File size: 2,547 Bytes
4ff3728 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | # 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.
|