the word 'value'
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. 5 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 word 'value' in phrases like 'add value to', 'the value of' or 'diminish the value'. When turned up, the text did not clearly pick up the theme, so the link is only from the examples.
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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anything we need would be provided for by someone other than ourselves, we wouldn’t stand up and do something; we would not acquire skills we could use to add value to others and hence earn and income for ourselves.⏎– dependence on others
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: “Our vision is fill the feeding gap and to make ECD Centres so attractive that parents want to send their children to school. We aim to add value to the existing government feeding schemes, allowing children to receive two meals a day.
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processor to type a story instead of handwriting it.⏎This is where technology is used to carry out learning tasks in relatively more efficient ways. the technology here only adds value to the assignment and does not transform how it is done, examples of this include
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ers and attach them to the deck⏎How To Build Cascading Deck Stairs⏎Building cascading deck stairs can be a fun and easy project that can add value to your home. There are a few things to consider when building cascading stairs
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to a minimum so the restored object doesn’t look like it just came from the shop? Over-zealous intervention in the course of restoration can actually diminish the value of high quality antique furniture to as little as one-tenth of its market value
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the existing product by introducing new features in it or bring out a new product to sustain in the market.⏎(iv) Productivity Productivity is calculated by comparing the value of outputs with the value of inputs. It is used as a measure of efficiency
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: (1) 1 when both 0 and 1, for both 0 and : 2 4 5, 3 4 4 5 as 0 4 2 4 3 4
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 are called one-to-one functions. DEFINITION A function is called a one-to-one function if it never takes on the same value twice; that is, If a horizontal line intersects the graph of
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three possible values: +, 0 and -⏎1997 note: This is a
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1 FUNCTIONS AND MODELS Not all functions possess inverses. Lets compare the functions and whose arrow diagrams are shown in Figure 1. Note that never takes on the same value twice (any two inputs in have different outputs), whereas does take
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, the exponential sum version of the Gauss sum modulo is defined by⏎and the quadratic residue version is defined by⏎where is the Jacobi symbol, which has values
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
- L3:2493 suppresses
- L4:572 weakly suppresses
- L4:2401 weakly excites
- L3:2146 weakly excites
- L1:1807 weakly suppresses
- L4:1103 weakly suppresses
- L4:25 weakly excites
- L2:3044 weakly excites
- L1:1179 weakly suppresses 'in' after 'result' (results in)
- L1:2212 weakly suppresses
- L4:923 weakly suppresses
- L3:1834 weakly excites
- L4:1667 weakly suppresses
- L4:2745 weakly suppresses
- L3:2415 weakly suppresses
- L4:2413 weakly suppresses
- L1:992 weakly excites
- L0:65 weakly suppresses
- L2:2818 weakly suppresses
- L3:354 weakly suppresses
- L4:158 weakly suppresses
- L3:231 weakly excites
- L1:1962 weakly suppresses
- L0:1390 weakly excites
Feeds later layers
- L16:3030 excites
- L11:448 weakly suppresses
- L25:243 weakly excites
- L6:746 weakly suppresses
- L11:1973 weakly excites
- L19:1156 weakly suppresses
- L11:946 weakly excites
- L12:1828 weakly suppresses
- L9:1068 weakly suppresses
- L16:1783 weakly suppresses
- L9:2028 weakly excites
- L15:3028 weakly excites
- L15:2507 weakly excites
- L12:2969 weakly excites
- L10:2163 weakly excites
- L18:252 weakly excites
- L6:1746 weakly excites
- L8:1694 weakly excites
- L17:2770 weakly excites
- L12:411 weakly excites
- L9:2488 weakly excites
- L9:56 weakly suppresses
- L22:2799 weakly excites
- L20:2367 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
- 時間を
- OMIT
- tras
- سر
- .SuspendLayout
- AN
- enough
- ANTI
- ству
- time
Suppresses
- íst
- inox
- ired
- zioni
- .yahoo
- iring
- ışı
- acha
- 买到
- inary