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möbius is a model conversion framework for running AI on edge devices. It converts models from PyTorch/ONNX to CoreML, ONNX Runtime, and OpenVINO — targeting Apple Neural Engine, NPUs, and embedded accelerators. Every model that ships in FluidAudio was converted through möbius.

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Why möbius

Running AI on NVIDIA GPUs is straightforward. The edge is a different story — fragmented devices, different accelerators, format incompatibilities. möbius handles the conversion, validation, and quantization so you get production-ready models with a few commands. Each conversion includes:
  • Parity validation — numerical comparison between PyTorch and converted outputs
  • Latency benchmarks — Torch CPU vs CoreML (ANE/GPU) on real inputs
  • Quantization sweeps — size, speed, and quality trade-offs for int8, palettization, etc.

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Repository Structure

Models are organized by class, name, and target runtime. Each target directory is self-contained with its own pyproject.toml and dependencies managed by uv.

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Converted Models

These models have been converted and published to Hugging Face:

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Quick Start

Each model directory has its own README with specific conversion steps.

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Conversion Guidelines

  • Trace with .CpuOnly — ensures deterministic tracing without ANE/GPU side effects
  • Target iOS 17+ / macOS 14+ — minimum deployment target for all CoreML exports
  • Use uv — each model has isolated dependencies via its own pyproject.toml
  • Validate numerically — always compare converted outputs against PyTorch reference

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License

Apache 2.0. See individual model directories for upstream model licenses.