--- title: "Awesome-Code-LLM vs llm-course" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/huybery-awesome-code-llm-vs-mlabonne-llm-course" tools: ["huybery-awesome-code-llm", "mlabonne-llm-course"] --- # Awesome-Code-LLM vs llm-course *GraphCanon updated Jul 11, 2026* ## Verdict Pick Awesome-Code-LLM if awesome-Code-LLM is a curated repository focused on code-focused large language models (code-LLMs), providing insights into top-performing models, evaluation toolkits, and research papers; 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. [Awesome-Code-LLM](https://github.com/huybery/Awesome-Code-LLM) reports 1.3k GitHub stars, 74 forks, and 3 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 [Awesome-Code-LLM's repository](https://github.com/huybery/Awesome-Code-LLM) and [llm-course's repository](https://github.com/mlabonne/llm-course). | | [Awesome-Code-LLM](/tools/huybery-awesome-code-llm.md) | [llm-course](/tools/mlabonne-llm-course.md) | | --- | --- | --- | | Tagline | 👨💻 An awesome and curated list of best code-LLM for research. | Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. | | Stars | 1,288 | 80,839 | | Forks | 74 | 9,421 | | Open issues | 3 | 84 | | Language | - | - | | Adopt for | Awesome-Code-LLM is a curated repository focused on code-focused large language models (code-LLMs), providing insights into top-performing models, evaluation toolkits, and research papers. | 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 License: Permissive open-source license that allows usage in virtually any project with little restrictions. | Apache-2.0 | | Categories | Evaluation & Observability, LLM Frameworks | Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-Code-LLM](/tools/huybery-awesome-code-llm.md) | [llm-course](/tools/mlabonne-llm-course.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 578d | 155d | | Open issues (now) | 3 | 84 | | Full report | [trust report](/tools/huybery-awesome-code-llm/trust.md) | [trust report](/tools/mlabonne-llm-course/trust.md) | ## Decision facts: Awesome-Code-LLM - **Requirements:** No specific requirements to use the repository for reference or evaluation, but contributions may involve technical knowledge and familiarity with code-LLMs. - **Adopt for:** Awesome-Code-LLM is a curated repository focused on code-focused large language models (code-LLMs), providing insights into top-performing models, evaluation toolkits, and research papers. - **License detail:** MIT License: Permissive open-source license that allows usage in virtually any project with little restrictions. ## 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 Awesome-Code-LLM if… - License: Awesome-Code-LLM is MIT, llm-course is Apache-2.0. - Requirements: No specific requirements to use the repository for reference or evaluation, but contributions may involve technical knowledge and familiarity with code-LLMs.. - Tags unique to Awesome-Code-LLM: awesome, code-generation. - When you need a comprehensive list of state-of-the-art code generation LLMs with performance metrics such as HumanEval. ### Choose llm-course if… - License: llm-course is Apache-2.0, Awesome-Code-LLM is MIT. - Requirements: Course materials are available in Colab notebooks; access requires a Google account. - Tags unique to llm-course: colab-notebooks, course, machine-learning, roadmap. - Also covers Inference & Serving, Model Training. - - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge ## When NOT to use Awesome-Code-LLM - When looking for a tool that provides pre-trained models with built-in APIs or services, as Awesome-Code-LLM is primarily a directory/collection of information without direct service provision. - If you require real-time interactive use-cases and need immediate API access to LLMs; this repository does not offer such functionality. - In scenarios where you need a single end-to-end solution for training your own code generation models, as the platform is focused on aggregating third-party resources and research rather than offering ## 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 Awesome-Code-LLM and llm-course? Awesome-Code-LLM: 👨💻 An awesome and curated list of best code-LLM for research.. 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 Awesome-Code-LLM over llm-course? Choose Awesome-Code-LLM over llm-course when License: Awesome-Code-LLM is MIT, llm-course is Apache-2.0; Requirements: No specific requirements to use the repository for reference or evaluation, but contributions may involve technical knowledge and familiarity with code-LLMs.; Tags unique to Awesome-Code-LLM: awesome, code-generation; When you need a comprehensive list of state-of-the-art code generation LLMs with performance metrics such as HumanEval. ### When should I choose llm-course over Awesome-Code-LLM? Choose llm-course over Awesome-Code-LLM when License: llm-course is Apache-2.0, Awesome-Code-LLM is MIT; Requirements: Course materials are available in Colab notebooks; access requires a Google account; Tags unique to llm-course: colab-notebooks, course, machine-learning, roadmap; Also covers Inference & Serving, Model Training; - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge. ### When should I avoid Awesome-Code-LLM? When looking for a tool that provides pre-trained models with built-in APIs or services, as Awesome-Code-LLM is primarily a directory/collection of information without direct service provision. If you require real-time interactive use-cases and need immediate API access to LLMs; this repository does not offer such functionality. In scenarios where you need a single end-to-end solution for training your own code generation models, as the platform is focused on aggregating third-party resources and research rather than offering ### 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 Awesome-Code-LLM or llm-course more popular on GitHub? llm-course has more GitHub stars (80,839 vs 1,288). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-Code-LLM and llm-course open source? Yes - both are open-source projects on GitHub (Awesome-Code-LLM: MIT, llm-course: Apache-2.0). ### Where can I find alternatives to Awesome-Code-LLM or llm-course? GraphCanon lists graph-backed alternatives at [Awesome-Code-LLM alternatives](/tools/huybery-awesome-code-llm/alternatives) and [llm-course alternatives](/tools/mlabonne-llm-course/alternatives) ([Awesome-Code-LLM markdown twin](/tools/huybery-awesome-code-llm/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/huybery-awesome-code-llm-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, Awesome-Code-LLM or llm-course? Awesome-Code-LLM: 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 Awesome-Code-LLM and llm-course? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-Code-LLM trust report](/tools/huybery-awesome-code-llm/trust); [llm-course trust report](/tools/mlabonne-llm-course/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=huybery-awesome-code-llm`](/api/graphcanon/graph?tool=huybery-awesome-code-llm) - 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/_