AGENTS.md

AGENTS.md — maintenance & publishing guide

Purpose

• A short operating manual for maintainers and agents responsible for validating, publishing and reproducing experiments in this repo.

Primary responsibilities

• Verify artifacts are complete and named consistently.
• Confirm the run configuration, dataset provenance and licenses before publishing.
• Run a basic inference sanity check (generate sample images) before uploading.

Checklist prior to publishing to Hugging Face

1. Files: ensure *.safetensors (final & checkpoints), config.yaml, log.txt, optimizer.pt (optional), and samples/ are present and readable.
2. Metadata: create or update a short model card (README or model card in HF) with dataset provenance, license and usage notes.
3. Privacy: confirm no private personal data is included in the dataset or commit history.
4. Reproducibility: verify that config.yaml matches the run that produced the artifacts and that sample generation runs successfully.

Quick publish steps (recommended)

1. Inspect artifacts/size and confirm they match expectations.
2. Generate validation samples (use the local Z-Image Turbo runner or a minimal script that loads the base model + LoRA and produces 2–5 images).
3. Write or complete the model card: include base model, LoRA config (rank, layers), number of steps, dataset summary and license.
4. Upload/commit to a HF model repository. Minimal files to include:
   • cl4ud1a.safetensors (final adapter)
   • config.yaml (run configuration)
   • log.txt (training log or condensed training summary)
   • README.md or model_card.md (short description & instructions)
   • samples/ (small set of generated images)

Publishing tips & small scripts

• When in doubt, run a short inference test using the same sampler/seed used for saved samples to confirm the LoRA applies and produces reasonable output.
• Use HF CLI or web UI for model uploads; prefer safetensors for environments that accept them.

Versioning / tagging

• Follow semantic incrementing when creating releases (e.g., v1.0 for the first publish). Keep a changelog entry when re-trained or restructured.

Automation & CI

• Add a lightweight validation workflow to run a short inference test (CPU/GPU optional) to ensure cl4ud1a.safetensors loads and generates output.

Notes for reviewers

• Check for dataset licensing issues and flagged content in the training set before accepting publication.
• Encourage authors to add a clear license and small sanitized dataset description for the model card.