name: training
description: "Teach Donut fine-tuning, dataset layout, checkpoint handling, and evaluation."
disable-model-invocation: true
metadata:
disco-role: operating
license: MIT
Training
Use this sub-skill when the request is about Donut fine-tuning, dataset JSONL layout, validation metrics, checkpoint layout, or checkpoint evaluation.
Route here for
- training or fine-tuning Donut on CORD, DocVQA, RVL-CDIP, TrainTicket, or a similar structured-document task
- inspecting
metadata.jsonl
- understanding
gt_parse versus gt_parses
- comparing validation metrics or test scores
- resuming a run from a saved checkpoint
Do not route here for
- single-image inference or prompt construction details →
../inference/SKILL.md
- the Gradio demo or app launch →
../inference/SKILL.md
- SynthDoG dataset generation →
../synthdog/SKILL.md
Read first
references/workflows.md
references/configuration.md
references/troubleshooting.md
Fast rules
- Training requires CUDA; the bundled
scripts/train_donut.py preserves the source GPU, DDP, and 16-bit execution assumptions.
scripts/train_donut.py exposes only --config and --exp_version; all other overrides are passed through sconf.
metadata.jsonl stores file_name plus a JSON-encoded ground_truth string with either gt_parse or gt_parses.
val_metric is normalized edit distance, so lower is better.
test.py reports TED-based accuracy and F1, so higher is better.
Useful links inside this skill tree
- Core API reference:
../../references/api-reference.md
- Prompt-level prediction notes:
../inference/SKILL.md
- Synthetic data creation:
../synthdog/SKILL.md
Bundled helpers
scripts/train_donut.py plus scripts/lightning_module.py
scripts/check_training_config.py
scripts/evaluate_dataset.py
references/configs/train_*.yaml
Default workflow
- Validate the config and local metadata with
scripts/check_training_config.py.
- Train with the bundled
scripts/train_donut.py or compare a checkpoint with scripts/evaluate_dataset.py.
- Use the tables in
references/configuration.md and references/workflows.md before comparing runs.