Platform
LoRA Fine-Tuning for Private Codebases
Adapt coding models to your internal architecture patterns by training lightweight LoRA adapters on private repositories. OnPremize keeps dataset generation, training, and model serving inside your network.
Training workflow
- Generate structured training sets from repository code, docs, and commit history.
- Train adapters locally with resource-aware settings and reproducible run metadata.
- Validate output quality with citation coverage and regression checks before rollout.
When fine-tuning helps most
Teams see the biggest gains when repo conventions, naming patterns, and architecture boundaries are highly specific. Fine-tuning complements RAG by improving response style, code idioms, and suggestion consistency.
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Assess fit for your stack
We can help choose model families, adapter strategy, and GPU sizing for your environment.
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