swift-mlx-lm

You can run LLM inference locally on Apple Silicon by using the MLX Swift framework, which facilitates efficient text generation and streaming directly on your Mac hardware. This supports loading thousands of models from Hugging Face Hub without cloud dependencies.

Yes, you can fine-tune vision-language models (VLMs) on your Mac using the MLX Swift framework. The framework supports LoRA and DoRA fine-tuning to adapt both LLM and VLM architectures to specific tasks, including processing images and video alongside text.

To integrate Hugging Face models into a macOS application, use the MLX Swift framework to load and run thousands of available LLMs locally. This enables features like text summarization and code generation without requiring cloud connectivity.

Yes, MLX Swift supports tool calling and embeddings for local LLMs. It provides comprehensive capabilities for local inference, including streaming generation, tool calling, and generating embeddings across various LLM and VLM architectures.

The best way to port LLM architectures for local inference on Apple hardware is using the MLX Swift framework. It natively supports model porting across LLM and VLM architectures, ensuring efficient execution tailored for Apple Silicon.