Micro-model ecosystem: distill LLM knowledge into deployable micro-models.
| Package | PyPI | npm | crates.io | Description |
|---|---|---|---|---|
| plato-core | pip install plato-core |
@superinstance/plato-core |
— | Base types + mesh registry |
| tensor-spline | pip install tensor-spline |
@superinstance/tensor-spline |
— | SplineLinear layers, 5-20x compression |
| eisenstein-embed | pip install eisenstein-embed |
@superinstance/eisenstein-embed |
— | 5-layer matching cascade |
| plato-training | pip install plato-training |
— | — | Training framework (monolith) |
| plato-deadband | — | — | plato-deadband |
Deadband caching (Rust) |
| constraint-theory-core | — | — | constraint-theory-core |
Constraint solving (Rust) |
| spectral-conservation | — | — | spectral-conservation |
Spectral analysis (Rust) |
Each package is standalone — install only what you need. When co-installed, packages auto-discover and mesh via entry_points.
See MESH-ARCHITECTURE.md for the full specification.
- Eisenstein encoder: 71.2% hit rate, 653x smaller than Model2Vec
- SplineLinear: 16,384:1 compression ratio on 512×512 layers
- Bitvector matching: 93.8% typo accuracy, zero ML dependencies
- ONNX inference: 58,648 qps on CPU (700x faster than PyTorch)
- Heterogeneous compute: CUDA (training) + CPU/ONNX (inference) + iGPU (overflow) + NPU (pending)