--- title: "llm-action vs llm-course" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/liguodongiot-llm-action-vs-mlabonne-llm-course" tools: ["liguodongiot-llm-action", "mlabonne-llm-course"] --- # llm-action vs llm-course *GraphCanon updated Aug 16, 2026* ## Verdict Pick llm-action if llm-action aims to share large model technology principles and practical experiences covering areas such as engineering, deployment, inference, serving, and training; pick llm-course if 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. [llm-action](https://www.zhihu.com/column/c_1456193767213043713) reports 25k GitHub stars, 2.8k forks, and 19 open issues, last pushed Jul 19, 2026. [llm-course](https://mlabonne.github.io/blog/) has 82k stars, 9.5k forks, and 86 open issues, last pushed Feb 5, 2026. Figures are from public GitHub metadata via [llm-action's repository](https://github.com/liguodongiot/llm-action) and [llm-course's repository](https://github.com/mlabonne/llm-course). | | [llm-action](/tools/liguodongiot-llm-action.md) | [llm-course](/tools/mlabonne-llm-course.md) | | --- | --- | --- | | Tagline | Aims to share large model technology principles and practical experience (large model engineering, application implementation) | Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. | | Stars | 24,898 | 81,512 | | Forks | 2,842 | 9,490 | | Open issues | 19 | 86 | | Language | HTML | - | | Adopt for | llm-action aims to share large model technology principles and practical experiences covering areas such as engineering, deployment, inference, serving, and training. | 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 | llm-action is open-source under the Apache-2.0 license. | Apache-2.0 | | Categories | Inference & Serving, LLM Frameworks, Model Training | Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [llm-action](/tools/liguodongiot-llm-action.md) | [llm-course](/tools/mlabonne-llm-course.md) | | --- | --- | --- | | Maintenance | Active (82%) | Slowing (36%) | | Days since push | 28d | 183d | | Open issues (now) | 19 | 86 | | Stars delta | +162 (30d) | +771 (30d) | | Full report | [trust report](/tools/liguodongiot-llm-action/trust.md) | [trust report](/tools/mlabonne-llm-course/trust.md) | ## Decision facts: llm-action - **Adopt for:** llm-action aims to share large model technology principles and practical experiences covering areas such as engineering, deployment, inference, serving, and training. - **License detail:** llm-action is open-source under the Apache-2.0 license. ## 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 llm-action if… - Tags unique to llm-action: deployment, engineering, inference, large model. - - When you need detailed examples and best practices of implementing large language models (LLMs) in real-world applications, llm-action provides insights into the challenges faced during LLM's actual - More recently updated (last pushed Jul 19, 2026). ### Choose llm-course if… - Requirements: Course materials are available in Colab notebooks; access requires a Google account. - Tags unique to llm-course: colab-notebooks, course, large language models, machine-learning. - Also covers Evaluation & Observability. - - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge ## When NOT to use llm-action - - If your focus is narrowly on cutting-edge research rather than practical implementation details, llm-action may not be the best resource as it focuses more on deployment processes. - - When looking for a full-stack solution that includes detailed code implementations and libraries for each phase (training, serving), llm-action might fall short. It shines in sharing knowledge but ## 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 llm-action and llm-course? llm-action: Aims to share large model technology principles and practical experience (large model engineering, application implementation). 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 llm-action over llm-course? Choose llm-action over llm-course when Tags unique to llm-action: deployment, engineering, inference, large model; - When you need detailed examples and best practices of implementing large language models (LLMs) in real-world applications, llm-action provides insights into the challenges faced during LLM's actual; More recently updated (last pushed Jul 19, 2026). ### When should I choose llm-course over llm-action? Choose llm-course over llm-action when Requirements: Course materials are available in Colab notebooks; access requires a Google account; Tags unique to llm-course: colab-notebooks, course, large language models, machine-learning; Also covers Evaluation & Observability; - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge. ### When should I avoid llm-action? - If your focus is narrowly on cutting-edge research rather than practical implementation details, llm-action may not be the best resource as it focuses more on deployment processes. - When looking for a full-stack solution that includes detailed code implementations and libraries for each phase (training, serving), llm-action might fall short. It shines in sharing knowledge but ### 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 llm-action or llm-course more popular on GitHub? llm-course has more GitHub stars (81,512 vs 24,898). Stars measure visibility, not whether either tool fits your constraints. ### Are llm-action and llm-course open source? Yes - both are open-source projects on GitHub (llm-action: Apache-2.0, llm-course: Apache-2.0). ### Where can I find alternatives to llm-action or llm-course? GraphCanon lists graph-backed alternatives at [llm-action alternatives](/tools/liguodongiot-llm-action/alternatives) and [llm-course alternatives](/tools/mlabonne-llm-course/alternatives) ([llm-action markdown twin](/tools/liguodongiot-llm-action/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/liguodongiot-llm-action-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, llm-action or llm-course? llm-action: Active. 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 llm-action and llm-course? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [llm-action trust report](/tools/liguodongiot-llm-action/trust); [llm-course trust report](/tools/mlabonne-llm-course/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=liguodongiot-llm-action`](/api/graphcanon/graph?tool=liguodongiot-llm-action) - 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/_