Export, Train, and Evaluate
Use Overview to prepare a dataset download and Results to configure connected training and evaluation. Downloading a package does not submit a training job.
Prerequisites
- A reviewed dataset with the intended splits.
- An export profile compatible with its method.
- An active provider connection for connected training.
- An available supported model and explicit authorization to launch.
Prepare the package
Choose Prepare download in Overview. Inspect the pinned dataset, profile, counts and exclusions before creating the package. A ready package provides the canonical files and manifest for that exact dataset state.
Canonical demonstration and preference exports preserve their method-specific targets. The package can include train, validation and test files where those splits have members, plus its manifest and supporting instructions/materials. A provider-specific conversion is a separate compatibility boundary.
Train deliberately
In Results choose the provider connection, base model and supported settings. Saving configuration alone does not upload data or start a job. Launch explicitly and follow the provider job identity and state.
Provider profiles govern supported text, materials, tools, trajectories or preference formats. An unsupported feature is rejected rather than silently removed. A successful connection probe does not establish create permission, quota or successful training.
The training transfer uses train and validation as supported. Test content remains held out. Do not upload benchmark Cases or protected rubric/construction context as learner data.
Evaluate separately
Authorize evaluation through the supported inference connection or provider path. Training does not automatically provision an inference deployment for every service. Where the deployment association is user-reported, keep that distinction visible.
Inspect actual responses, rubric outcomes, completeness and the resulting model identity. A job that completed without a confirmed model output is not a verified trained candidate.
Object and state changes
Package creation captures the selected dataset. Launch records an authorized training operation and provider job. Cancellation and evaluation have separate operations. Existing packages and accepted results remain attributable.
Success criteria
- Package, dataset and provider job identities agree.
- Held-out data stays out of training.
- The resulting output is identified before evaluation.
- Evaluation uses actual behavior rather than job status.
Common failure modes
- Assuming Save starts training.
- Dropping unsupported fields to fit a provider.
- Treating training completion as correctness.
- Claiming inference resources were provisioned merely because training succeeded.