nouns for who or what a text is about
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. 13 of Qwen3-0.6B. Its strongest snippets are mixed: they peak on different tokens. 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 is unclear and loosely tied to descriptions of a subject. It fires on words like 'files', 'kids', 'audience' or 'may' in sentences about who or what a text is describing, such as 'All of these files will adhere' or 'Kids in this age group are dealing'. Turning it up or down gave repetitive or unrelated text.
Written by Claude from this neuron's activations and steering outputs. Confidence: low. 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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between the bottom-left square and the rightmost coloured square.⏎To evaluate your solution, the judges will run your program against several different input files. All of these files will adhere to the following bounds:⏎As<|endoftext|>Broccoli is a cruciferous
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and begin to code.⏎Your program should read from the file . The file will describe a single post-cubist squarist painting.⏎Your program should
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for this audience still tend to be based in the real world, and the chapters rely on cliffhangers to keep readers engaged. Kids in this age group are dealing with social situations, fitting in, and learning that there are consequences to their choices
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appearance, and correct.” – Mike Markel⏎Technical writing is used in many technical and professional fields. Its goal is to objectively convey to specific audiences, who may not be subject matter experts, in an accessible manner and in easy to understand language
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depression does not need to be addressed. Experts agree that depression, regardless of its cause, is something that should be addressed and treated.⏎Teens may also be dealing with bullying at school, or even just “harmless” teasing. They
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application in laboratories. However, there are several factors that influence the results, such as different types of pH meters and electrodes, the varying characteristics of a sample to be measured, and the chemical properties of a solution. METTLER TOLEDO
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 can't be the only thing in the mind in the mind, it's also very important in, the mind is the thing, in the mind, the problem, in the problem, the matter, in the matter, the matter,
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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pixel. Thus, an 8-bit pixel would have a maximum value of (2^8) - 1 = 255. Let the maximum value for pixels in image I be MAX.⏎Express the PSNR in decibels
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91-1995⏎The materials in this archive are copyright © The Manitoba Library Association. Reproduction for personal use is permitted only if this copyright notice is maintained. Any other reproduction is prohibited without permission Copyright information for reviewers⏎Young
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this message, Please click this link to reload this page.(Do not use your browser's "Refresh" button). Please email us if you're running the latest version of your browser and you still see this message.⏎Table of contents⏎Bluetooth is
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A good general rule of thumb is, if they’re old enough to read, they’re old enough to pack their own bags. Assuming your little ones are old enough, the most effective
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
- L11:1900 suppresses
- L7:61 excites
- L12:386 excites
- L11:1296 suppresses
- L12:1986 excites
- L8:584 excites
- L12:1938 excites
- L12:163 weakly suppresses
- L11:2224 weakly suppresses
- L7:611 weakly excites
- L11:741 weakly suppresses
- L7:1413 weakly excites
- L7:1454 weakly excites
- L10:1363 weakly excites
- L7:1657 weakly excites
- L6:1812 weakly suppresses
- L7:1381 weakly excites
- L5:1973 weakly excites
- L8:248 weakly excites
- L7:949 weakly suppresses
- L0:463 weakly suppresses
- L7:1024 weakly suppresses
- L8:2211 weakly suppresses
- L10:1597 weakly excites
Feeds later layers
- L15:1594 suppresses
- L14:1342 suppresses
- L17:2729 excites
- L14:2363 suppresses
- L19:1507 excites
- L17:2087 excites
- L17:1461 suppresses
- L17:2644 suppresses
- L18:2348 suppresses
- L17:2531 suppresses
- L14:3031 excites
- L14:2504 excites
- L15:1513 suppresses
- L14:1287 suppresses
- L16:1866 excites
- L16:2846 suppresses
- L14:685 excites
- L18:1606 excites
- L15:826 suppresses
- L21:2779 excites
- L20:2529 suppresses
- L15:2964 excites
- L15:2319 excites
- L14:922 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
- unknown
- unknown
- ars
- NER
- jas
- 为主线
- دير
- ncy
- nc
- ners
Suppresses
- _buffers
- forge
- émon
- iest
- ANDARD
- 就可以
- easiest
- ifact
- zf
- 便可