programming languages and software names
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. 1 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 the names of programming languages, number crunching tools and software, such as NumPy, Octave, Clojure, R and GIMP. Turning it up made the model's writing drift into repetitive, rambling text rather than anything about code.
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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programming language for statistics computing. These tools, especially their open source packages, have gained popularity among programmers in scientific and statistic computing. A fundamental component in both NumPy/SciPy and R are the linear algebra functions, which rely on the standard
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time.⏎You should give examples from dynamic typed languages instead, e.g. clojure.⏎Here's one example:<|endoftext|>The commerce of the ancient world was gathered into
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language. Octave has extensive tools for solving common numerical linear algebra problems, finding the roots of nonlinear equations, functions written in Octave's own language, or by using dynamically loaded modules written in C, C++,
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then move into more advanced topics such as neural networks and tree-based methods.⏎Finally, you’ll delve into the frontier of machine learning, using the caret package in R. Once you develop a familiarity with topics such as the difference between regression and classification
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will give a quick tutorial on how to extend NumPy/SciPy and R by writing a wrapper for an Intel MKL function and then use it from NumPy/SciPy or R programs.<|endoftext|>by Roman Davidov⏎(revisited from
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|⏎|Required to build||libstdc++|⏎|Required to build||fortran|⏎This entry (in part or in whole) was last reviewed on 4
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:1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.
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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Line using Visual C++ 2008⏎- Select Start >> All Programs >> Microsoft Visual C++ 2008 Express Edition >> Visual Studio Tools >> Visual Studio 2008 Command Prompt (Run with administrator privileges)⏎
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by DomainKeys Identified Mail (DKIM), before adding this signature to any outgoing mail it is essential that you generate a domain key which will be utilized by Google Apps to produce encrypted mail headers which are distinctive to your domain. Further, the mail
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you in the DNS records for your domain.⏎If your domain already has a DMIK domain key, then another key has to be produced to use with Google Apps and the domain key for Google apps can be differentiated from other keys through selector prefix
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kinds of powerful, dynamic applications and services. For more information on this great topic, please consult:⏎- Sun's Java Server Pages home (http://java.sun.com/products/jsp/)⏎- Westy Rockwell's article on "JSP Tag
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
- L0:737 excites
- L0:1301 suppresses
- L0:9 suppresses
- L0:1384 suppresses
- L0:25 excites
- L0:178 suppresses
- L0:1224 suppresses
- L0:1420 excites
- L0:2041 excites
- L0:1658 excites
- L0:398 suppresses
- L0:2489 suppresses
- L0:882 suppresses
- L0:2906 suppresses
- L0:991 weakly excites
- L0:1559 weakly excites
- L0:834 weakly suppresses
- L0:413 weakly suppresses
- L0:2019 weakly suppresses
- L0:2163 weakly suppresses
- L0:1292 weakly suppresses
- L0:97 weakly excites
- L0:1046 weakly excites
- L0:321 weakly excites
Feeds later layers
- L2:232 excites
- L2:1289 excites
- L2:795 excites
- L14:462 weakly excites
- L2:862 weakly suppresses
- L2:260 weakly suppresses
- L2:2174 weakly suppresses
- L6:56 weakly excites
- L4:369 weakly excites
- L7:2866 weakly excites
- L2:127 weakly excites
- L17:3048 weakly suppresses
- L9:2808 weakly excites
- L2:691 weakly suppresses
- L2:1789 weakly suppresses
- L24:1908 weakly excites
- L2:29 weakly suppresses
- L10:407 weakly excites
- L5:893 weakly excites
- L2:367 weakly excites
- L2:1387 weakly excites
- L22:2273 weakly excites
- L21:2327 weakly excites
- L15:544 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
- wi
- offsets
- ERN
- 的重大
- slack
- 了自己的
- 为之
- scor
- awi
- owering
Suppresses
- :⏎⏎
- [ ⏎
- asin
- ?⏎⏎
- -plan
- { ⏎ ⏎
- ([ ⏎
- 磙
- ; ⏎ ⏎
- !⏎⏎