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Just wanted to share this, I found it was a really nice resource to understand how diffusion Gemma worked: https://newsletter.maartengrootendorst.com/p/a-visual-guide-... The really interesting thing to me was that they didn’t need to train this model from scratch they just used their existing MOE checkpoint: “To convert a decoder-only model (Gemma 4 26B A4B) into a denoiser, we can make use of something it is not di…
there's still JEPA to be integrated before AGI. Would DiffusionGemma be suitable candidate for DFlash 2?
its more of a competitive approach to improve compute utilisation at lower batch sizes
I like the model a lot and it's fairly good at reasoning. You can also really bend it to your needs. It's designed for machines with more compute than memory bandwidth but IMO does really well on metal.
I've got it up to ~15tok/s on M3-class machines, but I wager there's a bunch of perf on M5 that I just don't have hardware access to unlock.
I tried to implement MTP using the other Gemma MTP heads but I failed to move that perf needle. There's some interesting research to be done about pre-seeding the diffusion canvas from draft models. DiffusionGemma with the right drafter can hit 20-30 tok/s on my machine, but I've been unable to combine the two together to make it faster than what it's been running at so far.
there's still JEPA to be integrated before AGI. Would DiffusionGemma be suitable candidate for DFlash 2?
I re-implemented this one for macOS over the last couple of months: https://github.com/mmastrac/diffgemma I like the model a lot and it's fairly good at reasoning. You can also really bend it to your needs. It's designed for machines with more compute than memory bandwidth but IMO does really well on metal. I've got it up to ~15tok/s on M3-class machines, but I wager there's a bunch of perf on M5 that I just don't ha…
About the drafting/MTP, how would that work? As I understand, MTP/drafting is mostly useful for autoregressive models, not for a diffusion model, because each canvas (in the case of DiffusionGemma at least) is fast enough, what you care about is the "convergence" time before it figures out that the accuracy is good enough to move on to another canvas. This is my understanding at least. Would this drafting for a diffusion model like this mean you'd generate multiple canvas at the same time? That'd also seem strange, as then you'll cut performance for generating a complete canvas... I'm genuinely curious about how it could be applied to DiffusionGemma though, so please do expand :)
If your model can reason and write code at 1500 toks/sec, then you should end up totally bottlenecked on CPU time the entire time a prompt is active. If you aren't, then you're losing wall time versus competitors. But our whole development stack is based around the idea that programmers spend most of their time thinking, talking and coding, not waiting for the CPU (melting CI clusters being a painful exception to that).
What I'm imagining here is some sort of hybrid mode in which compiling code and running it through an interpreter can be overlapped, so an LLM can propose a change and immediately begin running unit tests while type errors that might affect some other module are found in parallel. And the tests would always run sharded, potentially on a remote cluster, even in local dev.
Obviously this approach is to some extent what made the JVM popular. Java compiles very fast because javac does little more than type checking, and the type system is simple. Then the JVM does the heavy lifting of compilation in parallel with it running. So although Java has a reputation for poor startup times, turnaround times for the JVM can be really excellent compared to something like C++, Swift or Rust. And a lot of startup time pain is just poor frameworks like old Springs that want to reflectively scan the app's files and do other inefficient stuff. More modern frameworks push more to incremental build tasks and can get startup down to very little, But it feels like this approach should be pushed much further. The model should spend all its time waiting on unit tests to run.
Earlier quoted context omitted.
Because my brain thinks through text in a forward motion. Pausing at the end of each word and searching for the next. My entire brain runs on sentences and words since I have no inner eye or whatever. So my thinking and writing both work kind of forward only. I wouldn’t have thought that was too unique. But maybe it is?
Normally people have feelings about things before they are able to put them into words, I would imagine if you were asked a question like "what city would you most like to visit" then unless you've already thought about it a lot, then you would have to do substantial non-verbal thinking before you can come up with an answer, and once you have the answer you may respond "my favorite city is X" and you decided what X w…
If I'm asked a hard question, like what city I'd most like to visit, I kind of have to look up cities and cross reference them with ones I've noted that I want to visit, or have visited and liked enough to go back, then actually read the list, and figure out which one, but each time I read a city, I might have a, this would be great in summer or winter or they have a cool festival in June. Which leads to additional question I have to ask. Which I think about roughly the same speed I speak, maybe a word or two faster.
My wife is the complete opposite. She can put together a sentence in an instant in "parallel" so when she says it, she though it already, but not as a sentence.
Human brains all work so crazily weird, and we are only just now started to realize all the nuances.
Just wanted to share this, I found it was a really nice resource to understand how diffusion Gemma worked: https://newsletter.maartengrootendorst.com/p/a-visual-guide-... The really interesting thing to me was that they didn’t need to train this model from scratch they just used their existing MOE checkpoint: “To convert a decoder-only model (Gemma 4 26B A4B) into a denoiser, we can make use of something it is not di…
If these models get good at coding it's going to force a rethink of how languages, compilers and test suite runners work. "AI changes everything" is a cliché by this point but I think it's actually true. If your model can reason and write code at 1500 toks/sec, then you should end up totally bottlenecked on CPU time the entire time a prompt is active. If you aren't, then you're losing wall time versus competitors. Bu…
I'm very interested in Diffusion text models. The concept of taking noise and adding words starting randomly all over the response, and filling in the noise from there on breaks my brain. I'm sure I have a fundamental misunderstanding of the technology, though.
Simply predicting the surface of the data you are trying to generate is far more representationally efficient, and perhaps in the next few months we'll see a version of that for LLMs