suppresses
- L22:205 weakly suppresses
- L20:1103 weakly excites
- L20:851 weakly excites
- L19:1150 weakly excites
- L19:385 weakly excites
- L22:1093 weakly excites
- L24:803 weakly suppresses
- L19:1482 weakly suppresses
- L26:2817 weakly suppresses
- L19:272 weakly suppresses
- L21:239 weakly excites
- L22:2530 weakly excites
- L23:312 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
- ao
- oser
- 能在
- Taj
- ,在
- 不在
- 能够在
- uge
- 就在
- avoided
Suppresses
- aturing
- sağlamak
- proposing
- շ
- 疲劳
- offre
- abric
- atoria
- lectic
- -century