--- title: "mlx-tune vs Eagle" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/arahim3-mlx-tune-vs-nvlabs-eagle" tools: ["arahim3-mlx-tune", "nvlabs-eagle"] --- # mlx-tune vs Eagle *GraphCanon updated Aug 18, 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 Eagle if eagle: Frontier Vision-Language Models with Data-Centric Strategies. [mlx-tune](https://arahim3.github.io/mlx-tune/) reports 1.4k GitHub stars, 88 forks, and 11 open issues, last pushed Jun 23, 2026. [Eagle](https://nvlabs.github.io/Eagle/) has 3.4k stars, 327 forks, and 62 open issues, last pushed Jun 24, 2026. Figures are from public GitHub metadata via [mlx-tune's repository](https://github.com/ARahim3/mlx-tune) and [Eagle's repository](https://github.com/NVlabs/Eagle). | | [mlx-tune](/tools/arahim3-mlx-tune.md) | [Eagle](/tools/nvlabs-eagle.md) | | --- | --- | --- | | Tagline | Fine-tune LLMs on your Mac with Apple Silicon for various tasks including SFT, DPO, GRPO, Vision, TTS, STT, Embedding, and OCR. | Frontier Vision-Language Models with Data-Centric Strategies | | Stars | 1,372 | 3,407 | | Forks | 88 | 327 | | Open issues | 11 | 62 | | 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. | Eagle: Frontier Vision-Language Models with Data-Centric Strategies | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | 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. | | Categories | Computer Vision, LLM Frameworks, Model Training, Speech & Audio | Computer Vision, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [mlx-tune](/tools/arahim3-mlx-tune.md) | [Eagle](/tools/nvlabs-eagle.md) | | --- | --- | --- | | Days since push | 36d | 54d | | Open issues (now) | 11 | 62 | | Stars delta | Unknown | +199 (30d) | | Open issues delta | Unknown | +3 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/arahim3-mlx-tune/trust.md) | [trust report](/tools/nvlabs-eagle/trust.md) | ## 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: Eagle - **Pricing:** freemium - 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. - **Adopt for:** Eagle: Frontier Vision-Language Models with Data-Centric Strategies - **License detail:** 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. ## Choose when ### 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 ### 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 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 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. ## 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](/tools/arahim3-mlx-tune/alternatives) and [Eagle alternatives](/tools/nvlabs-eagle/alternatives) ([mlx-tune markdown twin](/tools/arahim3-mlx-tune/alternatives.md), [Eagle markdown twin](/tools/nvlabs-eagle/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-nvlabs-eagle.md) 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](/tools/arahim3-mlx-tune/trust); [Eagle trust report](/tools/nvlabs-eagle/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/_