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name: training-and-evaluation description: "Guides pix2tex model training, resizer training, evaluation, YAML configuration, checkpoints, GPU memory planning, and optional training dependencies." disable-model-invocation: true metadata: disco-role: operating license: MIT

Training and Evaluation

Use this sub-skill when the user wants to train or fine-tune the LaTeX-OCR encoder-decoder model, train the image-resizer model, evaluate BLEU/edit distance/token accuracy, tune config YAMLs, resume from checkpoints, or debug training dependencies and GPU memory.

Quick Route

1. Read references/configuration.md before editing YAML config fields.
2. Read references/training-and-evaluation.md for train, resizer, and eval command recipes.
3. Read references/model-architecture.md for the hybrid/VIT encoder, transformer decoder, tokenizer, and checkpoint relationships.
4. Read references/troubleshooting.md for missing extras, data/checkpoint issues, GPU memory, and long-run boundaries.
5. Run scripts/summarize_pix2tex_config.py to inspect a config safely before starting training.

Minimal Training Command Pattern

pip install "pix2tex[train]"
python -m pix2tex.train --config path/to/config.yaml

Run this only after dataset pickles and tokenizer paths in the config are valid. Training is long-running and can use W&B and GPU memory; confirm budget and hardware first.

Evaluation Pattern

python -m pix2tex.eval \
  --config path/to/config.yaml \
  --checkpoint path/to/weights.pth \
  --data path/to/val.pkl \
  --no-cuda \
  --num-batches 5

Use a small --num-batches first. Full evaluation requires the [train] extra because metrics use torchtext and edit distance uses Levenshtein.

Boundaries

• Dataset/tokenizer creation belongs in ../data-preparation/SKILL.md.
• User-facing inference after training belongs in ../ocr-inference/SKILL.md.
• Do not start training, downloads, W&B logging, or full evaluation unless the user has approved the runtime cost and provided valid datasets/checkpoints.