Comparison
mlx-tune vs lightly-train
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 lightly-train if lightly-train is a Python-based framework focused on training vision models including YOLO, ViTs, RT-DETR, and DINOv3, offering comprehensive features like pretraining, fine-tuning, and distillation.
Markdown twin · mlx-tune alternatives · lightly-train alternatives
GraphCanon updated 2d
vs
Trust & integrity
| Signal | mlx-tune | lightly-train |
|---|---|---|
| Maintenance | Steady (36d since push) As of 3w · github_public_v1 | Active (7d since push) As of 2d · github_public_v1 |
| Provenance | Not a fork · Personal account As of 3w · github_public_v1 | Not a fork · Organization 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.
- lightly-train
- All-in-one training for vision models: pretraining, fine-tuning, distillation.
Stars
- mlx-tune
- 1.4k
- lightly-train
- 1.6k
Forks
- mlx-tune
- 88
- lightly-train
- 107
Open issues
- mlx-tune
- 11
- lightly-train
- 68
Language
- mlx-tune
- Python
- lightly-train
- 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.
- lightly-train
- Lightly-train is a Python-based framework focused on training vision models including YOLO, ViTs, RT-DETR, and DINOv3, offering comprehensive features like pretraining, fine-tuning, and distillation.
Persona
- mlx-tune
- -
- lightly-train
- -
Runtime
- mlx-tune
- -
- lightly-train
- -
License
- mlx-tune
- Apache-2.0
- lightly-train
- AGPL-3.0
Last pushed
- mlx-tune
- Jun 23, 2026
- lightly-train
- Aug 14, 2026
Categories
- mlx-tune
- Computer Vision, LLM Frameworks, Model Training, Speech & Audio
- lightly-train
- Computer Vision, Model Training
Trust and health
Maintenance
- mlx-tune
- Steady (60%)
- lightly-train
- Active (82%)
Days since push
- mlx-tune
- 36d
- lightly-train
- 7d
Open issues (now)
- mlx-tune
- 11
- lightly-train
- 68
Stars delta
- mlx-tune
- Unknown
- lightly-train
- +28 (30d)
Open issues delta
- mlx-tune
- Unknown
- lightly-train
- +5 (30d)
Owner type
- mlx-tune
- User
- lightly-train
- Organization
OSV dependency advisories
- mlx-tune
- Published findings
- lightly-train
- No lockfile (source not queried)
Full report
- mlx-tune
- Trust report
- lightly-train
- Trust report
Shared compatibility
- Python · mlx-tune: Python runtime · lightly-train: Python runtime
Choose mlx-tune if…
- License: mlx-tune is Apache-2.0, lightly-train is AGPL-3.0.
- Tags unique to mlx-tune: apple-silicon, huggingface, large language models, llm.
- 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 lightly-train if…
- License: lightly-train is AGPL-3.0, mlx-tune is Apache-2.0.
- Requirements: Min 8 GB RAM.
- Tags unique to lightly-train: computer-vision, contrastive-learning, depth-estimation, dinov2.
- Lightly-train is a Python-based framework focused on training vision models including YOLO, ViTs, RT-DETR, and DINOv3, offering comprehensive features like pretraining, fine-tuning, and distillation.
When NOT to use lightly-train
- Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
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 (lightly-ai/lightly-train) · observed Aug 22, 2026
- GitHub forks (lightly-ai/lightly-train) · observed Aug 22, 2026
- Last push (lightly-ai/lightly-train) · observed Aug 14, 2026
- License file (AGPL-3.0) · observed Aug 22, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: mlx-tune 1.4k · lightly-train 1.6k (synced Jul 30, 2026).
Common questions
- What is the difference between mlx-tune and lightly-train?
- mlx-tune: Fine-tune LLMs on your Mac with Apple Silicon for various tasks including SFT, DPO, GRPO, Vision, TTS, STT, Embedding, and OCR.. lightly-train: All-in-one training for vision models: pretraining, fine-tuning, distillation.. See the comparison table for live GitHub stats and shared categories.
- When should I choose mlx-tune over lightly-train?
- Choose mlx-tune over lightly-train when License: mlx-tune is Apache-2.0, lightly-train is AGPL-3.0; Tags unique to mlx-tune: apple-silicon, huggingface, large language models, llm; 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 lightly-train over mlx-tune?
- Choose lightly-train over mlx-tune when License: lightly-train is AGPL-3.0, mlx-tune is Apache-2.0; Requirements: Min 8 GB RAM; Tags unique to lightly-train: computer-vision, contrastive-learning, depth-estimation, dinov2; Lightly-train is a Python-based framework focused on training vision models including YOLO, ViTs, RT-DETR, and DINOv3, offering comprehensive features like pretraining, fine-tuning, and distillation.
- 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 lightly-train?
- Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
- Is mlx-tune or lightly-train more popular on GitHub?
- lightly-train has more GitHub stars (1,650 vs 1,372). Stars measure visibility, not whether either tool fits your constraints.
- Are mlx-tune and lightly-train open source?
- Yes - both are open-source projects on GitHub (mlx-tune: Apache-2.0, lightly-train: AGPL-3.0).
- Where can I find alternatives to mlx-tune or lightly-train?
- GraphCanon lists graph-backed alternatives at mlx-tune alternatives and lightly-train alternatives (mlx-tune markdown twin, lightly-train 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 lightly-train?
- mlx-tune: Steady. lightly-train: 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 lightly-train?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: mlx-tune trust report; lightly-train trust report.