Palette achieves professional slide creation by dividing tasks between designer and coder roles unified under a single LoRA adapter. The designer first establishes an editorial JSON brief specifying layout, typography, and colors, after which the coder generates pptxgenjs code for slide geometry. Because slide parameters are established early, the coder executes slide production in parallel to speed up the coding process by six to seven times. The model demonstrated its efficacy by winning two out of three domains evaluated on the PresentBench benchmark.
Key points
Enterprises can avoid privacy risks and generic frontier-model designs by adopting specia…: Enterprises can avoid privacy risks and generic frontier-model designs by adopting specialized, self-hosted open-weight systems.
Splitting slide generation into separate designer and coder roles prevents visual overlap…: Splitting slide generation into separate designer and coder roles prevents visual overlaps and text overflow issues.
Generating slide contexts prior to coding enables parallel processing that accelerates ou…: Generating slide contexts prior to coding enables parallel processing that accelerates output speeds six to seven times.
Sources checked by our analysis pipeline: 1
Ask a frontier model for a slide deck and you'll get a good one. Ask for ten and they start to look alike: the same tasteful defaults, the same house style, a faint "AI-generated deck" look. That's fine until you want the deck to look like your template. Frontier models have their own house style, and no amount of prompting stops them drifting back to it. And sometimes the content is confidential and can't leave your systems at all.
Both of those point to a model you own: open weights, running on your own infrastructure, trained on your own design language. The catch is that small open models, the kind you'd actually self-host, are bad at this. Their decks come out broken: titles that vanish into the background, text running off the slide, shapes stacked on top of each other.
Palette tests one bet: that a small open-weight model, trained on synthetic data in your design language and checked by a harness, can compete with frontier systems while you keep ownership of the result. We fine-tuned a 20B model to…
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