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 directly using when generating tokens, namely the logits of all tokens!”
What makes me hopeful about this release is that possibly this same conversion can be applied to other open models and we might see a bunch of diffusion versions of existing local models. It’s exciting stuff!
Appealing results... do we think there is scope to close the accuracy gap against AR models? or even leverage the "Bidirectional Reasoning and Self-Correction" into an overall advantage?
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.
Diffusion text models for me is the more interesting type of LLMs for local usage, as it really makes good use of single GPUs for single responses, rather than auto-regressive ones, and is a lot faster! Probably the fastest model I've been able to run so far, ending up doing ~670 tok/s (depending on the type of text) on a Pro 6000
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 directly using when generating tokens, namely the logits of all tokens!”
What makes me hopeful about this release is that possibly this same conversion can be applied to other open models and we might see a bunch of diffusion versions of existing local models. It’s exciting stuff!
Appealing results... do we think there is scope to close the accuracy gap against AR models? or even leverage the "Bidirectional Reasoning and Self-Correction" into an overall advantage?
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.
DiffusionGemma goes one step further even, and does this denoising over multiple "canvases" which lets it do reasoning and separate out a "final reply" canvas, looks something like this: https://gist.github.com/embedding-shapes/f4cb46bad704b6d0168...
Diffusion text models for me is the more interesting type of LLMs for local usage, as it really makes good use of single GPUs for single responses, rather than auto-regressive ones, and is a lot faster! Probably the fastest model I've been able to run so far, ending up doing ~670 tok/s (depending on the type of text) on a Pro 6000
How does that break your brain? It's how basically every human writes and iterates on text..?
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?
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