end-of-text marker between documents
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. 19 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 fires on the invisible end-of-text marker that appears where one web page abruptly joins the next. It is most negative on code and markup symbols such as ";", "(" and HTTP header words. Steering produced ordinary text with no clear link to the pattern.
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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’t embark on any new diet restrictions without a doctor’s supervision if you are ill, pregnant or breastfeeding, or if you know that you have serious allergies.⏎Basically<|endoftext|>All scientific knowledge is uncertain. This experience with doubt and uncertainty is important. I
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's global environment for promoting peace and non-violence among mankind."<|endoftext|>Processing nodes in a graph one at a time, usually in some specified order.
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$50 from the mint."<|endoftext|>In the next of our blog posts for Census week, we showcase recent research undertaken
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historical data will only make sense when used in a chosen perspective by a given scholar.⏎Conversely, any scholar working on a given topic will find far greater<|endoftext|>Fifth Street Southeast Historic District⏎Boundaries:
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The length of a day normally changes anyway by<|endoftext|>A long time ago I wrote the article The Dull Case of Emissivity
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opportunity that would help sustain the museum long-term.⏎<|endoftext|>“A 200:1 ratio is very difficult to overcome,” said D
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 problem of a long-term-mid-mid-month-ten-day-completion-tweet-mastom-mid-month-tweet- But I'm a bit stuck. Let's break it into a more-organized answer-aid,
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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<polygon points="200,10 250,190 160,210" style="fill:lime;stroke:yellow;stroke-width:1"/> </svg>⏎Here is what the
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an exception, which is life as usual for a Python programmer.⏎if not server_response['successful']:⏎etag = server_response['file_handle'].info().getheader('ETag')⏎xml_document = server_response['xml_document']<|endoftext|>Deepak V
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406 Not Acceptable response because only German and English versions of the document are available.⏎GET /news HTTP/1.1 Host: www.example.ai Accept-Language: fr⏎HTTP/1.1 <|endoftext|>The move to electronic health
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When the 406 Not Acceptable error message is received, it is in response to an HTTP request that includes a negation-related header such as Accept or Accept-Language. When the client includes one or more of these headers, the server
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
- L16:2053 excites
- L6:1211 excites
- L11:1556 weakly excites
- L10:1424 weakly excites
- L6:2724 weakly excites
- L10:2687 weakly suppresses
- L8:1118 weakly excites
- L10:1724 weakly suppresses
- L17:2525 weakly suppresses
- L5:2123 weakly excites
- L7:162 weakly suppresses
- L9:2647 weakly suppresses
- L16:2911 weakly excites
- L11:189 weakly excites
- L18:458 weakly suppresses
- L17:61 weakly suppresses
- L11:1326 weakly excites
- L5:197 weakly excites
- L13:2744 weakly suppresses
- L18:894 weakly excites
- L1:724 weakly excites
- L4:2324 weakly excites
- L7:36 weakly suppresses
- L18:2839 weakly excites
Feeds later layers
- L25:389 suppresses
- L20:1571 suppresses
- L21:2430 weakly suppresses
- L21:1267 weakly excites
- L21:2975 weakly suppresses
- L21:981 weakly excites
- L23:2754 weakly excites
- L20:292 weakly excites
- L21:763 weakly excites
- L23:2700 weakly excites
- L20:2913 weakly excites
- L22:2903 weakly suppresses
- L21:1869 weakly suppresses
- L21:3064 weakly suppresses
- L24:1328 weakly excites
- L24:967 weakly suppresses
- L22:2660 weakly suppresses
- L25:1589 weakly excites
- L22:2299 weakly excites
- L20:327 weakly excites
- L20:404 weakly excites
- L20:3019 weakly suppresses
- L21:781 weakly excites
- L20:991 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
- reads
- Fun
- reads
- .Next
- patible
- BEST
- asks
- pops
- getenv
- Fun
Suppresses
- -margin
- MediaTek
- ament
- -addon
- 反而
- -column
- principalColumn
- URY
- continue
- marginTop