---

name: "training-recipes" description: "Operate x-transformers training recipe examples safely: catalog scripts, dependencies, data assumptions, and CPU-friendly copy-task smoke adaptation." metadata: disco-role: operating disable-model-invocation: true license: MIT

Training Recipes

Use this sub-skill when you need to choose, adapt, or smoke-test the repository's train_*.py examples without reopening the source checkout.

Fast decision path

1. For a safe local check, run the bundled copy-task smoke:

   python scripts/copy_task_smoke.py --steps 1 --device cpu
   

   It is synthetic, CPU-friendly, and derived from the copy recipe. It verifies a tiny forward/backward/generate loop; it does not prove task convergence.

2. For recipe selection, read references/recipe-catalog.md. It lists every training script, required extras, data expectations, CLI style, and run-safety tier.

3. If a recipe fails to start or stalls, use references/troubleshooting.md before increasing hardware, installing extras, or enabling online logging.

Boundaries

• This sub-skill covers recipe cataloging, command patterns, dataset/source notes, dependency variants, and the bundled copy-task smoke.
• For core model construction APIs, route to ../core-models/SKILL.md.
• For wrapper behavior and sequence workflow internals, route to ../sequence-workflows/SKILL.md.
• Treat the native training scripts as examples, not reusable libraries: several execute training at module import time, assume data/enwik8.gz, or force CUDA/logging-heavy defaults.