unclear: mixed, often before a number
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. 20 of Qwen3-0.6B. Its strongest snippets are fairly consistent: several of them 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 a grab bag of spots, many of them just before a number or a short word that completes the sentence, such as 'length -1' or 'value is 1'. Its negative side fires on method calls like '.info()'. Turning it up did not produce a clear theme.
Written by Claude from this neuron's activations and steering outputs. Confidence: low. 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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elements of the array in reverse, we need to use a loop that will iterate from length - 1 to 0. We can use a for loop or a while loop to
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far:⏎Originally Posted by MrFujin⏎Enter a 5-charcter string: hgout⏎String#1: tough ; ASCII code decremented by 32⏎String#2: HGUOT ; reverse order⏎mov char
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3, 5, 8, 13, 21, 34, 55… each number is the sum ( + ) of its two previous numbers. every fold in our ruler was folded on the crease
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sequence. It is not optional.⏎- step is the step value or difference between each number in the sequence. It is optional and by default it’s value is 1.⏎If we want to iterate an array in reverse order, we need to
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mg/kg, 5 times daily for 5 days.⏎Best of luck---⏎Search for questions⏎Still looking for answers? Try searching for what you seek or ask your own question.⏎Posted 31 Aug 2009 •
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that these files are not on the IDEAS site. Please be patient as the files may be large.⏎As the access to this document is restricted, you may want to look for
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: [12, [2, [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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there, I get 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']
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deciding on a treatment plan and a prognosis.⏎The following are prognostic factors for bile duct cancer.⏎Removal of the tumour with surgery @(Model.HeadingTag)>⏎Complete removal of the tumour with surgery offers the best chance for long
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, instead of writing Users.Current.User.Name, you can just write Users.Current.Name.⏎I’ll grant you that this doesn’t seem much for the User
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stages (they have spread into nearby lymph nodes or organs far from the bile ducts) have a less favourable prognosis.⏎Location of the tumour @(Model.HeadingTag)>⏎Tumours in the distal 3rd of the bile duct
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
- L9:156 weakly suppresses
- L19:1537 weakly suppresses
- L11:769 weakly excites
- L7:657 weakly excites
- L16:1592 weakly suppresses
- L14:2191 weakly excites
- L4:2461 weakly excites
- L16:1642 weakly suppresses
- L1:2453 weakly excites
- L10:1588 weakly excites
- L19:540 weakly excites
- L19:1598 weakly excites comma before a qualifying clause
- L11:36 weakly excites
- L13:856 weakly suppresses
- L6:2257 weakly excites
- L19:1596 weakly suppresses
- L3:2379 weakly excites
- L7:666 weakly suppresses
- L2:525 weakly suppresses
- L2:2948 weakly suppresses
- L5:1295 weakly suppresses
- L18:701 weakly excites
- L13:459 weakly excites
- L16:2206 weakly excites
Feeds later layers
- L21:811 strongly suppresses
- L25:478 suppresses
- L27:772 suppresses
- L25:1700 suppresses
- L26:232 suppresses
- L24:2100 suppresses
- L27:615 suppresses
- L22:636 suppresses
- L27:730 suppresses
- L23:1018 suppresses
- L26:457 weakly suppresses
- L23:293 weakly suppresses
- L22:2609 weakly excites
- L22:1470 weakly suppresses
- L26:326 weakly excites
- L27:315 weakly suppresses
- L23:540 weakly suppresses
- L23:2952 weakly excites
- L21:2979 weakly excites
- L24:721 weakly excites
- L27:1804 weakly suppresses
- L22:495 weakly excites
- L25:2793 weakly excites
- L27:524 weakly suppresses
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
- angling
- odiac
- rends
- esty
- ignant
- ://'
- iments
- chap
- ANDLE
- ตร
Suppresses
- ["
- ['
- ["
- [i
- [k
- [n
- [-
- [d
- ["_
- [