NVIDIA has announced a detailed guide on fine-tuning the Cosmos Predict 2.5 world model using parameter-efficient techniques like LoRA and DoRA.
Key Developments
Cosmos Predict 2.5 is a 2-billion-parameter model capable of predicting subsequent frames in robotic videos. However, to adapt to specific environments, fine-tuning is necessary. NVIDIA proposes using LoRA (Low-Rank Adaptation) and DoRA (Weight-Decomposed Low-Rank Adaptation) to reduce the required GPU memory, enabling execution on a single GPU such as the H100.
Why It Matters
Enabling robot learning efficiently through world models helps reduce the cost of real-world data collection. For the AI and robotics research community in Vietnam, this is an opportunity to access NVIDIA's state-of-the-art technologies with moderate hardware resources, paving the way for smart robotic applications in logistics and manufacturing.