Apple Intelligence Foundation Models Framework – Developer Reference Pack

The definitive dataset and developer reference for Apple’s new Foundation Models framework in iOS 26

Overview

The Developer Reference Pack combines LLM fine-tuning data, production-tested Swift implementation, and verified Apple Foundation Models specifications into one cohesive kit.
It is purpose-built to train, fine-tune, or augment AI systems with accurate, on-device Apple AI framework knowledge— while providing developers with working Swift examples for real-world use.

All content was derived from hands-on implementation using Xcode 26 and the Foundation Models framework, ensuring authenticity and technical depth.

What’s Included

1. Fine-Tuning Dataset

File: FoundationModels_FinetuningDataset.jsonl

• 74 prompt–completion pairs for fine-tuning or retrieval augmentation
• Designed to teach models Swift-based reasoning around Apple’s new LanguageModelSession, Tool protocol, and @Generable macros
• Example topics:
  • SystemLanguageModel availability
  • Constrained decoding and schema validation
  • Background inference using BGProcessingTask
  • Adapter training and ToolOutput design

2. Manifest Index

File: FoundationModels_FinetuningManifest.json

• Complete metadata map for dataset traceability
• Includes topic summaries, source file names, and complexity tier
• Ideal for staged fine-tuning, auditing, or curriculum creation

3. Technical Source Files (8 Markdown Specifications)

Covering the full Foundation Models API surface:

• FoundationModels-CoreFramework.md -- LanguageModelSession, system availability, response handling
• FoundationModels-AdvancedImplementation.md -- @Generable, @Guide, constrained decoding, Tool protocol
• FoundationModels-StrategicFeatures.md -- Adapter training toolkit, ToolOutput patterns, Apple’s AI roadmap
• FoundationModels-PerformanceProfiling.md -- Foundation Models Instruments, TTFT, TPS, profiling templates
• FoundationModels-LanguageModelFeedback.md -- LanguageModelFeedbackAttachment, submission workflows
• FoundationModels-PromptEngineering.md -- Instructions vs Prompts, #Playground directive, prompt safety
• FoundationModels-DynamicSchemas.md -- DynamicGenerationSchema, runtime composition, validation
• FoundationModels-BackgroundProcessing.md -- Background task generation, BGProcessingTask, CPU-only mode

4. Production Swift Example

File: FoundationModelsFrameworkGenericRecipeGenerator.swift

• Complete working Swift file demonstrating GenericAIGeneratorService
• Integrates @Generable schema creation, structured generation, and SwiftUI binding
• Production-quality implementation for iOS 26 projects

5. Documentation & Assets

• FoundationModelsDataset_README.md – Setup, usage, and licensing instructions
• Product image and thumbnail – Ready for Gumroad, Shopify, or documentation sites

Why It Matters

Current AI models have zero knowledge of iOS 26 Foundation Models — all public LLMs were trained before Apple introduced this framework.
This dataset bridges that gap, equipping your AI tools with:

• Knowledge of Apple’s on-device LLM architecture
• Working Swift examples for FoundationModels, LanguageModelSession, and DynamicGenerationSchema
• Understanding of @Generable / @Guide macros and Tool integration
• Support for adapter training, structured generation, and background inference

Perfect For

• AI companies building iOS-native development assistants
• Product teams training internal copilots with Apple framework knowledge
• iOS developers exploring on-device LLM integration
• Educators and consultants teaching Apple’s AI frameworks

What Your AI Will Learn

• Generate correct Swift code for Apple Foundation Models
• Implement Tool protocol for extending model functionality
• Build dynamic schemas for guided generation
• Optimize on-device inference performance
• Manage LanguageModelFeedbackAttachment for ethical fine-tuning
• Schedule background CPU-only tasks for model execution

Formatted for immediate ingestion. No preprocessing required.
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