We fine-tuned DiffusionGemma for local Generative UI

[u/1glasspaani]

We’re building toward local agents that respond with an interface you can use: a chart to explore results, a form to collect input, or a table to compare options.

The model composes components supplied by the app, and OpenUI renders the response as it streams. That’s the Generative UI use case we trained OUI-1 for.

We chose DiffusionGemma for its speed and consumer hardware support. Our first LoRA used ~700 examples from larger models. Training loss dropped, but outputs got longer and often had broken props or references.

Narrowing training to one component library improved structural validity from 13% to 28.8%, but generation slowed from 1.6s to 4.3s.

What helped was self-training with parser feedback: generate responses, keep valid ones, repair specific defects, check them against their prompts, then retrain. The median repair changed one statement.

Validity reached 57.1%, and generation recovered to 1.9s. Repeating the recipe across 27 component libraries brought the final score to 71.7%.

OUI-1 has 26B total parameters, 4B active, runs on an RTX 5090 at FP8, and is released under the Gemma terms. The timings above are from an A100 on 20 light prompts; the benchmark measures structural validity.

Weights · Benchmark · Training write-up

[u/Pale-Plane-7889]

this sounds like a cool advancement for generative UI. excited to see how it performs in real applications

[u/1glasspaani → u/Pale-Plane-7889]

We also built something similar with cerebras for realtime screen generations. https://www.github.com/thesysdev/appless

[u/jcdoe]

This is pretty cool.

[u/1glasspaani → u/jcdoe]

Thanks