IBM Research introduces Palette: a small model for custom design language slide generation

IBM Research has unveiled Palette, a specialized approach for training small-scale open-weight models to build professional slide decks that adhere to specific design languages. Unlike frontier models that often default to a generic 'AI-generated' aesthetic, Palette is designed to be self-hosted on private infrastructure to maintain data confidentiality and brand consistency.

The system utilizes a 20B parameter base model enhanced with a LoRA adapter. It solves the common failure modes of small models—such as overlapping text or broken layouts—by splitting the deck-building process into two distinct roles: a 'designer' that creates a structured JSON brief and a 'coder' that generates the necessary JavaScript (pptxgenjs) to render the slides.

The Designer-Coder architecture and synthetic training

• Dual-Role LoRA Adapter: The model splits tasks into an editorial 'designer' role for content planning and a geometric 'coder' role for precise element placement.
• Synthetic Data Pipeline: Training data was generated using a large open teacher model to create approximately 1,000 decks and 10,000 slides, including counterfactual reasoning to explain design choices.
• Automated Quality Harness: A rendering checker identifies layout errors like text overflow or shape overlaps, feeding these mistakes back into the training loop as repaired examples.
• Parallelized Execution: By separating the JSON design brief from the slide-specific code, the coding stage can be executed in parallel for each slide, increasing generation speed by 6–7×.
• Directives for Control: Users can steer specific design elements using plain English instructions wrapped in double brackets within the rollout plan.

Why it matters

This development provides a blueprint for enterprises to deploy highly specialized, small-scale AI models that respect strict design standards and data privacy. By moving away from massive frontier models toward fine-tuned 20B models, organizations can achieve professional-grade automation on their own hardware without vendor lock-in.