Edge is an open-source library to support the research and release of efficient state space models (SSMs) for on-device applications across multiple accelerators and environments.
Current state-of-the-art models are often too large and slow to run on-device, requiring cloud-based solutions that are expensive and slow. Edge aims to change this by providing a library of efficient SSM models that can run on-device in real-time.
State-Space Models (SSMs) offer a fundamentally more computationally efficient, yet high quality, approach to building AI models compared to Transformer models. They provide an elegant foundation for training efficient real-time models that can natively stream information with constant tokens per second (tok/s) and memory consumption, making them a game changer for on-device applications.
With Edge, you have:
📏 Custom inference kernels: Edge supports custom hardware-specialized inference kernels for SSM architectures, like Mamba
📏 Local SSM models: Edge brings together open-weights SSM models and optimizes them for multiple hardware platforms.
- [08/27/2024]: We are excited to release Edge and our first openly available small language model, Rene-v0.1. Rene is available with both PyTorch and MLX with quantization support and custom hardware kernels.
Edge currently hosts packages for multiple backends:
cartesia-pytorch: This package contains the PyTorch implementations of our models and can be used by any hardware where PyTorch is supported (e.g. CPU, CUDA GPU).cartesia-metal: This package contains custom metal kernels for fast SSM inference on Apple silicon on laptop and mobile.cartesia-mlx: A Python package containing models to run on MLX
For a full list of the available models in MLX and an example of how to run these models, see the cartesia-mlx README.
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