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:

• workflows/testing/cosmos-fetch.yaml
• workflows/testing/cosmos3-text-to-image.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.