'by' introducing a method or means
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. 17 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 'by' when it names how something is done, like 'sorted by length', 'by email', or 'by extension'. Turning it up gave wandering, repetitive sentences, which don't clearly show the pattern.
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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we'll notify you by email when courses for each new term are displayed here.<|endoftext|>Epigenetics is
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, history, and society.⏎About the authors⏎Elaine Treharne is Roberta Bowman Denning Professor of Humanities and Professor of English (and, by courtesy, of German Studies) at Stanford University, as well as Director of Stanford
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focus" uses 5 letters: C F O S U.⏎No direct anagrams for focus found in this word list.⏎List shorter words within focus, sorted by length⏎All words formed from focus by changing one letter⏎Browse words starting with
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://nrich.maths.org/⏎is one of a series of problems designed to develop learners' team⏎working skills. Other tasks in the series can be found by going to⏎What are you aiming to⏎For the task:⏎The team has
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version of bool-extras is 0.3.0 and is available on Hackage.⏎If you have cabal-install (which you really should have, by the way), the
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eat is the main ingredient in your diet.⏎Food is food insofar as our tastes are concerned. But, when it comes to our stomachs, and, by extension, our whole body is involved, food is more than just food. It
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 next time, from now on, the next time, the next time, and so on. The next time. So. in. on. from. . . . . from. . . . . from. . . . from
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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making the subject seem baffling and inaccessible to the outsider. Thankfully, Patrick breaks down any barriers by describing the fundamentals extremely well. Important beginner questions are answered thoroughly from the start: why don’t people bother drawing carbon hydrogen bonds? What is a
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all are waiting for. Teachers, parents and student, now you will find below the Solutions of Class 10 Science Chapter 4 Environmental management questions answers. From the subject English of Maharashtra State Board.⏎Below you can see
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teachers that have interest in using the Haber video in their curriculum. Reading materials and lesson ideas are available. I recently used a lesson that my colleague created directly from the provided materials. You can check out that lesson here. The lesson included some
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bathing to prevent rickets and leading interpreted discussions on pregnancy and birth control.<|endoftext|>Where’s The Harm – From Materials Processing?⏎Chemical processing: Chemical companies produce more than 1,0
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
- L9:1340 suppresses
- L10:1054 suppresses 'from' after movement or transfer verbs
- L4:1394 excites
- L5:763 suppresses
- L4:2347 excites
- L8:2982 excites
- L6:1726 suppresses
- L16:1079 suppresses "by" after a past participle
- L16:2307 excites
- L10:371 weakly suppresses
- L6:2594 weakly excites 'with' after a verb of action
- L10:201 weakly suppresses the letter B in vitamin names
- L13:3044 weakly excites
- L4:1013 weakly suppresses
- L16:2120 weakly suppresses
- L0:2486 weakly excites
- L16:1847 weakly suppresses
- L7:1734 weakly excites
- L12:2882 weakly suppresses
- L11:591 weakly suppresses 'if' in conditional asides
- L4:1126 weakly excites
- L15:2443 weakly suppresses
- L2:1554 weakly excites
- L2:1848 weakly suppresses
Feeds later layers
- L19:2273 excites
- L18:2916 excites
- L23:2763 excites
- L19:2719 excites
- L18:1747 excites
- L18:2607 excites
- L18:2599 weakly suppresses
- L18:2244 weakly excites "of" inside titles and names
- L27:1645 weakly excites
- L24:1915 weakly excites 'in' after a noun phrase
- L21:225 weakly suppresses
- L21:871 weakly excites
- L19:1618 weakly excites
- L19:2869 weakly excites
- L20:2239 weakly excites
- L19:1874 weakly excites
- L27:1086 weakly suppresses
- L24:137 weakly suppresses
- L21:461 weakly excites
- L19:1564 weakly excites
- L18:1165 weakly excites
- L21:763 weakly suppresses
- L18:1346 weakly suppresses
- L18:1509 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
- 这种方式
- virtue
- means
- gunfire
- sheer
- conventions
- pite
- intuition
- es
- convention
Suppresses
- cape
- ://${
- stakes
- kus
- stag
- ��
- scar
- ldr
- 那人
- aned