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How to use MLX LM: what you can do with it

What is MLX LM? MLX LM is a Python package for generating text and fine-tuning large language models on Apple silicon with MLX. It runs thousands of models from the Hugging Face Hub with a single command, quantizes and uploads models, supports low-rank and full fine-tuning including quantized models, distributed inference and fine-tuning with mx.distributed, and tools for long prompts such as a rotating KV cache and prompt caching.

How do I use it? Install with pip install mlx-lm or conda install -c conda-forge mlx-lm; then generate text with mlx_lm.generate --prompt "How tall is Mt Everest?"; start a chat REPL that keeps context for its lifetime with mlx_lm.chat. Full steps below.

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What do you want to do?

3 use cases

Quick start with MLX LM

  1. Check you have what it needs: A Mac with Apple silicon; Python with pip or conda; macOS 15 or later to speed up models that are large relative to RAM.
  2. Open ml-explore/mlx-lm on GitHub and read the README and license.
  3. Install with pip install mlx-lm or conda install -c conda-forge mlx-lm
  4. Generate text with mlx_lm.generate --prompt "How tall is Mt Everest?"
  5. Start a chat REPL that keeps context for its lifetime with mlx_lm.chat
  6. Pick another model with --model, for example mlx_lm.generate --model mistralai/Mistral-7B-Instruct-v0.3 --prompt "hello"

These steps come from our top use case, Chat with an open LLM locally on your Mac, which also has a copy-paste agent prompt.

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About this repo

  • Who it is forMac users and developers who want to run, quantize or fine-tune open LLMs locally on Apple silicon.
  • Why it is hot7.5k stars, talked about in 2 AI sources we track
  • LanguagePython
  • Sourcegithub.com/ml-explore/mlx-lm ยท Back to Hot repos

Requirements

  • A Mac with Apple silicon
  • Python with pip or conda
  • macOS 15 or later to speed up models that are large relative to RAM
  • Optional: a Hugging Face account to upload converted models

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Questions about MLX LM

What is MLX LM?

MLX LM is a Python package for generating text and fine-tuning large language models on Apple silicon with MLX. It runs thousands of models from the Hugging Face Hub with a single command, quantizes and uploads models, supports low-rank and full fine-tuning including quantized models, distributed inference and fine-tuning with mx.distributed, and tools for long prompts such as a rotating KV cache and prompt caching. It is an open-source project on GitHub: https://github.com/ml-explore/mlx-lm.

How do I use MLX LM?

Install with pip install mlx-lm or conda install -c conda-forge mlx-lm; then generate text with mlx_lm.generate --prompt "How tall is Mt Everest?"; start a chat REPL that keeps context for its lifetime with mlx_lm.chat. The use cases on this page give the full steps and a copy-paste agent prompt for each job.

Who is MLX LM for?

Mac users and developers who want to run, quantize or fine-tune open LLMs locally on Apple silicon.

What can I do with MLX LM?

3 use cases so far, for example: Chat with an open LLM locally on your Mac; Quantize a model and publish it for MLX; Ask many questions about one long document quickly. Each has steps and a copy-paste agent prompt.

What do I need to run MLX LM?

A Mac with Apple silicon; Python with pip or conda; macOS 15 or later to speed up models that are large relative to RAM; Optional: a Hugging Face account to upload converted models.

What are alternatives to MLX LM?

Other hot AI repos in Software and DevOps: ECC, Thinkrail, Superpowers, Deep Infer, Instructor.

Is MLX LM safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.