mlx-tune
Fine-tune LLMs on your Mac with Apple Silicon for various tasks including SFT, DPO, GRPO, Vision, TTS, STT, Embedding, and OCR.
GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026
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Decision brief
mlx-tune targets Mac users with Apple Silicon for fine-tuning LLMs across SFT, RLHP, GRPO, vision, TTS, STT, embeddings, and OCR using tools compatible with the UnSloth API.
Good fit when
- You need to fine-tune large language models on a Mac with Apple Silicon hardware
- You aim to work specifically with tasks like SFT, RLHP, GRPO, vision-based, TTS, STT, embeddings generation, or OCR
Avoid when
- Your development environment is not based on macOS running on Apple Silicon
- The specific tasks you are targeting do not align with the capabilities of mlx-tune such as those exclusive to alternative platforms or tools
Observed Jul 14, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Steady (88d since push)
- As of Sep 20, 2026
- Provenance
- Not a fork · Personal account
- As of Sep 20, 2026
- Security (OSV)
- 46 low (46 low)
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install mlx-tuneSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
mlx-tune offers tools to fine-tune large language models using Apple Silicon. Supports a wide range of tasks such as supervised fine-tuning (SFT), reinforcement learning from human preferences (RLHP), generative pre-trained transformer (GPT) retraining on prompt optimization (GRPO), vision, text-to-speech (TTS), speech-to-text (STT), embeddings generation, and optical character recognition (OCR). Framework is compatible with the UnSloth API.
Capability facts
- Languages
- python
Source: github.language+pyproject.toml · Sep 20, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Sep 20, 2026)
```python from mlx_tune import FastLanguageModel, SFTTrainer, SFTConfigSource link
Tags
README
Quick Start ```python from mlx tune import FastLanguageModel, SFTTrainer, SFTConfig from datasets import load dataset Load any HuggingFace model (1B model for quick start) model, tokenizer = FastLanguageModel.from pretrained( model name="mlx community/Llama 3.2 1B Instruct 4bit",...
For agents
This page has a .md twin and JSON over the API.