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ModelGarden is a Swift library and application based on Apple's MLX framework, enabling developers to run large language models (LLMs) and vision-language models (VLMs) locally on macOS and iOS devices, with AI inference achievable without an internet connection.
Section 01
ModelGarden is a Swift library and application based on Apple's MLX framework, enabling developers to run large language models (LLMs) and vision-language models (VLMs) locally on macOS and iOS devices, with AI inference achievable without an internet connection.
Section 02
ModelGarden is built on Apple's MLX framework, which is a high-performance computing framework designed by Apple specifically for machine learning, capable of fully leveraging the GPU acceleration of Apple Silicon chips. This project is not just a demo app; it's a reusable Swift library (ModelGardenKit) plus a fully functional SwiftUI app (ModelGardenApp), providing developers with a complete toolchain from underlying inference to upper-layer UI. The advantage of this architectural design is that developers can either directly use the provided sample app to quickly experience local AI capabilities or integrate ModelGardenKit into their own apps to implement customized AI features.
Section 03
ModelGarden's tech stack revolves around the MLX framework, offering the following core capabilities:
Section 04
The project uses mlx-swift-lm as the underlying inference engine; all models run entirely on the device without requiring an internet connection (except for the first-time model download). This brings significant privacy advantages—user conversation data never leaves the device.
Section 05
ModelGarden supports real-time token streaming output; users can see the model-generated content instantly instead of waiting for a complete response. Additionally, the system displays the generation speed (tokens per second) in real time to help developers evaluate model performance.
Section 06
In addition to text models, ModelGarden also supports vision-language models (VLMs), allowing users to upload images and have the model describe, analyze, or answer questions about them. This is of great significance for implementing multimodal AI on mobile devices.
Section 07
Considering the memory constraints of mobile devices, ModelGarden uses 4-bit quantization technology to significantly reduce the model's memory footprint. Additionally, the system provides automatic GPU memory management and supports manual model unloading to free up resources.
Section 08
ModelGarden comes with 13 optimized models covering different scales and use cases:
Lightweight Text Models (Suitable for Mobile Devices):
Medium-Scale Models (Balancing Performance and Resources):
Vision-Language Models:
All models use 4-bit quantization to maximize memory efficiency while ensuring usability.
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