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When you fine-tune or self-improve a model on Omnia, the result is yours. Unlike closed fine-tuning platforms, where the model you trained stays locked inside the vendor’s serving stack, Omnia gives you the weights and a choice of where they run.

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Three ways to run it

On Omnia

Deploy to a dedicated endpoint and call it through the same API as any model. Zero ops; billed per GPU-hour.

Another provider

The weights are a standard checkpoint. Run them on any provider that serves open-weight models. No Omnia-specific format, no lock-in.

Your own infrastructure

Download the weights and serve them yourself: on-premise, in your own cloud, or air-gapped. The model, and your data, never leave your control.

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Why ownership matters

A fine-tune encodes your proprietary domain logic: your tone, your policies, your task. Building that into a platform that then holds it hostage behind premium serving fees is a poor trade. Ownership means:
  • No serving lock-in. You are never forced to keep paying one vendor’s compute rates to use a model you trained.
  • Portability. Standard weights run anywhere open models run.
  • Sovereignty. For regulated or air-gapped deployments, the model runs where your data already lives, with a local, auditable footprint.

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The proof travels with the model

A downloaded model is only as valuable as the evidence it works. Every model Omnia trains carries its validation report: the calibrated judge it was trained against, its corrected pass rate on your held-out grades, and the confidence interval, the same validated-evals numbers, produced by a judge measured against your own ground truth. So the deliverable is not just a weights file. It is a weights file plus the measured proof that it clears your bar, the part no benchmark and no vendor dashboard can give you, because the bar is defined by your grades.
Deployment options depend on the base model and your account’s serving entitlements. If a model can be trained but not yet served on a given path, Omnia surfaces that plainly: you always know which of the three routes is available for a given model.