--- title: "code-eval vs llm-course" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/abacaj-code-eval-vs-mlabonne-llm-course" tools: ["abacaj-code-eval", "mlabonne-llm-course"] --- # code-eval vs llm-course *GraphCanon updated Jul 11, 2026* ## Verdict Pick code-eval when license: code-eval is MIT, llm-course is Apache-2.0; pick llm-course when license: llm-course is Apache-2.0, code-eval is MIT. [code-eval](https://github.com/abacaj/code-eval) reports 429 GitHub stars, 37 forks, and 5 open issues, last pushed Sep 12, 2023. [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 [code-eval's repository](https://github.com/abacaj/code-eval) and [llm-course's repository](https://github.com/mlabonne/llm-course). | | [code-eval](/tools/abacaj-code-eval.md) | [llm-course](/tools/mlabonne-llm-course.md) | | --- | --- | --- | | Tagline | Run evaluation on LLMs using human-eval benchmark | Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. | | Stars | 429 | 80,839 | | Forks | 37 | 9,421 | | Open issues | 5 | 84 | | Language | Python | - | | 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, Evaluation & Observability | Model Training, LLM Frameworks, Inference & Serving, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [code-eval](/tools/abacaj-code-eval.md) | [llm-course](/tools/mlabonne-llm-course.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 1033d | 155d | | Open issues (now) | 5 | 84 | | Security scan | 73 low (73 low) | No lockfile | | Full report | [trust report](/tools/abacaj-code-eval/trust.md) | [trust report](/tools/mlabonne-llm-course/trust.md) | ## Shared compatibility - **Python**: [code-eval](/tools/abacaj-code-eval.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 code-eval if… - License: code-eval is MIT, llm-course is Apache-2.0. - Tags unique to code-eval: wizardcoder, humaneval, python. - Leaner open-issue backlog (5). ### Choose llm-course if… - License: llm-course is Apache-2.0, code-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 NOT to use code-eval - Last GitHub push was 1034 days ago (dormant maintenance, Sep 12, 2023). Validate activity before betting a new project on code-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. ## 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 code-eval and llm-course? code-eval: Run evaluation on LLMs using human-eval benchmark. 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 code-eval over llm-course? Choose code-eval over llm-course when License: code-eval is MIT, llm-course is Apache-2.0; Tags unique to code-eval: wizardcoder, humaneval, python; Leaner open-issue backlog (5). ### When should I choose llm-course over code-eval? Choose llm-course over code-eval when License: llm-course is Apache-2.0, code-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 avoid code-eval? Last GitHub push was 1034 days ago (dormant maintenance, Sep 12, 2023). Validate activity before betting a new project on code-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. ### 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 code-eval or llm-course more popular on GitHub? llm-course has more GitHub stars (80,839 vs 429). Stars measure visibility, not whether either tool fits your constraints. ### Are code-eval and llm-course open source? Yes - both are open-source projects on GitHub (code-eval: MIT, llm-course: Apache-2.0). ### Where can I find alternatives to code-eval or llm-course? GraphCanon lists graph-backed alternatives at [code-eval alternatives](/tools/abacaj-code-eval/alternatives) and [llm-course alternatives](/tools/mlabonne-llm-course/alternatives) ([code-eval markdown twin](/tools/abacaj-code-eval/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/abacaj-code-eval-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, code-eval or llm-course? code-eval: 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 code-eval and llm-course? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [code-eval trust report](/tools/abacaj-code-eval/trust); [llm-course trust report](/tools/mlabonne-llm-course/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=abacaj-code-eval`](/api/graphcanon/graph?tool=abacaj-code-eval) - 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/_