---
name: overmind-training
description: Select models, prepare exact training inputs, estimate costs, launch and inspect Overmind fine-tuning jobs. Use for the Training surface, including held-out evaluation and benchmark selection; live model activation belongs to Inference.
---

# Overmind Training

Start with `list_projects` and choose the intended accessible project. For an
account connection, pass its `project_id` on every project tool and resource
URI query; follow returned links. Project API keys retain their narrower access.

Produce or inspect a trained model with an explicit data, evaluation and cost
contract. Use the chosen MCP project. For an existing job, start with
`overmind://finetunes/{job_id}` and its `finetune_job` status; do not relaunch it.

## Prepare the training contract

Prefer the native `finetune-capability` prompt with the selected dataset and
optional capability. Resolve datasets with `list_datasets`; inspect and query
the exact train and held-out eval cells. Inspect whole-frame task families and
workshop findings rather than assuming a sample describes the whole corpus.

Use `get_model_catalog` for initial model discovery, then
`check_finetune_readiness` for the selected dataset. A capability is optional;
held-out evaluation data and an applicable eval set are required. Use the
returned requirements to identify technical blockers. Quality findings,
overlap and incomplete semantic reviews remain visible advisory warnings.

Read `benchmark_model` on the capability when comparing with an incumbent.
`set_benchmark_model` changes future benchmark selection, not live serving or
existing jobs. Use it only for a requested benchmark change. New jobs pin their
benchmark selection and readable dataset versions.

## Size and price the run

For Modal training, call `prepare_training_data` for the chosen model, context
length and cell, then poll the returned `training_preparation` job. Inspect
exact token counts, supervised content and incompatible rows. Send concrete
source-row repairs to `message_dataset_agent`; keep model-specific preprocessing
in Training. Reprepare a changed cell rather than reusing a stale report.

Use `estimate_finetune` and evaluation readiness to present training plus
before/after evaluation spend. Each selected evaluation uses the full pinned
eval dataset. Context warnings do not automatically block launch or change
model selection. An approved preview's `judge_model` becomes
`eval_judge_model` on `start_finetune`; omission preserves the set's judges.

## Launch and verify

Launch only the authorized model/configuration with `start_finetune`. Keep the
selected data, benchmark, evaluator choices and expected spend in the receipt.
Poll `get_job(kind=finetune_job, id=...)`, then inspect the linked deployment
separately. Training completion, evaluation completion and serving readiness
are distinct outcomes.

Report measured before/after results with trust flags and any unresolved
preparation findings. A successful training job does not authorize activation
or a repository model swap. For requested checkpoint export, use
`download-checkpoint` or the `overmind://checkpoint-download` CLI handoff.

Open `training` under the project's Console base with its `projectId` for the
visual job dashboard. Retain the exact job and deployment IDs in the report.