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Model Registry: HuggingFace Hub Private Repo + MLflow + S3 Layout + Versioning

How to manage 50+ FT model versions in production? HuggingFace Hub private repo + MLflow Model Registry + S3 (chunked artifacts) hybrid. Versioning convention (semver + lineage), tags (production/canary/archive), retention policy. Cookbook's model card template (LoRA adapter + base + recipe).

Şükrü Yusuf KAYA
28 min read
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Model Registry: HuggingFace Hub Privat Repo + MLflow + S3 Layout + Versioning

1. Cookbook Model Registry Hiyerarşisi#

HuggingFace Hub (privat repo): kompanyam/llm-models ├── llama-3.1-8b-tr-instruct-v1.0/ # Stable baseline ├── llama-3.1-8b-tr-instruct-v1.1/ # Minor improvement ├── llama-3.1-8b-tr-instruct-v2.0/ # Major retrain ├── llama-3.1-8b-tr-customer-support-v1.0/ # Domain variant └── ... Her repo içinde: - adapter_model.safetensors # LoRA weights - adapter_config.json # PEFT config - tokenizer.json / tokenizer_config.json - README.md # model card (zorunlu) - eval_results.json # benchmark sonuçlar - training_config.yaml # reproducible - WANDB_RUN_URL # full training telemetry
Lineage triple (Part 0 Ders 0.5):
  • _git_sha
    — kod versiyonu
  • _data_sha256
    — dataset versiyonu
  • _wandb_run_id
    — eğitim run ID
Bu triple ile 6 ay sonra reproduce edilebilir.
✅ Teslim
  1. HF Hub'da privat repo aç. 2) Bir FT model'i push et (full convention'la). 3) Sonraki ders: 16.2 — A/B + Shadow Traffic.

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