swift-mlx-lm

What This Skill Does

The swift-mlx-lm skill integrates the power of the MLX machine learning framework into OpenClaw, enabling high-performance inference, fine-tuning, and embedding generation directly on Apple Silicon. This skill provides a specialized wrapper around the mlx-swift-lm package, abstracting the complexities of model loading, memory management, and stream handling. By leveraging the Unified Memory Architecture (UMA) of Apple Silicon, it allows users to run state-of-the-art Large Language Models (LLMs) and Vision-Language Models (VLMs) with significant efficiency. Key features include model containerization, streaming chat sessions, tool/function calling, LoRA fine-tuning for custom model adaptation, and semantic embedding generation.

Installation

To integrate this skill into your environment, use the OpenClaw CLI: clawhub install openclaw/skills/skills/ronaldmannak/mlx-swift-lm

Once installed, ensure your environment has the necessary Apple Silicon hardware requirements and that the Swift toolchain is correctly configured to support the library's underlying C++ dependencies.

Use Cases

• Local AI Agents: Run private, offline LLMs for data-sensitive tasks without external API dependencies.
• Vision Analysis: Automatically describe, classify, or extract information from images and video sequences using VLM architectures like Qwen2-VL.
• Fine-Tuning: Use LoRA or DoRA adapters to specialize base models for domain-specific tasks or private data formats.
• RAG Implementation: Utilize the embedder library to convert large document sets into high-dimensional vectors for local vector search and retrieval-augmented generation.
• Tool Orchestration: Utilize the tool-calling framework to allow the model to interact with local system APIs and OpenClaw commands.

Example Prompts

1. "Load the Qwen3-4B model and stream a detailed explanation of Apple's Memory Management, then summarize the key takeaways at the end."
2. "Analyze this image of a system architecture diagram and provide a list of all technical components found within the chart using the VLM skill."
3. "Fine-tune the local model with the LoRA adapter provided in my documents folder to prioritize Swift-specific coding patterns for my current project."

Tips & Limitations

• Memory Constraints: Ensure you have enough unified memory to accommodate the model weights and the KV cache. Quantized models (4-bit) are highly recommended for devices with less than 32GB of RAM.
• Hardware: This skill is strictly designed for Apple Silicon (M-series chips). It will not function on Intel-based Macs or other hardware architectures.
• Caching: For repeated tasks, utilize the KVCache to optimize performance, though be mindful of memory usage as the cache grows during long conversations.
• Security: Since models run locally, you maintain total control over your data; however, ensure you have sufficient disk space for downloading model weights from HuggingFace.