The LWM has several capabilities including retrieving facts across 1M context with high accuracy, answering questions over 1 hour YouTube video, chatting with images, and generating videos and images from text. The model is available in different versions with context sizes ranging from 32K to 1M tokens. The vision-language models are available only in Jax, and the language-only models are available in both PyTorch and Jax. The codebase and model weights of LWM are released under the Apache 2.0 License.
Key takeaways:
- The Large World Model (LWM) is a general-purpose large-context multimodal autoregressive model, trained on a large dataset of diverse long videos and books using RingAttention, capable of language, image, and video understanding and generation.
- It addresses challenges of memory constraints, computational complexity, and limited datasets by curating a large dataset of diverse videos and books, using the RingAttention technique to train on long sequences, and gradually increasing context size from 4K to 1M tokens.
- LWM has capabilities such as retrieving facts across 1M context with high accuracy, answering questions over 1 hour YouTube video, chatting with images, and generating videos and images from text.
- The project has open-sourced a family of 7B parameter models capable of processing long text documents and videos of over 1M tokens, paving the way for training on massive datasets of long video and language to develop understanding of both human knowledge and the multimodal world.