--- title: "mlx-tune vs SimpleTuner" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/arahim3-mlx-tune-vs-bghira-simpletuner" tools: ["arahim3-mlx-tune", "bghira-simpletuner"] --- # mlx-tune vs SimpleTuner *GraphCanon updated Aug 23, 2026* ## Verdict Pick mlx-tune if 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; pick SimpleTuner if simpleTuner is a Python-based tool for fine-tuning diffusion models used in machine learning tasks such as image, video, and audio processing. It offers utilities and scripts to. [mlx-tune](https://arahim3.github.io/mlx-tune/) reports 1.4k GitHub stars, 88 forks, and 11 open issues, last pushed Jun 23, 2026. [SimpleTuner](https://github.com/bghira/SimpleTuner) has 2.9k stars, 289 forks, and 5 open issues, last pushed Aug 23, 2026. Figures are from public GitHub metadata via [mlx-tune's repository](https://github.com/ARahim3/mlx-tune) and [SimpleTuner's repository](https://github.com/bghira/SimpleTuner). | | [mlx-tune](/tools/arahim3-mlx-tune.md) | [SimpleTuner](/tools/bghira-simpletuner.md) | | --- | --- | --- | | Tagline | Fine-tune LLMs on your Mac with Apple Silicon for various tasks including SFT, DPO, GRPO, Vision, TTS, STT, Embedding, and OCR. | A Python-based general fine-tuning kit for image/video/audio diffusion models | | Stars | 1,372 | 2,906 | | Forks | 88 | 289 | | Open issues | 11 | 5 | | Language | Python | Python | | Adopt for | 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. | SimpleTuner is a Python-based tool for fine-tuning diffusion models used in machine learning tasks such as image, video, and audio processing. It offers utilities and scripts to streamline the process. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | The AGPL-3.0 license ensures the source code is available and permits free alteration of the software but may require derivative works to also be distributed under this license. | | Categories | Computer Vision, LLM Frameworks, Model Training, Speech & Audio | Computer Vision, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [mlx-tune](/tools/arahim3-mlx-tune.md) | [SimpleTuner](/tools/bghira-simpletuner.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 36d | 0d | | Open issues (now) | 11 | 5 | | Stars delta | Unknown | +21 (30d) | | Open issues delta | Unknown | -8 (30d) | | Full report | [trust report](/tools/arahim3-mlx-tune/trust.md) | [trust report](/tools/bghira-simpletuner/trust.md) | ## Shared compatibility - **Python**: [mlx-tune](/tools/arahim3-mlx-tune.md) - Python runtime; [SimpleTuner](/tools/bghira-simpletuner.md) - Python runtime ## Decision facts: mlx-tune - **Adopt for:** 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. ## Decision facts: SimpleTuner - **Requirements:** SimpleTuner does not have a stated requirement for Docker, making deployment more flexible. - **Adopt for:** SimpleTuner is a Python-based tool for fine-tuning diffusion models used in machine learning tasks such as image, video, and audio processing. It offers utilities and scripts to streamline the process. - **License detail:** The AGPL-3.0 license ensures the source code is available and permits free alteration of the software but may require derivative works to also be distributed under this license. ## Choose when ### Choose mlx-tune if… - License: mlx-tune is Apache-2.0, SimpleTuner is AGPL-3.0. - Tags unique to mlx-tune: apple-silicon, deep-learning, huggingface, large language models. - Also covers LLM Frameworks, Speech & Audio. - You need to fine-tune large language models on a Mac with Apple Silicon hardware ### Choose SimpleTuner if… - License: SimpleTuner is AGPL-3.0, mlx-tune is Apache-2.0. - Requirements: SimpleTuner does not have a stated requirement for Docker, making deployment more flexible.. - Tags unique to SimpleTuner: diffusers, diffusion-models, fine-tuning, flux-dev. - SimpleTuner ships Docker support for self-hosted deployment. - Use SimpleTuner when you need specialized fine-tuning capabilities for diffusion models involving image, video, or audio data. ## When NOT to use mlx-tune - 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 ## When NOT to use SimpleTuner - Do not use SimpleTuner if your project requires proprietary licensing, since it is released under AGPL-3.0 which may impose conditions that could be incompatible with commercial projects. - Avoid SimpleTuner for tasks unrelated to diffusion models such as natural language processing, as it was designed specifically for image, video, and audio data. ## Common questions ### What is the difference between mlx-tune and SimpleTuner? mlx-tune: Fine-tune LLMs on your Mac with Apple Silicon for various tasks including SFT, DPO, GRPO, Vision, TTS, STT, Embedding, and OCR.. SimpleTuner: A Python-based general fine-tuning kit for image/video/audio diffusion models. See the comparison table for live GitHub stats and shared categories. ### When should I choose mlx-tune over SimpleTuner? Choose mlx-tune over SimpleTuner when License: mlx-tune is Apache-2.0, SimpleTuner is AGPL-3.0; Tags unique to mlx-tune: apple-silicon, deep-learning, huggingface, large language models; Also covers LLM Frameworks, Speech & Audio; You need to fine-tune large language models on a Mac with Apple Silicon hardware. ### When should I choose SimpleTuner over mlx-tune? Choose SimpleTuner over mlx-tune when License: SimpleTuner is AGPL-3.0, mlx-tune is Apache-2.0; Requirements: SimpleTuner does not have a stated requirement for Docker, making deployment more flexible.; Tags unique to SimpleTuner: diffusers, diffusion-models, fine-tuning, flux-dev; SimpleTuner ships Docker support for self-hosted deployment; Use SimpleTuner when you need specialized fine-tuning capabilities for diffusion models involving image, video, or audio data. ### When should I avoid mlx-tune? 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 ### When should I avoid SimpleTuner? Do not use SimpleTuner if your project requires proprietary licensing, since it is released under AGPL-3.0 which may impose conditions that could be incompatible with commercial projects. Avoid SimpleTuner for tasks unrelated to diffusion models such as natural language processing, as it was designed specifically for image, video, and audio data. ### Is mlx-tune or SimpleTuner more popular on GitHub? SimpleTuner has more GitHub stars (2,906 vs 1,372). Stars measure visibility, not whether either tool fits your constraints. ### Are mlx-tune and SimpleTuner open source? Yes - both are open-source projects on GitHub (mlx-tune: Apache-2.0, SimpleTuner: AGPL-3.0). ### Where can I find alternatives to mlx-tune or SimpleTuner? GraphCanon lists graph-backed alternatives at [mlx-tune alternatives](/tools/arahim3-mlx-tune/alternatives) and [SimpleTuner alternatives](/tools/bghira-simpletuner/alternatives) ([mlx-tune markdown twin](/tools/arahim3-mlx-tune/alternatives.md), [SimpleTuner markdown twin](/tools/bghira-simpletuner/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/arahim3-mlx-tune-vs-bghira-simpletuner.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, mlx-tune or SimpleTuner? mlx-tune: Steady. SimpleTuner: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for mlx-tune and SimpleTuner? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [mlx-tune trust report](/tools/arahim3-mlx-tune/trust); [SimpleTuner trust report](/tools/bghira-simpletuner/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=arahim3-mlx-tune`](/api/graphcanon/graph?tool=arahim3-mlx-tune) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_