# Treat model adaptation as a controlled experiment

Canonical: https://savrn.com/cloud/training

SAVRN Cloud · Coming soon. Public documentation and a browser simulation are available; connected services are not yet available.

Training should begin with a question that a baseline model does not yet answer well. SAVRN Cloud’s planned adaptation workflow connects the training dataset, recipe and resulting candidate to a separate evaluation and release decision. The public preview creates synthetic runs and candidates. Connected training is Coming soon.


## State the change you want to measure

A specialist research assistant may need a more consistent response format or better performance on a bounded task. Define that behavior and establish an evaluation method before selecting a training recipe. Some problems may be better addressed through sources, prompts or task design. Adaptation becomes useful when a repeatable test can show whether it helps.

## Inspect the ingredients of a run

The preview pairs a demonstration model with a dataset marked for training examples. Its recipe controls include LoRA or QLoRA, epochs, learning rate and rank. These settings are recorded as part of a synthetic run. They illustrate configuration and handoff; no weights are loaded, no optimizer runs and no adapter artifact is actually produced.

## Keep learning separate from release

Finishing the local training flow creates a candidate rather than silently changing an active deployment. The next step is evaluation using a different dataset identity, followed by a distinct approval and promotion process. This separation makes the intended experiment easier to review and avoids treating completion of a technical job as evidence that its output is suitable.

## Require a reproducible connected recipe

A future training service would need pinned base artifacts, dataset revisions, runtime details, checkpoints and meaningful failure records. Resource estimates and supported recipes would need qualification on actual execution capacity. The team should be able to explain which inputs produced a candidate and why it was selected, without inferring quality from a declining training metric alone.

## What you can explore today

Configure a sample adapter run and follow its synthetic candidate into evaluation and review.

## Before this service launches

Connected training requires qualified recipes and capacity, artifact management, reliable job accounting and measured candidate evaluation.

## The customer outcome

The intended outcome is an adaptation experiment whose inputs, recipe and evaluation path can be reproduced and reviewed before deployment.

## Related documentation

- [Lab and learning methods](https://savrn.com/cloud/docs/lab-overview)
- [First adapter training run](https://savrn.com/cloud/docs/lab-first-adapter-run)
- [Dataset and experiment releases](https://savrn.com/cloud/docs/lab-datasets-releases)
- [Explore training](https://savrn.com/cloud/console/#/training): Create a synthetic run with a recorded recipe.