Comparison
mlx-tune vs SimpleTuner
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.
Markdown twin · mlx-tune alternatives · SimpleTuner alternatives
GraphCanon updated 2d
vs
Trust & integrity
| Signal | mlx-tune | SimpleTuner |
|---|---|---|
| Maintenance | Steady (36d since push) As of 3w · github_public_v1 | Very active (0d since push) As of 2d · github_public_v1 |
| Provenance | Not a fork · Personal account As of 3w · github_public_v1 | Not a fork · Personal account As of 2d · github_public_v1 |
| OSV dependency advisories | Published findings As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- 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
Stars
- mlx-tune
- 1.4k
- SimpleTuner
- 2.9k
Forks
- mlx-tune
- 88
- SimpleTuner
- 289
Open issues
- mlx-tune
- 11
- SimpleTuner
- 5
Language
- mlx-tune
- Python
- SimpleTuner
- Python
Adopt for
- mlx-tune
- 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
- 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
- mlx-tune
- -
- SimpleTuner
- -
Runtime
- mlx-tune
- -
- SimpleTuner
- -
License
- mlx-tune
- Apache-2.0
- SimpleTuner
- 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.
Last pushed
- mlx-tune
- Jun 23, 2026
- SimpleTuner
- Aug 23, 2026
Categories
- mlx-tune
- Computer Vision, LLM Frameworks, Model Training, Speech & Audio
- SimpleTuner
- Computer Vision, Model Training
Trust and health
Maintenance
- mlx-tune
- Steady (60%)
- SimpleTuner
- Very active (96%)
Days since push
- mlx-tune
- 36d
- SimpleTuner
- 0d
Open issues (now)
- mlx-tune
- 11
- SimpleTuner
- 5
Stars delta
- mlx-tune
- Unknown
- SimpleTuner
- +21 (30d)
Open issues delta
- mlx-tune
- Unknown
- SimpleTuner
- -8 (30d)
OSV dependency advisories
- mlx-tune
- Published findings
- SimpleTuner
- No lockfile (source not queried)
Full report
- mlx-tune
- Trust report
- SimpleTuner
- Trust report
Shared compatibility
- Python · mlx-tune: Python runtime · SimpleTuner: Python runtime
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
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
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (ARahim3/mlx-tune) · observed Jul 30, 2026
- GitHub forks (ARahim3/mlx-tune) · observed Jul 30, 2026
- Last push (ARahim3/mlx-tune) · observed Jun 23, 2026
- License file (Apache-2.0) · observed Jul 30, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (bghira/SimpleTuner) · observed Aug 23, 2026
- GitHub forks (bghira/SimpleTuner) · observed Aug 23, 2026
- Last push (bghira/SimpleTuner) · observed Aug 23, 2026
- License file (AGPL-3.0) · observed Aug 23, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: mlx-tune 1.4k · SimpleTuner 2.9k (synced Jul 30, 2026).
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 and SimpleTuner alternatives (mlx-tune markdown twin, SimpleTuner markdown twin), 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 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; SimpleTuner trust report.