tao-finetune-cosmos-reason
Cosmos3-Nano video QA supervised fine-tuning with FSDP parallelism. Use when training or evaluating video question-answering models, fine-tuning Cosmos3-Nano or compatible Cosmos Reason models with SFT/LoRA, or working with Cosmos-RL. Trigger phrases include "fine-tune Cosmos", "Cosmos3 Nano Reasoner", "Cosmos-RL SFT", "video QA fine-tune", "Cosmos3-Nano training".
npx skills add NVIDIA/skills --skill tao-finetune-cosmos-reason --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Cosmos-RL Supervised fine-tuning (SFT) of Cosmos Reason video QA models. The packaged default base model is **hf_model://nvidia/Cosmos3-Nano**. Pretrained weights are sourced from HuggingFace, not NGC. Gated HuggingFace models require `HF_TOKEN`. Some Cosmos-RL images cannot load the native Cosmos3 Omni checkpoint format directly; for those images, convert Cosmos3-Nano to a Qwen3-VL HF safetenso
What does the tao-finetune-cosmos-reason skill do?
Cosmos3-Nano video QA supervised fine-tuning with FSDP parallelism. Use when training or evaluating video question-answering models, fine-tuning Cosmos3-Nano or compatible Cosmos Reason models with SFT/LoRA, or working with Cosmos-RL. Trigger phrases include "fine-tune Cosmos", "Cosmos3 Nano Reasoner", "Cosmos-RL SFT", "video QA fine-tune", "Cosmos3-Nano training".
How do I install it?
Run `npx skills add NVIDIA/skills --skill tao-finetune-cosmos-reason --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From NVIDIA/skills, a repository with 2,789 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.