Individual skill
tao-train-single-step
Standard single-step train/eval/export workflow for any TAO model. Use when training a TAO model on a dataset without iterative data augmentation, AutoML, or DEFT loops. Trigger phrases include "single train run", "train then evaluate then export", "plain TAO training", "normal training", "no AutoML", "skip the loop". Routes through the per-model SKILL.md for action specifics and through `tao-launch-workflow` for platform/credentials/dataset intake.
skills/tao-train-single-step/SKILL.md
Source commit 25340619336e89bcc40365c28bfe9e790430468e
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- Last checked
- Nov 2, 2026
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- Repository last updated
- Nov 6, 2026
- License
- Apache-2.0
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- training Declared skill tag
- single-step Declared skill tag
- generic Declared skill tag
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BENCHMARK.mddoc · 7,150 bytesSKILL.mdcore · 4,771 bytesconfig/skillspector-baseline.yamlasset · 834 bytesevals/evals.jsonasset · 814 bytesreferences/skill_info.yamlasset · 561 bytesskill-card.mddoc · 3,906 bytesskill.oms.sigasset · 5,077 bytes
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# Normal Train
> **Standalone install?** If this session was not initialized by the TAO skill bank plugin, run the `tao-setup` skill first (host preflight, credentials, cross-skill discovery).
Standard supervised fine-tuning: train a model on a labeled dataset, optionally evaluate, then optionally export. The most common TAO workflow for adapting a pretrained model to a new dataset.
## Steps
1. **train** — executed through AutoML when the selected model has
`automl_enabled: true` and `automl_policy` is `on`; set
`automl_policy=off` for a plain single training run
2. **eval** — executed if `eval_dataset_uri` is resolved
3. **export** — optional, on user request after training
