weight loss and body fat 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. 10 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
The neuron fires on words about losing weight or body fat, such as 'lose weight', 'excess body fat' and 'reduce weight'. Its negative side fires on wording in technical documentation, such as 'this variable identifies' or 'property allows'. Turning it up did not produce a clear diet theme in the output.
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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to run continuously.⏎Reduce indoor air pollution sources such as smoking, burning incense, and frying foods.⏎If travelling in a car with air conditioning, keep the windows up and the ventilation set to recirculate.⏎If you are very sensitive to
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diet, it is also important to get proper amounts of vitamins, minerals and other nutrients. It is also important to avoid fat when you are trying to shed excess fat from your body. The reason being is that fat contains more calories than protein.
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the higher your body fat content (calculate your body fat now), the higher the concentration of leptin in
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uncertain what aspect of chocolate promotes slender physiques, but suspect it is caused by catechins. These compounds have been shown to improve lean muscle mass and reduce weight in mice. Chocolate also contains caffeine, which can boost metabolism. This research,
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beneficial in many aspects.⏎One of the best benefits to your daily lives is a natural fat-burning properties. Almonds are used, of course, to lose weight, and the added benefit of a good fiber content is incredible. They are rich
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to maintain their current weight is to eat a well balanced diet composed of a variety of foods. To lose weight, your body needs a variety of nutrients. This can be accomplished by eating a
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: "I have a few days of work experience in the field of education. This is a small part of the education system. I need to add a new section in the educational system. So, to be specific, I think the
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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(CANBERRA TIME) 24/04/2001⏎|Page tools: Print Page RSS Search this Product|⏎This variable identifies holders of Australian citizenship.⏎Citizenship data are used to obtain information on the
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an area of interest via GetText.⏎Sometimes you may wish to be more selective about whether text⏎is returned or not. The ClippedTextTolerance property allows you to⏎define a rectangular area in the center of the character, the⏎
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the start point of the sequence. By default it is 0 and this is an optional value.⏎- stop is the stop point of the sequence. It is not included in the sequence. It
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constant space.⏎In an imperative language (here Modula-3) the same would be achieved with the following code:⏎TYPE List = REF RECORD next: List; value: INTEGER; END;⏎VAR x := NEW(List, value:=1);
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
- L8:943 suppresses
- L9:1583 suppresses
- L9:566 excites
- L7:1325 suppresses
- L7:177 excites
- L6:623 suppresses
- L7:739 suppresses
- L9:1951 suppresses
- L6:288 excites
- L6:572 weakly excites
- L6:2755 weakly suppresses
- L8:300 weakly suppresses
- L7:1082 weakly suppresses
- L8:1871 weakly suppresses
- L7:525 weakly suppresses
- L9:2080 weakly excites
- L8:1782 weakly suppresses
- L9:1926 weakly excites
- L9:1786 weakly excites
- L9:1337 weakly suppresses
- L9:112 weakly excites
- L9:801 weakly excites
- L6:1309 weakly excites
- L7:504 weakly suppresses
Feeds later layers
- L11:960 excites
- L16:2370 suppresses
- L11:2382 suppresses
- L12:1291 suppresses
- L11:781 suppresses
- L12:660 suppresses
- L11:882 excites
- L12:350 suppresses
- L17:2565 suppresses
- L12:1339 suppresses
- L12:886 weakly suppresses
- L11:935 weakly excites
- L12:1413 weakly excites
- L15:2368 weakly suppresses
- L14:265 weakly suppresses
- L13:1299 weakly suppresses
- L14:972 weakly suppresses
- L22:2311 weakly excites
- L12:2201 weakly suppresses
- L13:2403 weakly suppresses
- L12:1541 weakly suppresses
- L11:515 weakly excites 'hands' in hand-washing advice
- L11:1733 weakly excites
- L12:1478 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
- ubs
- icular
- iples
- NotSupportedException
- /cms
- ียว
- Forms
- bruises
- iku
- ยว
Suppresses
- chie
- 道
- che
- ElapsedTime
- 객
- leading
- OwnerId
- descent
- heiro
- spiel