Comparison
mlx-tune vs Eagle
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 Eagle if eagle: Frontier Vision-Language Models with Data-Centric Strategies.
Markdown twin · mlx-tune alternatives · Eagle alternatives
GraphCanon updated 1w
Trust & integrity
| Signal | mlx-tune | Eagle |
|---|---|---|
| Maintenance | Steady (36d since push) As of 3w · github_public_v1 | Steady (54d since push) As of 1w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 3w · github_public_v1 | Not a fork · Organization account As of 1w · 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.
- Eagle
- Frontier Vision-Language Models with Data-Centric Strategies
Stars
- mlx-tune
- 1.4k
- Eagle
- 3.4k
Forks
- mlx-tune
- 88
- Eagle
- 327
Open issues
- mlx-tune
- 11
- Eagle
- 62
Language
- mlx-tune
- Python
- Eagle
- 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.
- Eagle
- Eagle: Frontier Vision-Language Models with Data-Centric Strategies
Persona
- mlx-tune
- -
- Eagle
- -
Runtime
- mlx-tune
- -
- Eagle
- -
License
- mlx-tune
- Apache-2.0
- Eagle
- The code is released under Apache 2.0 license, while the pretrained models are under CC BY-NC 4.0 or NVIDIA licenses for non-commercial use only.
Last pushed
- mlx-tune
- Jun 23, 2026
- Eagle
- Jun 24, 2026
Categories
- mlx-tune
- Computer Vision, LLM Frameworks, Model Training, Speech & Audio
- Eagle
- Computer Vision, LLM Frameworks
Trust and health
Days since push
- mlx-tune
- 36d
- Eagle
- 54d
Open issues (now)
- mlx-tune
- 11
- Eagle
- 62
Stars delta
- mlx-tune
- Unknown
- Eagle
- +199 (30d)
Open issues delta
- mlx-tune
- Unknown
- Eagle
- +3 (30d)
Owner type
- mlx-tune
- User
- Eagle
- Organization
OSV dependency advisories
- mlx-tune
- Published findings
- Eagle
- No lockfile (source not queried)
Full report
- mlx-tune
- Trust report
- Eagle
- Trust report
Choose mlx-tune if…
- Tags unique to mlx-tune: apple-silicon, deep-learning, large language models, llm.
- Also covers Model Training, 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 Eagle if…
- Pricing: Free for non-commercial use; requires adherence to licensing agreements.
- Requirements: Min 8 GB RAM; Ensure compliance with all applicable laws and regulations when using the dataset and model weights..
- Tags unique to Eagle: data-centric-strategies, gpt4, llm-improvements, nvidia-technology.
- When you need advanced vision-language models enhanced by data-centric strategies developed by NVlabs and improved using Qwen.
When NOT to use Eagle
- If your project requires commercial use, as Eagle's models are intended for non-commercial use only under the CC BY-NC 4.0 License or NVIDIA License.
- In situations where you require a vision-language model that does not rely on improvements made using Qwen.
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 (NVlabs/Eagle) · observed Aug 18, 2026
- GitHub forks (NVlabs/Eagle) · observed Aug 18, 2026
- Last push (NVlabs/Eagle) · observed Jun 24, 2026
- License file (Apache-2.0) · observed Aug 18, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: mlx-tune 1.4k · Eagle 3.4k (synced Jul 30, 2026).
Common questions
- What is the difference between mlx-tune and Eagle?
- mlx-tune: Fine-tune LLMs on your Mac with Apple Silicon for various tasks including SFT, DPO, GRPO, Vision, TTS, STT, Embedding, and OCR.. Eagle: Frontier Vision-Language Models with Data-Centric Strategies. See the comparison table for live GitHub stats and shared categories.
- When should I choose mlx-tune over Eagle?
- Choose mlx-tune over Eagle when Tags unique to mlx-tune: apple-silicon, deep-learning, large language models, llm; Also covers Model Training, Speech & Audio; You need to fine-tune large language models on a Mac with Apple Silicon hardware.
- When should I choose Eagle over mlx-tune?
- Choose Eagle over mlx-tune when Pricing: Free for non-commercial use; requires adherence to licensing agreements; Requirements: Min 8 GB RAM; Ensure compliance with all applicable laws and regulations when using the dataset and model weights.; Tags unique to Eagle: data-centric-strategies, gpt4, llm-improvements, nvidia-technology; When you need advanced vision-language models enhanced by data-centric strategies developed by NVlabs and improved using Qwen.
- 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 Eagle?
- If your project requires commercial use, as Eagle's models are intended for non-commercial use only under the CC BY-NC 4.0 License or NVIDIA License. In situations where you require a vision-language model that does not rely on improvements made using Qwen.
- Is mlx-tune or Eagle more popular on GitHub?
- Eagle has more GitHub stars (3,407 vs 1,372). Stars measure visibility, not whether either tool fits your constraints.
- Are mlx-tune and Eagle open source?
- Yes - both are open-source projects on GitHub (mlx-tune: Apache-2.0, Eagle: Apache-2.0).
- Where can I find alternatives to mlx-tune or Eagle?
- GraphCanon lists graph-backed alternatives at mlx-tune alternatives and Eagle alternatives (mlx-tune markdown twin, Eagle 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 Eagle?
- mlx-tune: Steady. Eagle: Steady. 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 Eagle?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: mlx-tune trust report; Eagle trust report.