| # 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. | |