Can I use this for vision models?

Yes, the skill includes support for mlx-vlm to run multimodal models like Qwen2-VL, LLaVA, and Phi-3-Vision on Apple hardware.

Does it support fine-tuning?

Yes, it provides implementation patterns for LoRA and QLoRA fine-tuning with advanced features like gradient checkpointing.

How does 4-bit quantization affect model performance?

It reduces model size by approximately 75%, allowing larger models to fit in memory while maintaining high accuracy for most general tasks.

Does this skill support NVIDIA GPUs?

While primarily optimized for Apple Silicon via Metal, MLX version 0.28+ includes support for Linux CUDA environments.

What are the benefits of Unified Memory?

Unified memory allows the CPU and GPU to share the same memory pool, eliminating slow data transfers and allowing LLMs to use all available RAM.

MLX Apple Silicon

Name: MLX Apple Silicon
Author: plurigrid

byplurigrid

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Data Science & ML

Optimizes LLM performance on Apple M-series chips using the MLX framework for high-efficiency local inference and fine-tuning.

About

The MLX Apple Silicon skill empowers Claude to leverage Apple’s native MLX framework for running, fine-tuning, and converting large language models directly on Mac hardware. By utilizing unified memory architectures, it eliminates GPU-CPU bottlenecks, enabling rapid 4-bit quantization, streaming generation, and speculative decoding. This skill is essential for developers building high-performance local AI applications, providing patterns for LoRA training, multimodal vision support, and efficient memory management on macOS.

Key Features

Multimodal vision-language model integration via mlx-vlm
Unified memory management for zero-copy GPU transfers
2 GitHub stars
LoRA and QLoRA fine-tuning support with gradient accumulation
Advanced 4-bit and 8-bit quantization for efficient model storage
Streaming generation and speculative decoding for low-latency inference

Use Cases

Running Llama, Mistral, and DeepSeek models locally on Mac hardware
Fine-tuning language models using local datasets on M-series chips
Converting Hugging Face models into optimized MLX formats for distribution

About

Key Features

Multimodal vision-language model integration via mlx-vlm
Unified memory management for zero-copy GPU transfers
2 GitHub stars
LoRA and QLoRA fine-tuning support with gradient accumulation
Advanced 4-bit and 8-bit quantization for efficient model storage
Streaming generation and speculative decoding for low-latency inference

Use Cases

Running Llama, Mistral, and DeepSeek models locally on Mac hardware
Fine-tuning language models using local datasets on M-series chips
Converting Hugging Face models into optimized MLX formats for distribution