mlx-vlm and SiLLM
MLX-VLM extends MLX's capabilities to multimodal vision-language tasks, while SiLLM provides a higher-level abstraction layer for training and deploying text-only LLMs on the same MLX foundation, making them complementary tools serving different model types within the Apple Silicon ecosystem.
About mlx-vlm
Blaizzy/mlx-vlm
MLX-VLM is a package for inference and fine-tuning of Vision Language Models (VLMs) on your Mac using MLX.
This project helps you understand images, audio, and video content by describing or answering questions about them. You provide a visual, audio, or multi-modal input and a question or prompt, and the tool generates a textual response. It's designed for anyone working with multimedia content on a Mac who needs to extract information or generate descriptions.
About SiLLM
armbues/SiLLM
SiLLM simplifies the process of training and running Large Language Models (LLMs) on Apple Silicon by leveraging the MLX framework.
This tool helps researchers, AI developers, and data scientists leverage their Apple Silicon Mac for advanced work with Large Language Models (LLMs). You can take existing LLMs or datasets and train them further using methods like LoRA or DPO, or use them for chat and experimentation. It simplifies running and fine-tuning these models directly on your Mac, rather than needing cloud services or specialized hardware.
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