--- title: "dart-math vs llm-course" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hkust-nlp-dart-math-vs-mlabonne-llm-course" tools: ["hkust-nlp-dart-math", "mlabonne-llm-course"] --- # dart-math vs llm-course *GraphCanon updated Jul 11, 2026* ## Verdict Pick dart-math when license: dart-math is MIT, llm-course is Apache-2.0; pick llm-course when license: llm-course is Apache-2.0, dart-math is MIT. [dart-math](https://hkust-nlp.github.io/dart-math/) reports 120 GitHub stars, 8 forks, and 5 open issues, last pushed Dec 10, 2024. [llm-course](https://mlabonne.github.io/blog/) has 81k stars, 9.4k forks, and 84 open issues, last pushed Feb 5, 2026. Figures are from public GitHub metadata via [dart-math's repository](https://github.com/hkust-nlp/dart-math) and [llm-course's repository](https://github.com/mlabonne/llm-course). | | [dart-math](/tools/hkust-nlp-dart-math.md) | [llm-course](/tools/mlabonne-llm-course.md) | | --- | --- | --- | | Tagline | [NeurIPS'24] Official code for *🎯DART-Math: Difficulty-Aware Rejection Tuning for Mathematical Problem-Solving* | Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. | | Stars | 120 | 80,839 | | Forks | 8 | 9,421 | | Open issues | 5 | 84 | | Language | Jupyter Notebook | - | | Adopt for | - | The llm-course provides a comprehensive guided course on Large Language Models (LLMs), divided into three parts: LLM Fundamentals, The LLM Scientist, and The LLM Engineer. It includes resources such as Colab notebooks to | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | LLM Frameworks, Model Training, Inference & Serving | LLM Frameworks, Model Training, Inference & Serving, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [dart-math](/tools/hkust-nlp-dart-math.md) | [llm-course](/tools/mlabonne-llm-course.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 578d | 155d | | Open issues (now) | 5 | 84 | | Owner type | Organization | User | | Security scan | No criticals | No lockfile | | Full report | [trust report](/tools/hkust-nlp-dart-math/trust.md) | [trust report](/tools/mlabonne-llm-course/trust.md) | ## Shared compatibility - **Python**: [dart-math](/tools/hkust-nlp-dart-math.md) - Python runtime; [llm-course](/tools/mlabonne-llm-course.md) - Python runtime ## Decision facts: llm-course - **Requirements:** Course materials are available in Colab notebooks; access requires a Google account - **Adopt for:** The llm-course provides a comprehensive guided course on Large Language Models (LLMs), divided into three parts: LLM Fundamentals, The LLM Scientist, and The LLM Engineer. It includes resources such as Colab notebooks to - **License detail:** Apache-2.0 ## Choose when ### Choose dart-math if… - License: dart-math is MIT, llm-course is Apache-2.0. - Tags unique to dart-math: deep-learning, llm, nlp, jupyter notebook. - Leaner open-issue backlog (5). ### Choose llm-course if… - License: llm-course is Apache-2.0, dart-math is MIT. - Requirements: Course materials are available in Colab notebooks; access requires a Google account. - Tags unique to llm-course: colab-notebooks, machine-learning, course, large-language-models. - Also covers Evaluation & Observability. - - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge ## When NOT to use dart-math - Last GitHub push was 579 days ago (dormant maintenance, Dec 10, 2024). Validate activity before betting a new project on dart-math. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ## When NOT to use llm-course - - If you only require a quick introduction to LLMs without deep dive into core components - - When you prefer working directly with commercial platforms that provide complete services rather than following detailed steps on building and deploying models yourself through this course's open,DI ## Common questions ### What is the difference between dart-math and llm-course? dart-math: [NeurIPS'24] Official code for *🎯DART-Math: Difficulty-Aware Rejection Tuning for Mathematical Problem-Solving*. llm-course: Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.. See the comparison table for live GitHub stats and shared categories. ### When should I choose dart-math over llm-course? Choose dart-math over llm-course when License: dart-math is MIT, llm-course is Apache-2.0; Tags unique to dart-math: deep-learning, llm, nlp, jupyter notebook; Leaner open-issue backlog (5). ### When should I choose llm-course over dart-math? Choose llm-course over dart-math when License: llm-course is Apache-2.0, dart-math is MIT; Requirements: Course materials are available in Colab notebooks; access requires a Google account; Tags unique to llm-course: colab-notebooks, machine-learning, course, large-language-models; Also covers Evaluation & Observability; - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge. ### When should I avoid dart-math? Last GitHub push was 579 days ago (dormant maintenance, Dec 10, 2024). Validate activity before betting a new project on dart-math. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ### When should I avoid llm-course? - If you only require a quick introduction to LLMs without deep dive into core components - When you prefer working directly with commercial platforms that provide complete services rather than following detailed steps on building and deploying models yourself through this course's open,DI ### Is dart-math or llm-course more popular on GitHub? llm-course has more GitHub stars (80,839 vs 120). Stars measure visibility, not whether either tool fits your constraints. ### Are dart-math and llm-course open source? Yes - both are open-source projects on GitHub (dart-math: MIT, llm-course: Apache-2.0). ### Where can I find alternatives to dart-math or llm-course? GraphCanon lists graph-backed alternatives at [dart-math alternatives](/tools/hkust-nlp-dart-math/alternatives) and [llm-course alternatives](/tools/mlabonne-llm-course/alternatives) ([dart-math markdown twin](/tools/hkust-nlp-dart-math/alternatives.md), [llm-course markdown twin](/tools/mlabonne-llm-course/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/hkust-nlp-dart-math-vs-mlabonne-llm-course.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, dart-math or llm-course? dart-math: Dormant. llm-course: Slowing. 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 dart-math and llm-course? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [dart-math trust report](/tools/hkust-nlp-dart-math/trust); [llm-course trust report](/tools/mlabonne-llm-course/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=hkust-nlp-dart-math`](/api/graphcanon/graph?tool=hkust-nlp-dart-math) - 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/_