middle pieces of split longer words
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. 2 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 fires on the later pieces of long words that the model had to split into several chunks, such as 'reconfiguration', 'comparability' and 'grouping'. Turning it up did not produce a clear visible change in 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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reconfiguration region are governed by field-aligned currents, which in their turn generate auroral bright
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any reconfiguration event, there is no need to run this command interactively.⏎For more information
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and texture of materials on the surface. Areas with a redder color appear red, and areas with a grayer color appear blue. Young crater rays, arrayed radially around fresh impact craters
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certain services category definition. For such reasons, compilers should have in-depth knowledge of the methodologies and definitions used by the other data sources to ensure consistency and the comparability of the resulting merged data. Various dimensions of the different sources must be analysed by
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data points, it mainly deals with unlabelled data.⏎Some applications of unsupervised machine learning techniques include: clustering (grouping coherent segments of customers, or grouping logistic pick-up points), anomaly detection (detect
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asymmetric radial transects are fixed. We provide a new method that eliminates edge-effect bias for designs that use asymmetric radial transects with fixed orientation, but a workable solution for polygonal transects remains elusive.⏎Keywords: Boundary overlap, Inclusion
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: this is the time when I have been thinking about lately, I need to think about this is the time when I have been thinking about lately, I need to think about this is the time when I have been thinking about lately, I need
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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al and differentiation.⏎"This research will be a big boost for stem cell scientists. The protein phosphorylation sites identified in this study are freely available to the broader research community, and researchers can use these data to study the cells in greater depth and determine
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a threat from “possible large” earthquakes, studies on active fault zones in Thailand are still minimal.⏎With all of these in mind, the Geophysics research group at Faculty of Science, Mahidol University is founded with the objective of being
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drivers of plant redistribution in mountainous areas under global change.⏎Personal links. ResearchGate | GoogleScholar | Research Group Website⏎Institute. Department of Agronomy, Food, Natural resources, Animals
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0, 1, depending on the quadratic residue properties of modulo .⏎A famous result of Gauss states that when the modulus is a prime , the two sums and have
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
- L1:719 weakly suppresses
- L1:585 weakly excites
- L1:2382 weakly excites
- L1:935 weakly excites
- L1:223 weakly excites unclear: 'selected' and 'shape' in mixed text
- L0:2833 weakly suppresses
- L0:234 weakly excites
- L1:1667 weakly excites
- L0:281 weakly excites
- L0:1848 weakly excites
- L0:1051 weakly suppresses
- L0:2808 weakly suppresses
- L1:936 weakly suppresses
- L1:525 weakly excites
- L1:951 weakly excites
- L0:378 weakly excites
- L0:1150 weakly suppresses
- L1:1977 weakly excites
- L1:756 weakly suppresses
- L1:152 weakly suppresses
- L0:530 weakly excites
- L1:2723 weakly suppresses mid-word syllables in capitalised names
- L1:1092 weakly suppresses
- L1:897 weakly suppresses
Feeds later layers
- L11:452 suppresses
- L20:2090 suppresses
- L10:276 suppresses
- L20:2200 weakly excites
- L21:2417 weakly excites
- L15:1548 weakly excites
- L24:767 weakly suppresses
- L21:2700 weakly excites
- L13:1115 weakly suppresses
- L12:2225 weakly excites
- L4:2919 weakly suppresses
- L8:77 weakly suppresses
- L9:65 weakly suppresses
- L6:1129 weakly suppresses
- L25:2047 weakly excites
- L13:681 weakly suppresses
- L24:2207 weakly suppresses
- L22:234 weakly suppresses
- L13:1218 weakly excites
- L14:3046 weakly excites
- L25:873 weakly suppresses
- L22:1940 weakly suppresses
- L17:2864 weakly excites
- L4:2978 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
- éri
- ью
- 旬
- mpr
- 十字
- mighty
- antino
- 宣言
- 冷静
- erty
Suppresses
- COPYING
- ::⏎
- inceton
- Contrib
- mum
- PLICATE
- Foo
- ypes
- Islamabad
- 벽