--- title: "llm-course vs Awesome-LLM-Eval" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/mlabonne-llm-course-vs-onejune2018-awesome-llm-eval" tools: ["mlabonne-llm-course", "onejune2018-awesome-llm-eval"] --- # llm-course vs Awesome-LLM-Eval *GraphCanon updated Jul 11, 2026* ## Verdict Pick llm-course when license: llm-course is Apache-2.0, Awesome-LLM-Eval is MIT; pick Awesome-LLM-Eval when license: Awesome-LLM-Eval is MIT, llm-course is Apache-2.0. [llm-course](https://mlabonne.github.io/blog/) reports 81k GitHub stars, 9.4k forks, and 84 open issues, last pushed Feb 5, 2026. [Awesome-LLM-Eval](https://arxiv.org/abs/2508.18646) has 648 stars, 78 forks, and 38 open issues, last pushed Nov 24, 2025. Figures are from public GitHub metadata via [llm-course's repository](https://github.com/mlabonne/llm-course) and [Awesome-LLM-Eval's repository](https://github.com/onejune2018/Awesome-LLM-Eval). | | [llm-course](/tools/mlabonne-llm-course.md) | [Awesome-LLM-Eval](/tools/onejune2018-awesome-llm-eval.md) | | --- | --- | --- | | Tagline | Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. | Awesome-LLM-Eval: a curated list of tools, datasets/benchmark, demos, leaderboard, papers, docs and models, mainly for Evaluation on LLMs. 一个由工具、基准/数据、演示、排行榜和大模型等组成的精选列表，主要面向基础大模型评测，旨在探求生成式AI的技术边界. | | Stars | 80,839 | 648 | | Forks | 9,421 | 78 | | Open issues | 84 | 38 | | Language | - | - | | 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 | Apache-2.0 | MIT | | Categories | LLM Frameworks, Model Training, Inference & Serving, Evaluation & Observability | LLM Frameworks, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [llm-course](/tools/mlabonne-llm-course.md) | [Awesome-LLM-Eval](/tools/onejune2018-awesome-llm-eval.md) | | --- | --- | --- | | Days since push | 155d | 229d | | Open issues (now) | 84 | 38 | | Full report | [trust report](/tools/mlabonne-llm-course/trust.md) | [trust report](/tools/onejune2018-awesome-llm-eval/trust.md) | ## 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-course if… - License: llm-course is Apache-2.0, Awesome-LLM-Eval 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 Model Training, Inference & Serving. - - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge ### Choose Awesome-LLM-Eval if… - License: Awesome-LLM-Eval is MIT, llm-course is Apache-2.0. - Tags unique to Awesome-LLM-Eval: bert, evaluation, dataset, benchmark. - Leaner open-issue backlog (38). ## 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 ## When NOT to use Awesome-LLM-Eval - Last GitHub push was 230 days ago (slowing maintenance, Nov 24, 2025). Validate activity before betting a new project on Awesome-LLM-Eval. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ## Common questions ### What is the difference between llm-course and Awesome-LLM-Eval? llm-course: Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.. Awesome-LLM-Eval: Awesome-LLM-Eval: a curated list of tools, datasets/benchmark, demos, leaderboard, papers, docs and models, mainly for Evaluation on LLMs. 一个由工具、基准/数据、演示、排行榜和大模型等组成的精选列表，主要面向基础大模型评测，旨在探求生成式AI的技术边界.. See the comparison table for live GitHub stats and shared categories. ### When should I choose llm-course over Awesome-LLM-Eval? Choose llm-course over Awesome-LLM-Eval when License: llm-course is Apache-2.0, Awesome-LLM-Eval 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 Model Training, Inference & Serving; - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge. ### When should I choose Awesome-LLM-Eval over llm-course? Choose Awesome-LLM-Eval over llm-course when License: Awesome-LLM-Eval is MIT, llm-course is Apache-2.0; Tags unique to Awesome-LLM-Eval: bert, evaluation, dataset, benchmark; Leaner open-issue backlog (38). ### 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 ### When should I avoid Awesome-LLM-Eval? Last GitHub push was 230 days ago (slowing maintenance, Nov 24, 2025). Validate activity before betting a new project on Awesome-LLM-Eval. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ### Is llm-course or Awesome-LLM-Eval more popular on GitHub? llm-course has more GitHub stars (80,839 vs 648). Stars measure visibility, not whether either tool fits your constraints. ### Are llm-course and Awesome-LLM-Eval open source? Yes - both are open-source projects on GitHub (llm-course: Apache-2.0, Awesome-LLM-Eval: MIT). ### Where can I find alternatives to llm-course or Awesome-LLM-Eval? GraphCanon lists graph-backed alternatives at [llm-course alternatives](/tools/mlabonne-llm-course/alternatives) and [Awesome-LLM-Eval alternatives](/tools/onejune2018-awesome-llm-eval/alternatives) ([llm-course markdown twin](/tools/mlabonne-llm-course/alternatives.md), [Awesome-LLM-Eval markdown twin](/tools/onejune2018-awesome-llm-eval/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/mlabonne-llm-course-vs-onejune2018-awesome-llm-eval.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, llm-course or Awesome-LLM-Eval? llm-course: Slowing. Awesome-LLM-Eval: 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-course and Awesome-LLM-Eval? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [llm-course trust report](/tools/mlabonne-llm-course/trust); [Awesome-LLM-Eval trust report](/tools/onejune2018-awesome-llm-eval/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=mlabonne-llm-course`](/api/graphcanon/graph?tool=mlabonne-llm-course) - 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/_