NVIDIA发了个Colab友好教程，教你搭简化版Cosmos 3世界模型，用Omnimodal MoT架构实现跨模态预测，比跑全量模型省资源。
本教程使用NVIDIA的cosmos-framework，在Colab上构建紧凑的Omnimodal Mixture-of-Transformers模型。该模型共享跨模态注意力，路由文本、视觉、动作各模态到专属专家。通过合成物理世界数据和自回归rollout，模型预测未来潜状态。教程展示了如何用小型版模拟Cosmos 3世界模型的核心能力。
NVIDIA’s Cosmos-Framework Tutorial: Designing a Colab-Friendly Miniature of Cosmos 3 World Models with Omnimodal Mixture-of-Transformers
In this tutorial, we explore NVIDIA's cosmos-framework from a practical Colab angle while staying honest about the hardware needed for real Cosmos 3 checkpoints. We probe the runtime, then use the framework's real structure, CLI surface, and input schema as a foundation. We build and train a compact omnimodal Mixture-of-Transformers that shares cross-modal attention while routing each modality to its own expert. Using synthetic physical-world data and an autoregressive rollout, we show how the model predicts future latent states across text, vision, and action. The post NVIDIA’s Cosmos-Framework Tutorial: Designing a Colab-Friendly Miniature of Cosmos 3 World Models with Omnimodal Mixture-of-Transformers appeared first on MarkTechPost .