Why architecture matters here
SLM fine-tuning fails on data quality and safety regressions. A model that improves on a benchmark can regress on safety criteria; a well-tuned model on bad data mimics its data.
The architecture matters because eval + safety gates + rollback keep fine-tuning as an improvement loop rather than a risk pipeline.
The architecture: every piece explained
The top strip is the pipeline. Base SLM is the starting model. Data curation cleans + labels task data. LoRA adapter is small trainable weights adjacent to base. Trainer uses SFT or DPO.
The middle row is quality. Eval harness tests task + safety. Safety guards gate on toxicity + jailbreak. Registry versions adapters. Deployment merges adapter into base or loads dynamically.
The lower rows are lifecycle. Rollback safely reverts. Feedback loop harvests labels for next iteration. Ops tracks cost, retraining cadence, and monitoring.
End-to-end flow
End-to-end: team fine-tunes 7B base with LoRA for legal doc classification. Data curated + labeled. Trainer runs 3 epochs. Eval harness confirms +15% task F1, safety metrics unchanged. Registry stores adapter v3. Deployed dynamically. User feedback flows back to data pool for next cycle. Total cost < $500.