apple-on-device-ailisted

Integrate on-device AI using Foundation Models framework, Core ML, and open-source LLM runtimes on Apple Silicon. Covers Foundation Models (LanguageModelSession, @Generable, @Guide, SystemLanguageModel, structured output, tool calling), Core ML (coremltools, model conversion, quantization, palettization, pruning, Neural Engine, MLTensor), MLX Swift (transformer inference, unified memory), and llama.cpp (GGUF, cross-platform LLM). Use when building tool-calling AI features, working with guided generation schemas, converting models, or running on-device inference.
dpearson2699/swift-ios-skills · ★ 640 · AI & Automation · score 81

Install: claude install-skill dpearson2699/swift-ios-skills

# On-Device AI for Apple Platforms Guide for selecting, deploying, and optimizing on-device ML models. Covers Apple Foundation Models, Core ML, MLX Swift, and llama.cpp. ## Contents - [Framework Selection Router](#framework-selection-router) - [Apple Foundation Models Overview](#apple-foundation-models-overview) - [Core ML Overview](#core-ml-overview) - [MLX Swift Overview](#mlx-swift-overview) - [Multi-Backend Architecture](#multi-backend-architecture) - [Performance Best Practices](#performance-best-practices) - [Common Mistakes](#common-mistakes) - [Review Checklist](#review-checklist) - [References](#references) ## Framework Selection Router Use this decision tree to pick the right framework for your use case. ### Apple Foundation Models **When to use:** Text generation, summarization, entity extraction, structured output, and short dialog on iOS 26+ / macOS 26+ devices with Apple Intelligence enabled. Zero setup -- no API keys, no network, no model downloads. **Best for:** - Generating text or structured data with `@Generable` types - Summarization, classification, content tagging - Tool-augmented generation with the `Tool` protocol - Apps that need guaranteed on-device privacy **Not suited for:** Complex math, code generation, factual accuracy tasks, or apps targeting pre-iOS 26 devices. ### Core ML **When to use:** Deploying custom trained models (vision, NLP, audio) across all Apple platforms. Converting models from PyTorch, TensorFlow, or scikit-learn with