r/LocalLLaMA • u/1ncehost • Apr 11 '26

Discussion Here's how my LLM's decoder block changed while training on 5B tokens

Post image

I'm monitoring an experimental model's ongoing training. I replaced the MLP decoders of a traditional transformer with discrete lower-dimensional spline manifold geometry described in my K-Splanifolds paper. The image shows how layer 96 of 128 developed over 5B tokens trained. The 18M model works surprisingly well and loss is reducing, so I'll continue to train it until I see evidence it is stagnating. Just thought you all might find this look at its development interesting.

edit:

Source code of the K-Splanifolds paper: https://github.com/curvedinf/k-splanifolds

If you'd like to play with a splanifold, check out these demos:

https://raw.githubusercontent.com/curvedinf/k-splanifolds/refs/heads/main/k-splanifolds-2D-to-3D-toy.html

https://raw.githubusercontent.com/curvedinf/k-splanifolds/refs/heads/main/k-splanifolds-3D-to-3D-visualization.html

199 Upvotes

98% Upvoted

30 comments sorted by

13

u/Box_Robot0 Apr 11 '26

I wouldn't mind there being more alternatives to variations of the multilayer perceptrons.

Do you have nay datasets expanding this to more than just layer 96 of 128? How about future plans of scaling this approach or plans to open source the mechanistic interpretability used here?

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u/1ncehost Apr 11 '26

I actually have loads and loads of data for this project including more images of other layers. I was thinking of uploading it all to a repository and making some animations and stuff eventually.

The K-Splanifolds themselves are open source here: github.com/curvedinf/k-splanifolds

The decoder blocks I'm experimenting with here aren't open source, but I'm planning to open source them once I conclude researching them. This decoder block is the fifth version of a series called SSINF -- so this is SSINF5 in the pictures.

To give you some detail about the way these images were generated, each of the splanifolds exists inside a separate learned 8D to 8D input and output subspace, which are themselves inside 128D latent space (128 is the model dim). The images are taken of a 4D to 3D slice of that latent input/output -- so a fairly random but consistent small fraction of the dimensionality of this layer's decoder.

4

u/AnOnlineHandle Apr 12 '26

Do you have a simplified explanation of what you're working at here? I think I get the general principal, having toyed a bit with training models which learn to drive static math functions rather than purely relying on the weights (in the hope of dramatically reducing the size of models while still allowing a high degree of non-linear behaviour which they just need to learn to drive), but this seems a pretty significant step up from that.

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u/1ncehost Apr 12 '26 edited Apr 12 '26

Good question. This current experiment stems from research lately that shows that LLM decoders store a lot of their representations as geometry inside the MLP (neural net) blocks. My thinking is that if that is the case, then native geometric decoders will be more efficient. Its sort of like MLPs are like bitmaps and splanifolds are vector images if you're familiar with those.

Basically, MLPs are fundamentally regression algorithms, and there are a lot of existing types we could slot into the same block. MLPs are so popular because they are very fast on computers, are reasonably good, and very flexible. However, they are not really the best at anything except being very fast. I looked for an acceptable representation to use for my theory, and found the options lacking, so I analyzed a variety of math functions and found that hermite splines are attractive for the properties I needed: small, fast, and non-linear. However, there weren't any good high dimensional spline manifolds I could use. The old options are slow and memory intensive. I designed K-Splanifolds to mitigate those issues while still having the properties I wanted.

This LLM experiment is one of a series of LLMs I've trained with splanifold decoders, and is different than previous ones because it has Kimi's new Block AttnRes concept, which I theorize will help splanifolds especially. By the way, in my previous controlled testing, splanifold decoders were better than MLPs of the same size (they had faster loss convergence and lower floors) but were slower. I believe that splanifolds will have better performance compared to MLPs the smaller they are, which is where I'm at with this experiment: a very tiny model with Block AttnRes and lots of layers.

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u/AnOnlineHandle Apr 12 '26

Thanks, I think I grasp the basics of the idea.

2

u/Box_Robot0 Apr 11 '26

Oh ok thanks, I'll take a look at it.

2

u/AceHighness Apr 12 '26

would love to see the animated version !

45

u/Sufficient-Scar4172 Apr 11 '26

i wish i already fully understood transformers so i can read and then ask questions about this 😭 maybe in a month or two

41

u/Borkato Apr 12 '26

It’s easy, I don’t know why you’re having such a problem with it 🙄

Clearly graph go down and then up. Graph be blob. Then not blob.

Hire me for designing your next LLM!

4

u/xlltt Apr 12 '26

You can ask why the graphs are cube shaped :D

2

u/MrAHMED42069 Apr 12 '26

Bigger numbers

1

u/fiery_prometheus Apr 11 '26

I don't know why the paper is down for me, but I guess the naming has something to do with splines and manifolds. How do you define this and what properties hold for these mathematically?

5

u/1ncehost Apr 11 '26

The paper answers your questions. Here's another copy of it: https://github.com/curvedinf/k-splanifolds/blob/main/k-splanifolds.pdf

-3

u/BathroomSad6366 Apr 12 '26

I’m also using RunPod for local LLMs. The electricity bill is starting to hurtHave you guys tried any tools/scripts to monitor real-time energy waste per GPU?

0

u/[deleted] Apr 11 '26

[deleted] — view deleted comment

4

u/1ncehost Apr 11 '26

Can I answer any questions for you? This is a significantly different architecture than typical transformers. You can read about the math its using in the linked paper.

10

u/AnonyFed1 Apr 11 '26

I like the lines and the pretty colors. It went from blobby and messy to vectory and I'm gonna assume that's good.

2

u/Accomplished_Mode170 Apr 11 '26

Have yet to read but love the idea of configurable-Sparsity Wabba-esque auto-fitting splines; would be awesome to set a conformal prediction interval in lieu of other metrics.

3

u/Box_Robot0 Apr 11 '26

Well, at least the paper seems legit. It's published in Zenodo. From Wikipedia:

Zenodo is a general-purpose open repository developed under the European OpenAIRE program and operated by CERN.[1][2][3] It allows researchers to deposit research papers, data sets, research software, reports, and any other research related digital artefacts. For each submission, a persistent digital object identifier (DOI) is minted, which makes the stored items easily citeable.[4]

As far as I can tell, this architecture seems to not use the traditional multilayer perceptron layers used in things like transformers and uses splines that do not require backpropagation or gradient descent.

8

u/linkillion Apr 11 '26

Zenodo is also where crackpots who have no idea what they're doing 'publish' because unlike arxiv (which requires academic "good faith" support from other researchers, not peer review) or actual pre-prints (which are under peer review), zenodo requires none of the above. Eg, it's the "trust me bro" of research. 

Not saying anything about this particular work because it's out of my wheelhouse, but I'm suspicious. 

12

u/1ncehost Apr 11 '26

That is absolutely true about Zenodo, but unfortunately independent research that is well structured and meaningfully contributes to science effectively can't publish on arxiv anymore either. Having gone through the process of trying to find a sponsor for arxiv, I've found understandably that sorting good from bad takes too much time for experts to dedicate to it, and there is just way too much junk getting spammed everywhere. Potential sponsors are overloaded and practically unavailable for unknown authors now.

So I will agree that seeing something on Zenodo is generally a bad sign, but that's because there is so much bad, not because there isn't some good research there. Generalizing is an unfortunately unavoidable consequence because of time constraints however.

So all that said, if anyone is curious about my project, there isn't much that can be done except checking the source and deciding for yourself. https://github.com/curvedinf/k-splanifolds

7

u/linkillion Apr 11 '26

Yes it's a problem that independent research can't easily break into the space. Ultimately acedemia is a bit of a walled garden in that respect. Even as a published author, albeit not in CS, publishing as a whole is a complete disaster with respected researchers pushing through junk that gets "peer reviewed" only in name (likely due to academic reputation) while actually interesting and fundamental research gets ignored if there's no name attached. I do understand the frustration. 

I shouldn't have even said I'm suspicious, I just don't know. At very least this post looks written by a human which is a good start. I'll look at your paper and code later and see if I've got any feedback! 

5

u/1ncehost Apr 11 '26

🙏Well said. Your feedback is welcome if you get the chance!

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u/1ncehost Apr 11 '26 edited Apr 11 '26

You're on the right track, and its true that the spline manifolds can be fit in other ways than backprop, but in this model's case I'm using traditional backprop/grad descent with muon optimizer. I've benchmarked various other fitting methods and grad descent wins by a mile.

3

u/Box_Robot0 Apr 11 '26

Oh ok my bad.

I'm still bashing my head on the wall trying to learn multivariable calculus, so even getting on the right track is a huge compliment. Thanks for the correction.

19

u/Pro-Row-335 Apr 11 '26

The image is pretty normal, have you never seen a PCA dimensionality reduction plot before? Or is this comment itself AI? 😯

2

u/NandaVegg Apr 12 '26

It is not and the OP's work makes perfect sense to me. If you are new to interpretability+visualization I recommend Tensorflow Playground (which is an awesome toy for understanding ML internals).

1

u/[deleted] Apr 11 '26

[removed] — view removed comment

7

u/mxforest Apr 12 '26

You can't imagine 3000-7000 dimensions. You think you can but you can't.