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Last-mile accuracy remains a challenge in physical AI, with training data bottlenecks and slow post-training iteration cycles slowing teams down.
NVIDIA Cosmos 3 is the open frontier omni-model for physical AI — and now with NVIDIA TAO agentic skills, you can solve that last-mile accuracy challenge.
This livestream shows how to post-train Cosmos 3 in a day, with just a few natural language prompts - taking Cosmos 3 Nano video question answering from 54.41% to 93.35% accuracy with AutoML.
What You'll Learn:
· Why post-train and which method to choose (LoRA vs. SFT)
· How to run an end-to-end post-training pipeline for Cosmos 3 with a single prompt
· How TAO AutoML eliminates manual hyperparameter tuning
· How to deploy your post-trained model with NVIDIA NIM
Have questions about how to post-train and deploy NVIDIA Cosmos 3? Drop them live — the NVIDIA team will answer them in real time.
Access more NVIDIA Cosmos developer resources and join our developer community:
📄 Read How To Post-Train NVIDIA Cosmos 3 in a Day → https://nvda.ws/4wDWJJi
📆 Join Our Office Hour on Discord → https://www.addevent.com/calendar/ss55fmjpm04t
📺 Watch a Tutorial on YouTube → https://www.youtube.com/watch?v=9AQkVbx3fKA
📚 Explore Models & Datasets on GitHub → https://github.com/nvidia/Cosmos
⬇️ Download Cosmos on Hugging Face → https://huggingface.co/collections/nvidia/cosmos3
👥 Join the Cosmos Community → https://discord.com/invite/nvidiaomniverse