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This guide is the fastest post-onboarding path: copy the commands, run the included multiply-local-openai example, and submit one training run before customizing anything. If you already have a dataset or task-specific rollout in mind, use Create Your Own Rollout instead.
This guide assumes you have completed Onboarding: your workspace repository is cloned, the CLI is installed and authenticated, and commands are running from the workspace directory.
1

Verify workspace context

Confirm that the CLI can resolve your workspace before running the example:
osmosis doctor
2

Upload the dataset

Evaluation runs and training runs both reference platform datasets by name. Upload the dataset that ships with the starter example — both configs/eval/multiply-local-openai.toml and configs/training/multiply-local-openai.toml reference it as multiply:
osmosis dataset upload data/multiply.jsonl
3

Register the OpenAI secret

The starter evaluation run calls the OpenAI endpoint, so [secrets] in configs/eval/multiply-local-openai.toml maps OPENAI_API_KEY to a workspace secret record. Register that secret at /:orgName/secrets in the platform UI before submitting, so the platform can inject the key into the evaluation run container.
4

Push and submit an evaluation run

Push the repository so the platform can clone the rollout code, then submit an evaluation run:
git push
osmosis eval submit configs/eval/multiply-local-openai.toml
Inspect progress and results:
osmosis eval list
osmosis eval info <eval-run-name>
5

Submit a training run

Once the evaluation run looks healthy, submit a training run:
osmosis train submit configs/training/multiply-local-openai.toml
6

Inspect and deploy

Monitor the run and deploy a LoRA model when it finishes:
osmosis train info <run-name>
osmosis model deploy <lora-model-name>
At this point, you have run the full Osmosis loop with known-good code: dataset upload, evaluation run, training run submission, and deployment inspection. Use the rollout docs only after this works.

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Next Steps

Create Your Own Rollout

Use project-local Agent Skills when you are ready to adapt Osmosis to your own task.

Training Runs

Learn about training configuration, statuses, and management.

Datasets

Understand dataset formats and validation.

Models

Manage base models and deploy trained LoRA models.