--- title: "llm-course vs heretic" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/mlabonne-llm-course-vs-p-e-w-heretic" tools: ["mlabonne-llm-course", "p-e-w-heretic"] --- # llm-course vs heretic *GraphCanon updated Aug 16, 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 heretic if heretic is a Python-based tool that removes censorship from language models automatically. [llm-course](https://mlabonne.github.io/blog/) reports 82k GitHub stars, 9.5k forks, and 86 open issues, last pushed Feb 5, 2026. [heretic](https://heretic-project.org) has 28k stars, 3.0k forks, and 74 open issues, last pushed Aug 14, 2026. Figures are from public GitHub metadata via [llm-course's repository](https://github.com/mlabonne/llm-course) and [heretic's repository](https://github.com/p-e-w/heretic). | | [llm-course](/tools/mlabonne-llm-course.md) | [heretic](/tools/p-e-w-heretic.md) | | --- | --- | --- | | Tagline | Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. | Fully automatic censorship removal for language models | | Stars | 81,512 | 27,709 | | Forks | 9,490 | 2,997 | | Open issues | 86 | 74 | | 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 | Heretic is a Python-based tool that removes censorship from language models automatically. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | AGPL-3.0 | | Categories | Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | Evaluation & Observability, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [llm-course](/tools/mlabonne-llm-course.md) | [heretic](/tools/p-e-w-heretic.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 183d | 2d | | Open issues (now) | 86 | 74 | | Stars delta | +771 (30d) | +1.3k (30d) | | Open issues delta | +1 (30d) | +2 (30d) | | Full report | [trust report](/tools/mlabonne-llm-course/trust.md) | [trust report](/tools/p-e-w-heretic/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: heretic - **Adopt for:** Heretic is a Python-based tool that removes censorship from language models automatically. ## Choose when ### Choose llm-course if… - License: llm-course is Apache-2.0, heretic is AGPL-3.0. - 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 Inference & Serving, Model Training. - - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge ### Choose heretic if… - License: heretic is AGPL-3.0, llm-course is Apache-2.0. - Tags unique to heretic: abliteration, llm, transformer. - Use Heretic if your work involves uncovering full capabilities of censored or restricted language models. Heretic excels in tasks requiring the removal of such restrictions. ## 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 heretic - Avoid Heretic if operating within jurisdictions where removing censorship from language models is illegal or against organizational policies. This could result in regulatory violations. - If your project does not benefit from uncensored content produced by language models (e.g., compliance-driven applications that require filtering mechanisms), using Heretic might be unnecessary. ## Common questions ### What is the difference between llm-course and heretic? llm-course: Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.. heretic: Fully automatic censorship removal for language models. See the comparison table for live GitHub stats and shared categories. ### When should I choose llm-course over heretic? Choose llm-course over heretic when License: llm-course is Apache-2.0, heretic is AGPL-3.0; 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 Inference & Serving, Model Training; - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge. ### When should I choose heretic over llm-course? Choose heretic over llm-course when License: heretic is AGPL-3.0, llm-course is Apache-2.0; Tags unique to heretic: abliteration, llm, transformer; Use Heretic if your work involves uncovering full capabilities of censored or restricted language models. Heretic excels in tasks requiring the removal of such restrictions. ### 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 heretic? Avoid Heretic if operating within jurisdictions where removing censorship from language models is illegal or against organizational policies. This could result in regulatory violations. If your project does not benefit from uncensored content produced by language models (e.g., compliance-driven applications that require filtering mechanisms), using Heretic might be unnecessary. ### Is llm-course or heretic more popular on GitHub? llm-course has more GitHub stars (81,512 vs 27,709). Stars measure visibility, not whether either tool fits your constraints. ### Are llm-course and heretic open source? Yes - both are open-source projects on GitHub (llm-course: Apache-2.0, heretic: AGPL-3.0). ### Where can I find alternatives to llm-course or heretic? GraphCanon lists graph-backed alternatives at [llm-course alternatives](/tools/mlabonne-llm-course/alternatives) and [heretic alternatives](/tools/p-e-w-heretic/alternatives) ([llm-course markdown twin](/tools/mlabonne-llm-course/alternatives.md), [heretic markdown twin](/tools/p-e-w-heretic/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-p-e-w-heretic.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, llm-course or heretic? llm-course: Slowing. heretic: Very 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 heretic? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [llm-course trust report](/tools/mlabonne-llm-course/trust); [heretic trust report](/tools/p-e-w-heretic/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/_