#TPU

MaxText is an open-source LLM that enables efficient training and inference on Google Cloud TPUs and GPUs. With support for models such as Llama2, Mistral, and Gemma, it provides robust scalability and high Model Flops Utilization through Jax and XLA compiler integration, making it adaptable for diverse LLM applications.

Brax, developed with JAX, is a high-performance physics engine that supports fast simulations in robotics, human perception, and reinforcement learning. It is optimized for acceleration hardware, making it scalable for simulations across multiple devices. Brax includes training algorithms such as PPO and SAC, utilizing its differentiable simulator for analytical policy gradients. With four physics pipelines, including MuJoCo XLA and Spring, Brax adapts to diverse simulation needs. It offers Colab notebook support and integrates with frameworks like PyTorch. Installation is flexible with pip, Conda, or source, fitting different computational environments.

Explore a framework for training extensive language and foundation models with a focus on readability, scalability, and reproducibility. It's constructed with JAX, Equinox, and Haliax for distributed training on TPUs and GPUs. Enjoy effortless integration with Hugging Face tools and utilize advanced optimizers like Sophia and Optax. Levanter guarantees consistent results across various computing environments, with features like on-demand data preprocessing and robust logging capabilities. Perfect for developers pursuing efficient model development with top-tier benchmarks.