--- title: "llm-course vs KnowledgeEditingPapers" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/mlabonne-llm-course-vs-zjunlp-knowledgeeditingpapers" tools: ["mlabonne-llm-course", "zjunlp-knowledgeeditingpapers"] --- # llm-course vs KnowledgeEditingPapers *GraphCanon updated Jul 12, 2026* ## Verdict 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; pick KnowledgeEditingPapers if a specialized collection of foundational papers and reports that delve into the editing and manipulation of knowledge within large language models. [llm-course](https://mlabonne.github.io/blog/) reports 81k GitHub stars, 9.4k forks, and 84 open issues, last pushed Feb 5, 2026. [KnowledgeEditingPapers](https://github.com/zjunlp/KnowledgeEditingPapers) has 1.2k stars, 79 forks, and 0 open issues, last pushed Jun 25, 2026. Figures are from public GitHub metadata via [llm-course's repository](https://github.com/mlabonne/llm-course) and [KnowledgeEditingPapers's repository](https://github.com/zjunlp/KnowledgeEditingPapers). | | [llm-course](/tools/mlabonne-llm-course.md) | [KnowledgeEditingPapers](/tools/zjunlp-knowledgeeditingpapers.md) | | --- | --- | --- | | Tagline | Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. | Must-read Papers on Knowledge Editing for Large Language Models | | Stars | 80,839 | 1,235 | | Forks | 9,421 | 79 | | Open issues | 84 | 0 | | 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 | A specialized collection of foundational papers and reports that delve into the editing and manipulation of knowledge within large language models, making it a valuable resource for researchers looking to understand and斧 | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | LLM Frameworks, Model Training, Inference & Serving, Evaluation & Observability | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [llm-course](/tools/mlabonne-llm-course.md) | [KnowledgeEditingPapers](/tools/zjunlp-knowledgeeditingpapers.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Active (82%) | | Days since push | 155d | 16d | | Open issues (now) | 84 | 0 | | Owner type | User | Organization | | Full report | [trust report](/tools/mlabonne-llm-course/trust.md) | [trust report](/tools/zjunlp-knowledgeeditingpapers/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 ## Decision facts: KnowledgeEditingPapers - **Hosting:** unknown - **Adopt for:** A specialized collection of foundational papers and reports that delve into the editing and manipulation of knowledge within large language models, making it a valuable resource for researchers looking to understand and斧 - **License detail:** MIT ## Choose when ### Choose llm-course if… - License: llm-course is Apache-2.0, KnowledgeEditingPapers 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, roadmap. - Also covers Inference & Serving, Evaluation & Observability. - - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge ### Choose KnowledgeEditingPapers if… - License: KnowledgeEditingPapers is MIT, llm-course is Apache-2.0. - Tags unique to KnowledgeEditingPapers: model-editing, natural-language-processing, knowledge-editing, pre-trained-language-models. - You are specifically interested in recent advancements in knowledge editing techniques for large language models. ## 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 KnowledgeEditingPapers - You are looking for a broad overview of machine learning or AI in general, as this repository focuses narrowly on knowledge editing within large language models. - If you seek practical tooling or implementation guidance rather than theoretical insights and review papers. - Your focus is more on data preprocessing or model training techniques unrelated to the specific modification of knowledge mechanisms in LLMs. ## Common questions ### What is the difference between llm-course and KnowledgeEditingPapers? llm-course: Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.. KnowledgeEditingPapers: Must-read Papers on Knowledge Editing for Large Language Models. See the comparison table for live GitHub stats and shared categories. ### When should I choose llm-course over KnowledgeEditingPapers? Choose llm-course over KnowledgeEditingPapers when License: llm-course is Apache-2.0, KnowledgeEditingPapers 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, roadmap; Also covers Inference & Serving, Evaluation & Observability; - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge. ### When should I choose KnowledgeEditingPapers over llm-course? Choose KnowledgeEditingPapers over llm-course when License: KnowledgeEditingPapers is MIT, llm-course is Apache-2.0; Tags unique to KnowledgeEditingPapers: model-editing, natural-language-processing, knowledge-editing, pre-trained-language-models; You are specifically interested in recent advancements in knowledge editing techniques for large language models. ### 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 KnowledgeEditingPapers? You are looking for a broad overview of machine learning or AI in general, as this repository focuses narrowly on knowledge editing within large language models. If you seek practical tooling or implementation guidance rather than theoretical insights and review papers. Your focus is more on data preprocessing or model training techniques unrelated to the specific modification of knowledge mechanisms in LLMs. ### Is llm-course or KnowledgeEditingPapers more popular on GitHub? llm-course has more GitHub stars (80,839 vs 1,235). Stars measure visibility, not whether either tool fits your constraints. ### Are llm-course and KnowledgeEditingPapers open source? Yes - both are open-source projects on GitHub (llm-course: Apache-2.0, KnowledgeEditingPapers: MIT). ### Where can I find alternatives to llm-course or KnowledgeEditingPapers? GraphCanon lists graph-backed alternatives at [llm-course alternatives](/tools/mlabonne-llm-course/alternatives) and [KnowledgeEditingPapers alternatives](/tools/zjunlp-knowledgeeditingpapers/alternatives) ([llm-course markdown twin](/tools/mlabonne-llm-course/alternatives.md), [KnowledgeEditingPapers markdown twin](/tools/zjunlp-knowledgeeditingpapers/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-zjunlp-knowledgeeditingpapers.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, llm-course or KnowledgeEditingPapers? llm-course: Slowing. KnowledgeEditingPapers: Active. 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 KnowledgeEditingPapers? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [llm-course trust report](/tools/mlabonne-llm-course/trust); [KnowledgeEditingPapers trust report](/tools/zjunlp-knowledgeeditingpapers/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/_