{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/4"}],"enrichment":{"capability":"MLX-VLM runs vision language models and omni models (with audio and video support) for inference and fine-tuning on Mac hardware using MLX, with CLI, Python, FastAPI server, and Gradio UI interfaces.","skillfed_tags":["mac-native","multimodal-inference","quantization"],"use_cases":["Run vision-language inference locally on Mac via CLI or Python without cloud API calls.","Deploy a multimodal model as a local FastAPI service with continuous batching and KV cache optimization.","Build an interactive chat interface with Gradio for image, audio, and text input on a single machine.","Fine-tune a vision language model on your own Mac hardware for domain-specific tasks.","Process images and audio together in a single inference call using omni models."],"what_it_does":"MLX-VLM is a framework for running vision language models on Apple Silicon Macs using the MLX framework. It supports inference and fine-tuning of models that combine vision, language, and optionally audio and video inputs. The package provides multiple interfaces: a command-line tool for quick inference, a Python API for programmatic use, a FastAPI server for deployment with features like continuous batching and KV cache optimization, and a Gradio web UI for interactive chat.\n\nThe package is built on a stack of 15 runtime dependencies including transformers for model loading, FastAPI and Uvicorn for the server, Pillow and OpenCV for image processing, and miniaudio for audio support. It targets developers working on Mac hardware who want to run or fine-tune multimodal models without cloud infrastructure. The framework includes quantization support, speculative decoding, and model-specific documentation for OCR and reasoning models.","worth_installing":"Yes. MLX-VLM is actively maintained, has no known vulnerabilities, uses a permissive MIT license, and offers low-friction installation. It fills a clear niche for developers with Mac hardware who want local multimodal inference without cloud dependencies. The Beta status and broad feature set (CLI, server, fine-tuning, multiple model architectures) suggest a maturing project worth adopting for Mac-based ML workflows."},"id":"mlx-vlm","links":{"html":"https://skillfed.io/packages/mlx-vlm","md":"https://skillfed.io/packages/mlx-vlm.md","pypi":"https://pypi.org/project/mlx-vlm/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-12","license_spdx":null,"license_treatment":"permissive","name":"mlx-vlm","python_support":"supports_current","summary":"MLX-VLM is a package for inference and fine-tuning of Vision Language Models (VLMs) and Omni Models (VLMs with audio and video support) on your Mac using MLX."},"popularity":{"monthly_downloads":959074,"position":4639,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.6.13"}