EdgeLLM v0.2.0 - Real MLC-LLM Integration

🚀 EdgeLLM v0.2.0 - Real On-Device LLM Inference

✨ New Features

• Real MLC-LLM Integration: Complete 60MB XCFramework with all necessary libraries
• One-Line API: EdgeLLM.chat("Hello\!") now runs actual Qwen3-0.6B on-device
• Metal GPU Acceleration: Full iOS hardware acceleration support
• Binary Distribution: Easy Swift Package Manager installation

📦 What's Included

• MLCRuntime.xcframework: Unified 60MB framework with:
  • libmlc_llm.a (8.8MB) - Core inference engine
  • libmodel_iphone.a (15MB) - iOS optimizations
  • libtvm_runtime.a (8.5MB) - TVM execution
  • Tokenizer libraries (26MB+)
• 3 Pre-configured Models: Qwen3-0.6B, Gemma-2B, Phi-3.5-mini
• Example iOS App: SimpleChat with real inference

🎯 Usage

import EdgeLLM

let response = try await EdgeLLM.chat("Hello, world\!")
print(response) // Real on-device LLM response\!

📱 Requirements

• iOS 14.0+
• iPhone 12+ (Neural Engine recommended)
• 4GB+ RAM for larger models

🔧 Installation

Add to Package.swift:

.package(url: "https://github.com/john-rocky/EdgeLLM", from: "0.2.0")

Models download automatically on first use (~1-3GB)