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Zelda Model Manager

zelda-model-manager

Manage Zelda/ALTTP/Oracle model training, datasets, evals, registry updates, and deployment artifacts (Nayru/Din/Farore/Veran/Sahasrahla/IQuest). Use when planning or running training runs, curating ASM datasets, selecting base models, evaluating outputs, or converting/serving GGUF or MLX models.

scawful/afs_scawful0installs0starsDevOps & CI/CD

SKILL.md

Full skill instructions

Zelda Model Manager

Scope

  • Manage Zelda and ASM model lifecycle: dataset inventory, training runs, evals, registry, and deployment artifacts.

Workflow

  1. Confirm the target model role and naming.
    • Use ~/​src/​docs/​NAMING_CONVENTIONS.md and ~/​src/​lab/​afs-scawful/​docs/​MODEL_PORTFOLIO.md.
    • Keep hostnames as SSH aliases (medical-mechanica, halext-nj) instead of IPs.
  2. Locate datasets and scripts before deciding on a run.
    • Read ~/​src/​training/​INDEX.md and ~/​src/​training/​README.md for dataset paths and scripts.
    • For large runs, consult ~/​src/​training/​docs/​IQUEST_40B.md.
    • For smaller Zelda plans, consult ~/​src/​lab/​afs-scawful/​docs/​ZELDA_16B_TRAINING_PLAN.md.
  3. Choose base model and hardware based on tool-calling needs.
    • Prefer Qwen 2.5 Coder for tool calling and ASM workflows.
    • Follow ~/​src/​training/​docs/​MODEL_SELECTION_AND_PRACTICES.md.
  4. Run QA before training.
    • Use dataset QA and registry updates described in ~/​src/​lab/​afs-scawful/​docs/​ZELDA_16B_TRAINING_PLAN.md.
    • Ensure AFS dataset index is current (python -m afs_scawful datasets index).
  5. Monitor training and evaluate.
    • Use eval packs in ~/​src/​training/​evals/.
    • Track ASAR pass rate for ASM validity.
  6. Register and deploy artifacts.
    • Use the AFS registry (~/​src/​lab/​afs-scawful/​config/​chat_registry.toml) to define personas, ports, and parameters.
    • Use ~/​src/​tools/​model-mgr/​model-mgr for GGUF/​MLX conversion and Ollama imports.
    • Test deployments using python3 ~/​src/​lab/​afs/​lmstudio_client.py (checks health and ports).

Commands to reuse

  • model-mgr list and model-mgr info <model> for inventory.
  • model-mgr convert <model> --quantize q4km for GGUF.
  • model-mgr mlx-convert <model> --hf-path <path> for MLX exports.

Knowledge References

Consult the global knowledge base at ~/​.context/​knowledge/​models/ for background:

  • Model portfolio & status: models/​portfolio.md
  • Training pipeline architecture: models/​training-pipeline.md
  • Dataset catalog: models/​datasets.md
  • GGUF conversion & deployment: models/​infrastructure.md
  • Step-by-step workflows: models/​workflows.md
  • Serving & routing: models/​serving.md

References

  • Read references/​sources.md for source paths and anchors.