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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Fine-tune LLMs on your Mac with Apple Silicon. SFT, DPO, GRPO, Vision, TTS, STT, Embedding, and OCR fine-tuning — natively on MLX. Unsloth-compatible API.
| Date | Stars |
|---|---|
| 2026-07-24 | 1369 |
| 2026-07-25 | 1369 |
| 2026-07-28 | 1375 |
| 2026-07-30 | 1372 |
| 2026-08-06 | 1372 |
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<p align="center"> <img src="https://raw.githubusercontent.com/ARahim3/mlx-tune/main/mlx-tune-logo.png" alt="MLX-Tune Logo" width="300"/> </p> <p align="center"> <strong>Fine-tune LLMs, Vision, Audio, and OCR models on your Mac</strong><br> <em>SFT, DPO, GRPO, Vision, TTS, STT, Embedding, and OCR fine-tuning — natively on MLX. Unsloth-compatible API.</em> </p> <p align="center"> <a href="https://github.com/ARahim3/mlx-tune"><img src="https://img.shields.io/github/stars/arahim3/mlx-tune?style=social" alt="GitHub stars"></a> <a href="https://pepy.tech/projects/mlx-tune"><img src="https://static.pepy.tech/personalized-badge/mlx-tune?period=total&units=INTERNATIONAL_SYSTEM&left_color=GREY&right_color=GREEN&left_text=downloads" alt="PyPI Downloads"></a> <a href="https://github.com/ARahim3/mlx-tune"><img alt="GitHub forks" src="https://img.shields.io/github/forks/arahim3/mlx-tune"></a> <br> <a href="#installation"><img src="https://img.shields.io/badge/Platform-Apple%20Silicon-black?logo=apple" alt="Platform"></a> <a href="#requirements"><img src="https://img.shields.io/badge/Python-3.9+-blue?logo=python&logoColor=white" alt="Python"></a> <a href="https://github.com/ml-explore/mlx"><img src="https://img.shields.io/badge/MLX-0.20+-green" alt="MLX"></a> <a href="#license"><img src="https://img.shields.io/badge/License-Apache%202.0-orange" alt="License"></a> </p> <p align="center"> <a href="https://arahim3.github.io/mlx-tune/">Documentation</a> · <a href="#quick-start">Quick Start</a> · <a href="#supported-training-methods">Training Methods</a> · <a href="#examples">Examples</a> · <a href="#project-status">Status</a> </p> --- > [!NOTE] > **Name Change**: This project was originally called `unsloth-mlx`. Since it's not an official Unsloth project and to avoid any confusion, it has been renamed to `mlx-tune`. The vision remains the same — bringing the Unsloth experience to Mac users via MLX. If you were using `unsloth-mlx`, simply switch to `pip install mlx-tune` and update your imports from `unsloth_mlx` to `mlx_tune`. > [!NOTE] > **Why I Built This (A Personal Note)** > > I rely on Unsloth for my daily fine-tuning on cloud GPUs—it's the gold standard for me. But recently, I started working on a MacBook M4 and hit a friction point: I wanted to prototype locally on my Mac, then scale up to the cloud without rewriting my entire training script. > > Since Unsloth relies on Triton (which Macs don't have, yet), I couldn't use it locally. I built `mlx-tune` to solve this specific "Context Switch" problem. It wraps Apple's native MLX framework in an Unsloth-compatible API. > > **The goal isn't to replace Unsloth or claim superior performance.** The goal is **code portability**: allowing you to write `FastLanguageModel` code once on your Mac, test it, and then push that *exact same script* to a CUDA cluster. It solves a workflow problem, not just a hardware one. > > This is an "unofficial" project built by a fan, for fans who happen to use Macs. It's helping me personally, and if it helps others like me, then I'll have my satisfaction. ## Why MLX-Tune? Bringing the [Unsloth](https://github.com/unslothai/unsloth) experience to Mac users via Apple's [MLX](https://github.com/ml-explore/mlx) framework. - 🚀 **Fine-tune LLMs, VLMs, TTS, STT & Embeddings** locally on your Mac (M1/M2/M3/M4/M5) - 💾 **Leverage unified memory** (up to 512GB on Mac Studio) - 🔄 **Unsloth-compatible API** - your existing training scripts just work! - 📦 **Export anywhere** - HuggingFace format, GGUF for Ollama/llama.cpp - 🎙️ **Audio fine-tuning** - 5 TTS models (Orpheus, OuteTTS, Spark, Sesame, Qwen3-TTS) + 7 STT models (Whisper, Moonshine, Qwen3-ASR, NVIDIA Canary, Voxtral, Voxtral Realtime, **NVIDIA Parakeet TDT**) ```python # Unsloth (CUDA) # MLX-Tune (Apple Silicon) from unsloth import FastLanguageModel from mlx_tune import FastLanguageModel from trl import SFTTrainer from mlx_tune import SFTT
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:2bb6a01b39184e1b, topic:speech-recognition, topic:text-to-speech, topic:whisper
matched fp:2bb6a01b39184e1b, topic:lora, topic:peft, desc:fine-tuning
matched fp:2bb6a01b39184e1b, topic:large-language-models, topic:llm