file formats and technical jargon
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 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 lights up on technical words, mostly about file formats (AVI, MP4, headers), archives and disk structure. It is not clear what ties them together. Turning it up made the model drift into lists of technical terms and file descriptions.
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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. Other file formats can be read by creating an NMRView parameter file that contains a description of the layout of the file.⏎|Program||Format||File Header||Block Header||Parameters|⏎|Varian||Serial||32/2
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of MPEG-4.⏎||Moving Picture Experts Group⏎||Flexible, works with a wide range of editing and compression programs; have support for multiple video or audio streams⏎||MP4 is are usually compressed; compatible with more devices like iPad,
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||MPEG-4 Video File⏎||AVI is a video wrapper format created by Microsoft, storing video data that might be encoded with various codecs. AVI files can be played by various video players, but the player must support the codec
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you highlighted a title with the data, you would use this option to prevent the program from sorting your title into the list. (In other words, the first row stays in place, regardless of its first letter).⏎When you have everything set the
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the player must support the codec used to encode the video data.⏎||MP4 files are an open standard that everyone can use.⏎|Programs that open AVI/MP4 files<|endoftext|>Pioneer Papers connects you with professional substantive editors to build
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.tar utility.⏎- x – Flag is used to extract the zipped files⏎- v – Flag is for verbose, or creating a list of extracted
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 blog. I am a bit of a fan of the blog, and I have been thinking about this. The blog is popular, and I have been thinking about this. Let's say I am a big fan of the blog, and
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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with a little water and use the sidewalk, cardboard or wood fence. Compare and contrast the textures and patterns the natural bristles make.⏎British artist Andy Goldsworthy creates art using natural materials. He creates his sculptures outside and leaves them in his
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data on on sicklefins, tagged them, and collected sperm and eggs for captive rearing. For more photos, visit: https://www.flickr.com/photos/usfwssoutheast/albums/72157667
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concentrations below 450 parts per million by 2100. Alternative energies will only get humankind so far, and a vast removal of CO2 will be an important tool in the race to remove the gas from the atmosphere.⏎
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hard coal and 276 Gt of lignite. These reserves would result in ~2,900 Gt of CO2 if combusted unabated, with approximately two thirds of this coming from the hard
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
- L6:1933 excites
- L6:2095 weakly suppresses
- L6:1758 weakly excites
- L5:513 weakly excites
- L5:2640 weakly suppresses
- L6:2793 weakly excites
- L6:1573 weakly suppresses
- L6:1760 weakly excites data quality and data formatting
- L6:1131 weakly suppresses
- L1:1971 weakly excites
- L3:2896 weakly excites
- L6:2409 weakly excites
- L3:2140 weakly suppresses
- L6:2033 weakly suppresses
- L3:2551 weakly suppresses
- L0:1731 weakly suppresses
- L2:2577 weakly suppresses
- L3:1744 weakly excites
- L2:2843 weakly excites
- L6:1158 weakly suppresses
- L2:2026 weakly excites
- L0:2953 weakly excites
- L1:2663 weakly suppresses
- L6:2784 weakly excites
Feeds later layers
- L20:1674 weakly excites
- L20:967 weakly excites
- L9:3027 weakly suppresses
- L23:1087 weakly suppresses
- L20:732 weakly excites
- L9:1481 weakly excites
- L10:1629 weakly suppresses
- L10:1864 weakly excites
- L12:2585 weakly excites
- L10:1446 weakly suppresses
- L18:3061 weakly suppresses
- L11:2203 weakly excites
- L10:3006 weakly excites
- L17:2670 weakly excites
- L10:2564 weakly excites
- L9:1636 weakly suppresses
- L20:678 weakly excites
- L12:2472 weakly excites
- L24:1899 weakly excites
- L17:2352 weakly suppresses
- L23:836 weakly suppresses
- L10:1724 weakly excites
- L11:640 weakly suppresses
- L10:1121 weakly suppresses 'to' or verb after 'what' or 'whether'
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
- renderer
- oser
- 内容
- .header
- ako
- فس
- brief
- tokenizer
- unpack
- utenberg
Suppresses
- mma
- ью
- ��
- imity
- -append
- ところ
- ERRUPT
- ALLY
- 海滨
- embro