What Whitney runs
Whitney exposes provider-neutral training primitives rather than a managed recipe endpoint. Your application owns datasets, batching, reward functions, advantage construction, loss construction, evaluation, and the decision to advance the optimizer. Whitney owns authentication, provider provisioning, operation sequencing, leases, checkpoint references, sampler synchronization, usage reconciliation, artifact signing, and cleanup. There is no managed job submit route, hosted evaluator, dataset upload product, or inference API. Implement evaluation in your own stack using Whitney sampling primitives. See Evaluation.Optimizer contract
Optimizer steps use explicit AdamW fields in the JSON control plane:Checkpoints and artifacts
- Only LoRA tuning is admitted by current capability rows.
- A resume checkpoint is provider-bound and model-bound. Resume creates a new session and run rather than reattaching a live provider client.
save_state,save_weights_for_sampler, andexport_loraare distinct operations with different purposes.- Full-weight export and cross-provider checkpoint portability are not part of the current contract.
Sampling and cost
The first sampler sync or sample on Modal may provision a separate sampler GPU and can be cold. Tinker routes sampling through its provider fleet. Shared Modal volume storage cost cannot be attributed to one run and is reported as unavailable accounting data, never as zero. Tinker provider billing may settle after primitive completion. Provisional usage remains visible until completed-hour reports arrive. UseGET /v1/training/sessions/{session_id}/cost for pending, settled, or
unavailable — Whitney does not fabricate a settled dollar amount.
