ModelRefs / Full Fine-Tuning Stack — Architecture Pattern
Full Fine-Tuning Stack — Architecture Pattern
Distributed full-parameter training with curated corpora, distributed checkpoints, and gated rollout. Maximal control, maximal cost.
Overview
Full-weight fine-tuning of mid-size models with distributed training, sharded checkpoints, RLHF/DPO post-training, and gated rollout. Maximal control, maximal cost.
When to use it: You need to materially change model behavior beyond what adapters allow.
Pattern details
| Pattern class | fine-tuning |
|---|---|
| Difficulty | advanced |
| Topology | microservices |
| Also known as | sft, full-parameter tuning |
| Last reviewed | 2026-06-07 |
Known failure modes
- Eval contamination — Test data leaks into train. Mitigation: Strict decontam + held-out evals.
- Alignment regression — Helpfulness up, safety down. Mitigation: Multi-axis evals gate release.
When not to use it
- Skipping decontamination of public benchmarks.
Continue your research
Use these connected ModelRefs sections to compare alternatives, inspect implementation paths, and review the evidence and governance boundaries relevant to Full Fine-Tuning Stack — Architecture Pattern.
Frequently asked questions
When should I adopt the Full Fine-Tuning Stack?
You need to materially change model behavior beyond what adapters allow.
What are common failure modes of Full Fine-Tuning Stack?
Eval contamination • Alignment regression
Is Full Fine-Tuning Stack production-ready?
Yes when paired with the safety controls and observability hooks documented on the pattern page.