--- title: "LLMSys-PaperList vs llm-course" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/amberljc-llmsys-paperlist-vs-mlabonne-llm-course" tools: ["amberljc-llmsys-paperlist", "mlabonne-llm-course"] --- # LLMSys-PaperList vs llm-course *GraphCanon updated Jul 11, 2026* ## Verdict Pick LLMSys-PaperList if lLMSys-PaperList offers a comprehensive list of papers and resources tailored specifically to Large Language Model (LLM) systems; 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. [LLMSys-PaperList](https://github.com/AmberLJC/LLMSys-PaperList) reports 2.2k GitHub stars, 114 forks, and 0 open issues, last pushed Jul 9, 2026. [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 [LLMSys-PaperList's repository](https://github.com/AmberLJC/LLMSys-PaperList) and [llm-course's repository](https://github.com/mlabonne/llm-course). | | [LLMSys-PaperList](/tools/amberljc-llmsys-paperlist.md) | [llm-course](/tools/mlabonne-llm-course.md) | | --- | --- | --- | | Tagline | Curated list of academic papers related to Large Language Model systems | Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. | | Stars | 2,175 | 80,839 | | Forks | 114 | 9,421 | | Open issues | 0 | 84 | | Language | - | - | | Adopt for | LLMSys-PaperList offers a comprehensive list of papers and resources tailored specifically to Large Language Model (LLM) systems. | 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 | (unknown) | 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._ | | [LLMSys-PaperList](/tools/amberljc-llmsys-paperlist.md) | [llm-course](/tools/mlabonne-llm-course.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 1d | 155d | | Open issues (now) | 0 | 84 | | Full report | [trust report](/tools/amberljc-llmsys-paperlist/trust.md) | [trust report](/tools/mlabonne-llm-course/trust.md) | ## Decision facts: LLMSys-PaperList - **Hosting:** unknown - (repository does not specify hosting environment) - **Adopt for:** LLMSys-PaperList offers a comprehensive list of papers and resources tailored specifically to Large Language Model (LLM) systems. - **License detail:** (unknown) ## 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 LLMSys-PaperList if… - (repository does not specify hosting environment) - Tags unique to LLMSys-PaperList: academic-sources, framework-overview, inference-techniques, research papers. - - When you need a curated list focusing on technical advancements in pre-training, post-training, serving, and multi-modal LLM systems. ### 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 LLMSys-PaperList - - If you are looking for a general repository of machine learning papers rather than specific developments related to Large Language Models. - - When your primary need is documentation or code examples rather than academic papers and project insights. - - For applications where real-time updates and active community support are imperative, as LLMSys-PaperList primarily serves as a static list without user interaction features like commenting or liveＱ ## 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 LLMSys-PaperList and llm-course? LLMSys-PaperList: Curated list of academic papers related to Large Language Model systems. 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 LLMSys-PaperList over llm-course? Choose LLMSys-PaperList over llm-course when (repository does not specify hosting environment); Tags unique to LLMSys-PaperList: academic-sources, framework-overview, inference-techniques, research papers; - When you need a curated list focusing on technical advancements in pre-training, post-training, serving, and multi-modal LLM systems. ### When should I choose llm-course over LLMSys-PaperList? Choose llm-course over LLMSys-PaperList 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 LLMSys-PaperList? - If you are looking for a general repository of machine learning papers rather than specific developments related to Large Language Models. - When your primary need is documentation or code examples rather than academic papers and project insights. - For applications where real-time updates and active community support are imperative, as LLMSys-PaperList primarily serves as a static list without user interaction features like commenting or liveＱ ### 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 LLMSys-PaperList or llm-course more popular on GitHub? llm-course has more GitHub stars (80,839 vs 2,175). Stars measure visibility, not whether either tool fits your constraints. ### Are LLMSys-PaperList and llm-course open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to LLMSys-PaperList or llm-course? GraphCanon lists graph-backed alternatives at [LLMSys-PaperList alternatives](/tools/amberljc-llmsys-paperlist/alternatives) and [llm-course alternatives](/tools/mlabonne-llm-course/alternatives) ([LLMSys-PaperList markdown twin](/tools/amberljc-llmsys-paperlist/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/amberljc-llmsys-paperlist-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, LLMSys-PaperList or llm-course? LLMSys-PaperList: Very 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 LLMSys-PaperList and llm-course? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LLMSys-PaperList trust report](/tools/amberljc-llmsys-paperlist/trust); [llm-course trust report](/tools/mlabonne-llm-course/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=amberljc-llmsys-paperlist`](/api/graphcanon/graph?tool=amberljc-llmsys-paperlist) - 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/_