# Hugging Face — Palette: teaching a small model to build decks in your design language

- Company: Hugging Face (huggingface.co)
- Announced: 2026-11-03T16:35:36+00:00
- Category: new-model
- Coverage: not counted
- Announcement: yes
- Group: models
- Source: https://huggingface.co/blog/ibm-research/palette-deck-building
- Record: https://forck.live/items/31088-palette-teaching-a-small-model-to-build-decks-in-your-design-language
- Subject: Platform
- Open weights: yes

IBM's research team trained Palette, a 20B open-weight model that builds slide decks in an organisation's own design language, splitting the work into a designer that writes a structured brief and a coder that draws each slide. Small self-hosted models usually produce broken slides: titles that vanish into the background, text running off the slide, shapes stacked on top of each other. Palette was trained with a LoRA adapter rather than full fine-tuning, and its training data follows the IBM Design Language. On PresentBench it ranks first on two of the three domains the team measured. The training data is synthetic: roughly 1000 decks and about 10,000 slides, generated with a large open teacher from recipes that set genre, length and layouts, and each example carries counterfactual reasoning for why an alternative was rejected. Every deck is rendered and checked for overlaps and overflow by the same checker used at inference, and broken slides are repaired with their reasoning rewritten, so each caught mistake becomes a training example. Because the designer fixes every slide's context before any code is written, the coder calls run in parallel, making the coding stage about 6–7× faster.

## Evidence

Verbatim from https://huggingface.co/blog/ibm-research/palette-deck-building:

> 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 build decks the way people do: a designer plans the deck and a coder draws each slide.

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Record: https://forck.live/items/31088-palette-teaching-a-small-model-to-build-decks-in-your-design-language
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