Provides research-grade implementations and pretrained models for sequence tasks (translation, LM, speech). Offers multi-GPU training, fast generation (beam/sampling/lexical constraints), mixed-precision, and state sharding — aimed at researchers reproducing or extending papers.
ONNX (Open Neural Network Exchange) is an open ecosystem that provides an open source format for AI models, including deep learning and traditional ML. It defines an extensible computation graph model, built-in operators, and standard data types, focusing on inferencing capabilities. Widely supported across frameworks and hardware, it enables interoperability and accelerates AI innovation.
Modular implementations of object detection, instance/semantic/panoptic segmentation and related vision models for research and deployment. Offers a large model zoo, export to TorchScript/Caffe2, and PyTorch-native optimizations for faster training and extensibility.
Provides PyTorch code, pretrained checkpoints, and evaluation tooling for V-JEPA 2 — a Meta FAIR family of self-supervised video encoders and an action-conditioned world model. Includes training recipes, HuggingFace checkpoints, evaluation probes, and robot post‑training artifacts.
Promptable image/video segmentation that finds, segments, and tracks all instances of an open‑vocabulary concept from short text or exemplars. Key differences: an automated 4M‑concept data engine, a presence token for finer text discrimination, and a detector–tracker design. Checkpoints on Hugging Face; GPU required.
