multiply-local-openai example to verify that your workspace can upload a dataset, finish an evaluation run, and submit training. If you already have a task or dataset to adapt, use the Custom Rollout Guide instead.
This guide assumes you have completed Onboarding — the fastest way is running
osmosis quickstart, which leaves you with a cloned workspace repository and an authenticated CLI. Run the commands below from the workspace directory.1
Verify workspace context
Confirm that the CLI can resolve your workspace before running the example:Success signal: the check identifies the expected workspace and reports no blocking setup issue.
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 Success signal: the upload completes and
configs/eval/multiply-local-openai.toml and configs/training/multiply-local-openai.toml reference it as multiply:multiply appears in the workspace dataset list.3
Register the OpenAI secret
The starter evaluation config requires a platform secret named
OPENAI_API_KEY. Add it under Secrets as a personal secret when the value should remain private to you, or as a workspace secret when the team should share it; creating a workspace secret requires an owner or admin. If both scopes contain this name, your personal value takes precedence at run time.Success signal: the Secrets page lists OPENAI_API_KEY with the intended Personal or Workspace scope.4
Confirm billing readiness
Evaluation and training submissions require billing to be in good standing; the optional model deployment later in this guide does too. For a self-serve workspace, an owner or admin must add a valid payment method. For an invoiced workspace, no invoice can be past due. Review Billing before starting the evaluation.Success signal: the workspace satisfies its billing mode and shows no billing action blocking paid operations.
5
Push and submit an evaluation run
Push the repository so the platform can clone the rollout code, then submit an evaluation run:Inspect progress and results:Success signal: the evaluation reaches
finished, and its score, pass rate, and sample results are available for inspection.6
Submit a training run
Once the evaluation run looks healthy, submit a training run:Success signal: the submitted run appears in the workspace and
osmosis train info shows its current status.multiply dataset is available, the evaluation run has finished successfully, and the training run has been submitted. Continue monitoring that run in the Platform or with osmosis train info, then use the rollout guide to adapt the workflow to your own task.
Optional Inference Deployment
After the training run finishes, if inference deployment is available for your account, you can optionally deploy one of its LoRA models:osmosis model info <lora-model-name>. Deployment is not required to complete this quickstart.
Next Steps
Custom Rollout Guide
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.