mlx-tune

mlx-tune is a speech (TTS/STT) tool - 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.

Overview

Fine-tune LLMs, Vision, Audio, and OCR models on your Mac SFT, DPO, GRPO, Vision, TTS, STT, Embedding, and OCR fine-tuning — natively on MLX. Unsloth-compatible API.

Bringing the Unsloth experience to Mac users via Apple's 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)

This is NOT a replacement for Unsloth or an attempt to compete with it. Unsloth is incredible - it's the gold standard for efficient LLM fine-tuning on CUDA.

Fine-tune vision-language models like Gemma 4, Qwen3.5 on image+text tasks:

See 'examples/38_gemma4_vision_finetuning.py' for Gemma 4 vision fine-tuning, 'examples/39_gemma4_text_to_sql.py' for text-only fine-tuning through the VLM path, 'examples/10_qwen35_vision_finetuning.py' for Qwen3.5, or 'examples/26_vision_grpo_training.py' for Vision GRPO reasoning.

Fine-tune Gemma 4 E2B/E4B for speech-to-text and audio understanding. The 12-layer Conformer audio tower processes 16kHz audio — no separate STT model needed:

See 'examples/47_gemma4_audio_asr_finetuning.py' for ASR fine-tuning or 'examples/48_gemma4_audio_understanding.py' for audio understanding with audio tower LoRA.

mlx-tune is open-source, written primarily in Python, with 1,279 GitHub stars under the Apache 2.0 license. The latest release is v0.5.1 (2026-05-31).

Key capabilities

From the project's documentation:

• 🚀 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
• 🧪 Prototype locally - Experiment with fine-tuning before committing to cloud GPU costs
• 📚 Learn & iterate - Develop your training pipeline with fast local feedback loops

Install

A quick way to get started (always check the official docs for the latest):

pip install mlx-tune

How it fits a local-AI stack

mlx-tune runs on your own hardware, so pair it with a model and a GPU sized to your needs. Use the VRAM calculator to pick a model that fits your card, and see what you can run for hardware guidance. Related speech (TTS/STT) tools in the directory:

• Kokoro TTS
• Miso One / MisoTTS
• Piper TTS
• Sherpa Onnx

Sources

• Source code & docs: ARahim3/mlx-tune
• Official website: https://arahim3.github.io/mlx-tune/

Stats from GitHub, 2026-06-08.