git:20260724.349c740 to git:20260802.5fa1771

---

name: cosmos3-post-training

description: Use when planning, reviewing, or explaining Cosmos3 supervised fine-tuning and post-training in NPA, including upstream recipes, dataset/checkpoint preparation, and why NPA does not expose post-training as a fake skill command.

---

# Cosmos3 Post-Training

## Source And Attribution

Adapted from NVIDIA cosmos-framework

`skills/workflows/cosmos3-post-training/SKILL.md`.

Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. Used under OpenMDW-1.1.

See `skills/LICENSE-NVIDIA-COSMOS3-OPENMDW-1.1` and

`skills/NOTICE-NVIDIA-COSMOS3`.

## When To Use

Use this skill when the user asks how Cosmos3 SFT works, how to review future

post-training support, where upstream recipes live, how to validate training

configs, or whether an NPA change should expose post-training.

For current NPA, treat Cosmos3 post-training as guidance and planning unless a

real executable workflow is implemented and tested. Do not add a Cosmos

skill-display subcommand or a SkyPilot YAML whose only purpose is to make this

agent skill runnable.

## Current NPA Boundary

Retained real Cosmos3 workflows:

- `npa/src/npa/workflows/skypilot/cosmos3-text-to-image-inference.yaml`

Current NPA Cosmos commands such as `npa workbench cosmos train` cover the

existing Cosmos workbench/serverless training surface, not a proven Cosmos3 SFT

workflow. Do not present that as Cosmos3 post-training unless implementation and

tests explicitly support it.

## Upstream Post-Training Map

In a clone of `https://github.com/NVIDIA/cosmos-framework.git`, inspect:

| Need | Upstream path |

| --- | --- |

| Training guide | `docs/training.md` |

| Dataset JSONL/captioning guide | `docs/dataset_jsonl.md` |

| SFT recipes | `examples/toml/sft_config/<recipe>.toml` |

| Paired recipe launchers | `examples/launch_sft_<recipe>.sh` |

| Common launcher helper | `examples/_sft_launcher_common.sh` |

| Training script | `cosmos_framework/scripts/train.py` |

| DCP conversion | `cosmos_framework/scripts/convert_model_to_dcp.py` |

| HF export | `cosmos_framework/scripts/export_model.py` |

| TOML schema | `cosmos_framework/configs/toml_config/sft_config.py` |

## Planning Checklist

When reviewing or designing NPA Cosmos3 post-training support:

1. Define the exact executable outcome: config validation, dry run, training,

checkpoint conversion, export, or inference from a trained checkpoint.

2. Require explicit dataset, base checkpoint, and Wan VAE paths where the

upstream recipe requires them.

3. Keep training extras explicit: `cu130-train` or `cu128-train`.

4. Validate TOML/schema behavior with upstream `train.py --dryrun` before

claiming training support.

5. Use temporary or user-selected output roots, not repository paths.

6. Preserve redaction for Hugging Face, GitHub, NGC, S3, and any other secret

env values.

7. Add tests that prove NPA maps inputs into a real executable workflow. Do not

use tests that only prove an agent skill can be listed or displayed by a CLI.

## Upstream Workflow At A Glance

The upstream flow is:

1. Install training extras and clear `LD_LIBRARY_PATH` if needed.

2. Prepare the dataset and any required VAE artifact.

3. Convert the base Hugging Face checkpoint to DCP when the recipe requires it.

4. Launch the paired `examples/launch_sft_<recipe>.sh` script or an equivalent

raw `torchrun` command.

5. Find checkpoints under the upstream training output root.

6. Run inference with the trained DCP checkpoint and its `config.yaml`.

7. Optionally export to Hugging Face safetensors.

If the user needs inference after training, switch to

`skills/workflows/cosmos3-inference/SKILL.md` and point `--checkpoint-path`

plus any upstream config file at the trained checkpoint output.