Glance of Apple foundation model

At WWDC 2025, Apple introduced the Foundation Models framework, offering a powerful, privacy-first Swift API for harnessing large‑language‑model capabilities. With this, developers can integrate features like text generation, guided structured output, tool calling, multi‑turn conversations, and streaming—all running on-device (or privately in the cloud).

Model Variants

• On‑device model: A compact (~3 billion parameter, 2‑bit quantized) LLM optimized for Apple silicon. It handles summarization, classification, extraction, creative text, and short dialogue—without inflating app size or requiring internet access .
• Private Cloud Compute model: A larger model for complex or up-to-date knowledge tasks, securely accessible via Apple’s encrypted private cloud .

System Requirements

• OS: iOS/iPadOS 18, macOS Sequoia (15), visionOS soon .
• Hardware: Apple Intelligence–enabled devices (A17 Pro, M-series) .
• Framework: Use import FoundationModels.
• Privacy: Default on-device. Cloud inference requires user permission and encryption .
• Languages: Supports 15+ languages (initially English, plus Simplified Chinese) .

Example Code

import FoundationModels

let systemModel = SystemLanguageModel.default
guard systemModel.isAvailable else {
    switch systemModel.availability {
    case .available:
        break
    case .unavailable(let reason):
        print("Model unavailable: \(reason)")
        return
    }
}

let model = FoundationModel()
let config = FoundationModel.Configuration(
    task: .textGeneration(prompt: "Describe the advantages of Swift."),
    streaming: true
)

let session = try await model.makeSession(configuration: config)
for try await chunk in session.streamResults() {
    print(chunk.text, terminator: "")
}

✅ This covers environment checks, streaming, and proper API usage.

Advanced Features

🔧 Guided (Structured) Generation

Use the @Generable macro with @Guide annotations to constrain the model’s output to typed Swift data structures. This ensures reliable, structured responses via constrained decoding .

🧩 Tool Calling

Define app-specific tools with the Tool protocol. The model can autonomously invoke functions (e.g., fetch POIs or call APIs), then integrate the tool responses into its output .

🧠 Stateful Sessions

LanguageModelSession maintains conversation context across multiple prompts, tracks transcripts, and handles context limits. Supports instructions separate from prompts to improve reliability and security .

⏱ Streaming Snapshots

Instead of token “deltas,” FoundationModels streams structured “snapshots”—partial objects matching your schema—allowing UI updates as content evolves .

🛠 Debugging & Profiling

Use the new FoundationModels Instruments tool to prewarm sessions, optimize schema inclusion, and measure latency for smoother experience .

⚙️ Fine-tuning via Adapters

Advanced users can train small adapters to add task-specific skills to the ~3B model, compatible via Python toolkit .

Pros & Cons

Summary

Apple’s Foundation Models framework empowers developers to embed intelligent features—like guided structured generation, dynamic tool interactions, and conversational flow—directly into apps, all within a privacy-first, on-device paradigm.