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name: training-and-adapters description: "Prepare and reason about DeTikZify training, pretraining, refinement, sketchification, TikZero adapter workflows, checkpoints, and distributed GPU launch patterns." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Training and Adapters

Use this sub-skill when the task is about fine-tuning, pretraining, refinement, sketchification, TikZero adapter work, distributed GPU launch patterns, or checkpoint recovery in DeTikZify.

Route away from this sub-skill when the task is only about one-off inference, the browser UI, or evaluation metrics.

Fast Path

1. Confirm the package and GPU environment are healthy:

   python scripts/api_smoke.py
   

2. Read the workflow reference before starting a long run:

   references/workflows.md
   

3. Check the training-specific failure modes if the launch uses DeepSpeed, TRL, or adapter checkpoints:

   references/troubleshooting.md
   

What This Sub-Skill Owns

• standard fine-tuning on DaTikZ-style data
• projection pretraining on large figure datasets
• GRPO / self-feedback refinement workflows
• sketchification and sketch-augmented training data generation
• TikZero adapter pretraining and end-to-end adapter fine-tuning
• checkpoint resumption, WORLD_SIZE, RANK, and distributed GPU launch concerns
• DeepSpeed and gradient-checkpointing choices

Common Decisions

• Use torchrun --nproc_per_node gpu for the repo's GPU-first training scripts.
• Use deepspeed only when the job needs it; do not add it to every run by default.
• Use gradient_checkpointing when memory pressure matters more than speed.
• Watch the output directory handling: some flows resume from the last checkpoint unless overwrite or a fresh directory is chosen.
• Keep the dataset requirements explicit: DaTikZ, ArxivCap, SPIQA, OpenMoji, adapter checkpoints, and sketch/parquet artifacts are workflow-specific.
• Treat examples/refine.py as the most dependency-heavy path because it needs TRL vision support plus compile-backed rewards.

Bundled References

• references/cli-reference.md: command-line arguments, launch patterns, and output behavior.
• references/workflows.md: end-to-end training, pretraining, refinement, and TikZero recipes.
• references/troubleshooting.md: long-run failures, missing dependencies, checkpoint, and distributed training issues.

Guardrails

• These workflows are intentionally expensive. Use them only when the user actually wants to train or refine a model.
• Do not assume CPU fallback is adequate for the repo's training paths.
• Do not start from the UI or inference sub-skill when the task is mainly about gradients, checkpoints, or dataset transforms.