Cosmos3 Post-Training (SFT)

When to use this skill

• User wants to fine-tune Cosmos3-Nano (or Cosmos3-Super via LoRA) on the example Bridge video dataset or a custom video dataset (SFT)
• User asks which fields in a recipe TOML to override ([model.parallelism].data_parallel_shard_degree, [dataloader_train].max_samples_per_batch, [optimizer].lr, [trainer].max_iter, [checkpoint].load_path, ...) or which experiment SKU to pick
• User wants to convert a base Hugging Face checkpoint to DCP, or convert a trained DCP back to safetensors
• For installation, --group=cu130-train / cu128-train, or LD_LIBRARY_PATH issues, hand off to cosmos3-setup
• For inference parameters, parallelism presets, or online serving, hand off to cosmos3-inference
• For raw-video captioning or assembling a SFT JSONL, see docs/dataset_jsonl.md (the captioning flow has moved out of docs/training.md)

Path convention

All paths below are relative to the cosmos3 package root (../../../ from this skill file). All uv run / python / torchrun / bash commands should also be run from there.

Where to find answers

The canonical reference is docs/training.md. Use this table to route questions: