{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/3"}],"enrichment":{"capability":"MLX LM loads, generates text with, fine-tunes, and quantizes large language models on Apple silicon using the MLX framework and Hugging Face Hub integration.","skillfed_tags":["apple-silicon","llm-inference","model-quantization"],"use_cases":["Run open-source LLMs locally on a Mac for chat, text generation, or code completion without cloud APIs.","Quantize and convert models from Hugging Face to MLX format for efficient inference on Apple silicon.","Fine-tune existing models on your own data using low-rank or full fine-tuning with quantized weights.","Build interactive chat applications with persistent context using the Python API or command-line REPL.","Cache long prompts to speed up repeated queries with the same context across multiple generations.","Distribute inference and fine-tuning workloads across multiple devices using mx.distributed."],"what_it_does":"MLX LM is a Python package that brings large language model inference, generation, fine-tuning, and quantization to Apple silicon Macs. It wraps the MLX framework and integrates with Hugging Face Hub to let you download and run thousands of pre-built models with a single command or Python call. The package works both as a command-line tool (mlx_lm.generate, mlx_lm.chat) and as a Python module, supporting streaming generation, custom sampling, prompt caching, and rotating key-value caches for long-context work.\n\nThe package handles model quantization and can upload converted models back to Hugging Face. It supports low-rank and full model fine-tuning, including on quantized models, and offers distributed inference and fine-tuning via mx.distributed. Runtime dependencies include mlx, numpy, transformers, sentencepiece, protobuf, pyyaml, and jinja2\u2014all standard ML and NLP libraries.","worth_installing":"Yes, if you have Apple silicon and want to run LLMs locally. The package is actively maintained, has no known vulnerabilities, installs cleanly, and offers a mature feature set including generation, fine-tuning, quantization, streaming, and caching. The MIT license imposes no restrictions. The main blocker is hardware: you need a Mac with Apple silicon; it will not work on Intel or non-macOS systems."},"id":"mlx-lm","links":{"html":"https://skillfed.io/packages/mlx-lm","md":"https://skillfed.io/packages/mlx-lm.md","pypi":"https://pypi.org/project/mlx-lm/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-04-22","license_spdx":null,"license_treatment":"permissive","name":"mlx-lm","python_support":"supports_current","summary":"LLMs with MLX and the Hugging Face Hub"},"popularity":{"monthly_downloads":1412545,"position":3933,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.31.3"}