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Mlx Tune

Mlx Tune is a Python developer tool that uses Apple’s MLX framework to fine-tune language, vision, audio, embedding and OCR models on Apple Silicon Macs. It provides training interfaces for methods including SFT, DPO and GRPO, and describes its API as compatible with Unsloth’s interface.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Mlx Tune — OpenViST project profile in AI Developer Tools & SDKs.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you want to develop or iterate on a model fine-tuning workflow on an Apple Silicon Mac. The project describes local work as a prototyping stage that can precede larger runs on cloud NVIDIA GPUs.

Who it is for

  • Developers training or adapting models on Apple Silicon Macs.
  • Practitioners who want to prototype fine-tuning locally before moving a workflow to cloud GPUs.
  • Teams working with language, vision, audio, embedding or OCR models.

Documented uses

  • Fine-tune a language model using supervised fine-tuning, preference optimization or related supported methods.
  • Add LoRA adapters to a model and train them against a dataset.
  • Adapt vision, speech, audio, embedding or OCR models in a local MLX workflow.
  • Save trained adapters, merged models or supported exports for later use.
Before you choose

Requirements & limits

The repository is public and licensed under Apache-2.0. The documented requirements include Apple Silicon, Python 3.9 or later and MLX 0.20 or later.

  • The tool targets Apple Silicon and MLX; the documented platform is not a general-purpose cross-platform training setup.
  • Audio fine-tuning has an optional audio installation and an FFmpeg system dependency for codecs.
  • The repository distinguishes local prototyping from larger cloud-GPU training.
  • GGUF export has a documented limitation for quantized base models.
  • The project offers a Python API and example training code rather than a hosted training service.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up and run a language-model SFT example

Install Mlx Tune, train a LoRA adapter on a Hugging Face language model and dataset, then save the result.

Before you start

  • Apple Silicon Mac
  • Python 3.9 or later
  • MLX 0.20 or later
  • Mlx Tune installed, plus the Python datasets package used in the example
  • A Hugging Face model and dataset, or equivalent available data

Steps

  1. Install with the command below or the command below.

    Documented command
    uv pip install mlx-tune
    Documented command
    pip install mlx-tune

  2. Load a model and tokenizer with FastLanguageModel.from_pretrained, then add LoRA adapters with get_peft_model.

  3. Load a dataset and configure SFTTrainer and SFTConfig with the model, tokenizer and dataset.

  4. Call trainer.train() and save adapters, a merged model or an export in a supported format.

Expected outcome

A locally fine-tuned language model or saved LoRA adapter.

Guide limitations

  • The quick-start example uses a small dataset slice and a 1B model; it does not establish training time or suitability for a particular Mac.
  • GGUF export is documented for non-quantized base models; quantized bases have limitations and require a workaround.
  • The documented workflow is an example, not a guarantee of compatibility with every model or dataset.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
12 October 2026
Original website title
ARahim3/mlx-tune · Official homepage title · retained in its source language
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