gaelic-ghost/socket/choose-apple-model-runtime

Claude Code and agent skill by gaelic-ghost/socket

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Compare and select Core AI, Core ML, MLX, MLX Swift, MLX LM, ExecuTorch Apple delegates, or Foundation Models. Use when an Apple model workflow needs a runtime choice and implementation handoff.

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SKILL.md

Choose Apple Model Runtime

Route By Artifact And Constraint

NeedStart with
Host a downloaded model locally for an API client, agent framework, or dev toolLM Studio local server; choose native /api/v1 model-management APIs or OpenAI-compatible /v1 inference APIs deliberately
Author .aimodel packages with editable Python primitives and Swift runtime utilitiesChoose Core AI, then hand off to the coreai-models working-with-coreai and model-authoring skills
Lower torch.export.ExportedProgram into Core AI IRcoreai-torch
Quantize, palettize, or prune Core AI modelsChoose Core AI, then hand off to Apple's model-compression-exploration skill and coreai-optimization
Convert and deploy established Core ML model packagescoremltools plus Core ML
Train or run tensor programs natively on Apple siliconMLX
Integrate MLX models in SwiftMLX Swift
Fine-tune or serve supported language models with MLXMLX LM
Use one ExecuTorch .pte pipeline with Apple accelerationCompare the ExecuTorch Core ML backend and experimental MLX delegate
Use Apple's system on-device language model without shipping weightsFoundation Models framework; use Python Apple FM SDK for supported Python access

Decision Workflow

  1. Decide whether the need is a local inference server for a client or an app-packaged runtime/artifact. Use LM Studio for the former; identify the source artifact for the latter.
  2. Identify the deployment API: Python research, Swift app, ExecuTorch C++/mobile, or Foundation Models.
  3. Consult the dated maturity and availability matrix in references/apple-model-tooling.md, then confirm OS, Xcode, SDK, device, architecture, operator, dynamic-shape, state/cache, and precision requirements against the current official source.
  4. Select the shortest supported conversion path. Do not round-trip through formats merely because converters exist.
  5. Prototype one representative subgraph and one stateful generation step before converting the full model.
  6. Evaluate numerical/behavioral parity on the exact packaged artifact.
  7. Benchmark on the target device with benchmark-model-runtime.
  8. Return the selected runtime, maturity class, source revision/date checked, unmet availability gates, and implementation owner. Hand Core AI authoring/compression to Apple's named skills and app-facing Swift/Xcode work to apple-dev-skills.

Important Distinctions

  • Core AI and Core ML are related Apple deployment surfaces but are not interchangeable artifact formats or APIs.
  • MLX is a general Apple-silicon array framework; MLX LM and MLX Swift are distinct higher-level/use-language surfaces.
  • ExecuTorch's MLX delegate is marked experimental and under active development upstream. Treat support as revision-specific and compare it separately with the Core ML backend.
  • Foundation Models uses Apple's system model and availability contract; it is not a route for packaging arbitrary user-supplied weights.
  • LM Studio is a local model server and control plane, not a Core AI, Core ML, MLX, or Foundation Models artifact/runtime. Its endpoint compatibility does not prove model tool use or structured-output behavior.
  • Apple research repositories vary from reusable frameworks to benchmark or paper-reproduction code. Classify the repository before recommending it as infrastructure.

References

Read references/apple-model-tooling.md for the official source map and verification checklist.

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