'transistor' and electronics terms
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. 3 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 responds to electronics words, especially the piece 'ist' inside 'transistors' and names like 'Kirchhoff'. It is strongly pushed negative by 'transistors', so that word gives the largest negative values. When turned up, the text drifted, but turning it down produced text about circuits and transistors, which fits the theme.
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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With a little bit of patience and dedication, you'll soon start to recognize the patterns and symbols and become an expert in no time.⏎A Brief On Kirchhoff S Laws With Circuit Diagram⏎Circuit Topology And Laws Applied Electricity⏎S
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works but as the question asked for a simple answer I'll skip the math, google Maxwell's Equations and how they are used in the derivation of Kirchhoff's voltage law.⏎Batteries do make a good example for this simply because
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node numbers:⏎The two large-value resistors Rbogus1 and Rbogus1 are connected across the capacitors to provide a DC path to ground in order that SPICE<|endoftext|>Writing essays The essay writing sentence
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efficient strategies for using renewable sources of electricity to produce energy-dense liquid fuels from carbon dioxide (CO2) and biomass. Researchers will develop new families of electrocatalysts for the electrohydrogenation of CO
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gardens near forests or villages all over Maharashtra state, he advises to those who are eager to see Mumbai butterflies to go look for them in Maharashtra Nature Park, Sanjay Gandhi National Park, Yeoor Hills in Thane or in the Vasai fort
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to liquid fuels is a critical frontier in the transition to a renewable energy economy. One potentially game-changing technique is electrohydrogenation – the use of an electrocatalyst to drive the chemical reaction between a molecule and hydrogen.⏎The goal of
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: a pnp in the configuration. The current has a high resistance (R_H) and the gate of the transistor is connected to the ground. The gate of the transistor is connected to the ground. The gate of the transistor is connected to
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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chip performance—measured by the number of transistors on a chip—doubles every 18 months.⏎It is no
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Livermore National Laboratory researchers said their new tool enhances the way computer chips, LEDs and transistors are build.Commonly used piezo-electric speakers, such as those found
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Class of products known as thermionic valves, or ‘tubes’. Valves were relegated to specialist applications once the semiconductor transistor came to dominate. Compared to transistors, valves were oversized, generated too much heat and required multiple high-voltage
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a circuit.⏎From Tubes to Transistors⏎Basic computers that could do the same thing as the microprocessor have been
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
- L0:178 suppresses
- L0:1224 suppresses
- L0:1301 suppresses
- L1:2801 excites
- L2:2328 weakly suppresses
- L2:75 weakly suppresses
- L2:41 weakly suppresses
- L2:111 weakly suppresses
- L1:394 weakly excites
- L2:72 weakly suppresses
- L2:2698 weakly excites
- L2:484 weakly suppresses
- L0:2412 weakly suppresses
- L2:70 weakly excites
- L2:1657 weakly excites
- L2:2620 weakly excites
- L2:94 weakly suppresses
- L2:2757 weakly suppresses
- L2:1494 weakly suppresses
- L2:1321 weakly suppresses
- L2:1710 weakly suppresses
- L2:126 weakly excites
- L1:1272 weakly excites
- L1:2683 weakly excites
Feeds later layers
- L7:2233 suppresses
- L5:1818 suppresses
- L4:2318 weakly suppresses
- L7:1439 weakly suppresses
- L4:2 weakly suppresses
- L25:1442 weakly suppresses
- L19:245 weakly suppresses
- L10:1713 weakly suppresses
- L8:2736 weakly excites
- L6:1582 weakly suppresses
- L15:2173 weakly suppresses
- L6:434 weakly suppresses
- L5:2042 weakly suppresses
- L8:2474 weakly excites
- L20:2655 weakly suppresses
- L10:1787 weakly excites
- L22:2645 weakly suppresses
- L11:1957 weakly excites
- L8:2129 weakly suppresses
- L8:1781 weakly excites
- L16:2137 weakly excites
- L23:2116 weakly excites
- L7:2907 weakly suppresses
- L8:3044 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
- ssp
- '})⏎⏎
- nak
- ness
- '$
- ⏎⏎
- specials
- '))⏎⏎
- '|
- 拉斯
Suppresses
- setActive
- /MIT
- ürger
- 首富
- TextInput
- enstein
- roi
- 奥巴
- başı
- 饯