---

name: inference description: "Load Donut checkpoints, build prompts, run single-image inference, convert JSON tokens, and launch the Gradio demo." disable-model-invocation: true metadata: disco-role: operating license: MIT

Inference

Use this sub-skill for one-image Donut inference, prompt selection, JSON token round-trips, and the Gradio demo.

Route First

• Start with references/demo-and-prediction.md for command recipes, prompt templates, image preprocessing behavior, output shapes, and token round-trips.
• Use scripts/run_inference.py for CLI inference and prompt-variant comparison.
• Use scripts/launch_demo.py for the Gradio demo launcher.
• Use references/troubleshooting.md for model loading, download, prompt-token, CPU/CUDA, and Gradio failures.
• If you need dataset scoring, accuracy, or evaluation over a dataset, route to ../training/SKILL.md.
• If the image came from synthetic generation and the user is actually asking about synthetic-data creation, route to ../synthdog/SKILL.md instead.
• For the shared package API and cross-cutting troubleshooting, use the parent-tree references (../../references/api-reference.md and ../../references/troubleshooting.md) once the root skill is in place.

Boundaries

• Include: DonutConfig, DonutModel, from_pretrained, inference, json2token, token2json, prompt/task selection, image preprocessing behavior, CPU/CUDA inference behavior, demo launcher.
• Exclude: training loops, Lightning trainer setup, dataset evaluation metrics, SynthDoG generation.

Quick Paths

• python scripts/run_inference.py --help
• python scripts/launch_demo.py --help

What to do

• Load a local checkpoint or Hub checkpoint with the Donut runtime API.
• Build the exact task prompt the checkpoint expects.
• Run one image at a time, or compare several prompt variants against the same image.
• Convert structured JSON to Donut tokens and back when you need to inspect prompt/output formatting.
• Start the Gradio demo with a local model and a sample image if available.