Training design

Require dataset contract and revision, model revision, objective, evaluation plan, hardware, memory budget, cost ceiling, environment policy, output location, and approval for any remote or paid action.

Workflow

1. Select SFT, DPO, GRPO, reward modeling, sentence-transformer, or vision classification/detection/segmentation from the task and evidence.
2. Preflight format, tokenizer/processor, sequence/image limits, labels, adapters, precision, accumulation, and memory.
3. Choose LoRA/QLoRA or full updates; define optimizer, schedule, seeds, evaluation cadence, checkpoint retention, and stop rules.
4. Capture PEP 723/UV or repository-native environment, data/model revisions, hardware, cost, and deterministic settings.
5. Specify Trackio/HF persistence and artifact paths without claiming integration; hand a job specification to ml-stack-compute and run comparison through ml-stack-experiment.

Boundary and outputs

Never submit paid/remote jobs, upload data, or publish checkpoints without explicit approval. If no registry operation matches training, return a bounded executable training specification and exact handoff, not a fake submission result. Abstain when formats, licenses, resource limits, or validation are unresolved. Next owner: ml-stack-compute for execution planning, ml-stack-experiment for isolated comparison, ml-stack-evaluation for quality, and ml-stack-hub for approved artifact storage.

See method selection, SFT/DPO/GRPO, vision training, job handoff, and checkpointing.