--- title: "Liger-Kernel vs Large-Language-Model-Notebooks-Course" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/linkedin-liger-kernel-vs-peremartra-large-language-model-notebooks-course" tools: ["linkedin-liger-kernel", "peremartra-large-language-model-notebooks-course"] --- # Liger-Kernel vs Large-Language-Model-Notebooks-Course *GraphCanon updated Jul 12, 2026* ## Verdict Pick Liger-Kernel when liger-Kernel is primarily Python; Large-Language-Model-Notebooks-Course is Jupyter Notebook; pick Large-Language-Model-Notebooks-Course when large-Language-Model-Notebooks-Course is primarily Jupyter Notebook; Liger-Kernel is Python. [Liger-Kernel](https://linkedin.github.io/Liger-Kernel/) reports 6.5k GitHub stars, 554 forks, and 161 open issues, last pushed Jul 6, 2026. [Large-Language-Model-Notebooks-Course](https://medium.com/@peremartra/list/large-language-models-practical-course-66b4ce5943ce) has 1.8k stars, 447 forks, and 0 open issues, last pushed May 28, 2026. Figures are from public GitHub metadata via [Liger-Kernel's repository](https://github.com/linkedin/Liger-Kernel) and [Large-Language-Model-Notebooks-Course's repository](https://github.com/peremartra/Large-Language-Model-Notebooks-Course). | | [Liger-Kernel](/tools/linkedin-liger-kernel.md) | [Large-Language-Model-Notebooks-Course](/tools/peremartra-large-language-model-notebooks-course.md) | | --- | --- | --- | | Tagline | Efficient Triton Kernels for LLM Training | Practical course about Large Language Models. | | Stars | 6,494 | 1,814 | | Forks | 554 | 447 | | Open issues | 161 | 0 | | Language | Python | Jupyter Notebook | | Adopt for | - | The Large-Language-Model-Notebooks-Course repository offers comprehensive hands-on experiences with large language models, focusing on practical applications using libraries like Hugging Face and OpenAI. | | Persona | - | - | | Runtime | - | - | | License | BSD-2-Clause | MIT | | Categories | LLM Frameworks, Model Training | LLM Frameworks, Vector Databases, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Liger-Kernel](/tools/linkedin-liger-kernel.md) | [Large-Language-Model-Notebooks-Course](/tools/peremartra-large-language-model-notebooks-course.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 4d | 44d | | Open issues (now) | 161 | 0 | | Owner type | Organization | User | | Full report | [trust report](/tools/linkedin-liger-kernel/trust.md) | [trust report](/tools/peremartra-large-language-model-notebooks-course/trust.md) | ## Decision facts: Large-Language-Model-Notebooks-Course - **Pricing:** freemium - The repository itself is free to use under the MIT License. However, for more comprehensive content not available in the repository, you might need to purchase the book. - **Requirements:** - Requires familiarity with Jupyter Notebooks and an interest in large language models.; - Recommended experience or at least a basic understanding of Hugging Face libraries and OpenAI API usage. - **Adopt for:** The Large-Language-Model-Notebooks-Course repository offers comprehensive hands-on experiences with large language models, focusing on practical applications using libraries like Hugging Face and OpenAI. ## Choose when ### Choose Liger-Kernel if… - Liger-Kernel is primarily Python; Large-Language-Model-Notebooks-Course is Jupyter Notebook. - License: Liger-Kernel is BSD-2-Clause, Large-Language-Model-Notebooks-Course is MIT. - Tags unique to Liger-Kernel: llms, llama, mistral, gemma2. ### Choose Large-Language-Model-Notebooks-Course if… - Large-Language-Model-Notebooks-Course is primarily Jupyter Notebook; Liger-Kernel is Python. - License: Large-Language-Model-Notebooks-Course is MIT, Liger-Kernel is BSD-2-Clause. - Pricing: The repository itself is free to use under the MIT License. However, for more comprehensive content not available in the repository, you might need to purchase the book.. - Requirements: - Requires familiarity with Jupyter Notebooks and an interest in large language models.; - Recommended experience or at least a basic understanding of Hugging Face libraries and OpenAI API usage.. - Tags unique to Large-Language-Model-Notebooks-Course: peft-fine-tuning-llm, fine-tuning-llm, large-language-models, chatbots. - Also covers Vector Databases. - - When you need a course that combines theoretical knowledge from published papers with practical implementation through small projects. ## When NOT to use Liger-Kernel - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## When NOT to use Large-Language-Model-Notebooks-Course - - Avoid if you require up-to-date information that is exclusively available within the book linked with the repository; the GitHub course does not contain all information present in the book. - - If your primary interest lies purely in learning from structured, complete, and unchanging materials, as this course is described to be 'in permanent development' and may lack a stable or final set. ## Common questions ### What is the difference between Liger-Kernel and Large-Language-Model-Notebooks-Course? Liger-Kernel: Efficient Triton Kernels for LLM Training. Large-Language-Model-Notebooks-Course: Practical course about Large Language Models.. See the comparison table for live GitHub stats and shared categories. ### When should I choose Liger-Kernel over Large-Language-Model-Notebooks-Course? Choose Liger-Kernel over Large-Language-Model-Notebooks-Course when Liger-Kernel is primarily Python; Large-Language-Model-Notebooks-Course is Jupyter Notebook; License: Liger-Kernel is BSD-2-Clause, Large-Language-Model-Notebooks-Course is MIT; Tags unique to Liger-Kernel: llms, llama, mistral, gemma2. ### When should I choose Large-Language-Model-Notebooks-Course over Liger-Kernel? Choose Large-Language-Model-Notebooks-Course over Liger-Kernel when Large-Language-Model-Notebooks-Course is primarily Jupyter Notebook; Liger-Kernel is Python; License: Large-Language-Model-Notebooks-Course is MIT, Liger-Kernel is BSD-2-Clause; Pricing: The repository itself is free to use under the MIT License. However, for more comprehensive content not available in the repository, you might need to purchase the book.; Requirements: - Requires familiarity with Jupyter Notebooks and an interest in large language models.; - Recommended experience or at least a basic understanding of Hugging Face libraries and OpenAI API usage.; Tags unique to Large-Language-Model-Notebooks-Course: peft-fine-tuning-llm, fine-tuning-llm, large-language-models, chatbots; Also covers Vector Databases; - When you need a course that combines theoretical knowledge from published papers with practical implementation through small projects. ### When should I avoid Liger-Kernel? LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### When should I avoid Large-Language-Model-Notebooks-Course? - Avoid if you require up-to-date information that is exclusively available within the book linked with the repository; the GitHub course does not contain all information present in the book. - If your primary interest lies purely in learning from structured, complete, and unchanging materials, as this course is described to be 'in permanent development' and may lack a stable or final set. ### Is Liger-Kernel or Large-Language-Model-Notebooks-Course more popular on GitHub? Liger-Kernel has more GitHub stars (6,494 vs 1,814). Stars measure visibility, not whether either tool fits your constraints. ### Are Liger-Kernel and Large-Language-Model-Notebooks-Course open source? Yes - both are open-source projects on GitHub (Liger-Kernel: BSD-2-Clause, Large-Language-Model-Notebooks-Course: MIT). ### Where can I find alternatives to Liger-Kernel or Large-Language-Model-Notebooks-Course? GraphCanon lists graph-backed alternatives at [Liger-Kernel alternatives](/tools/linkedin-liger-kernel/alternatives) and [Large-Language-Model-Notebooks-Course alternatives](/tools/peremartra-large-language-model-notebooks-course/alternatives) ([Liger-Kernel markdown twin](/tools/linkedin-liger-kernel/alternatives.md), [Large-Language-Model-Notebooks-Course markdown twin](/tools/peremartra-large-language-model-notebooks-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/linkedin-liger-kernel-vs-peremartra-large-language-model-notebooks-course.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Liger-Kernel or Large-Language-Model-Notebooks-Course? Liger-Kernel: Very active. Large-Language-Model-Notebooks-Course: Steady. 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 Liger-Kernel and Large-Language-Model-Notebooks-Course? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Liger-Kernel trust report](/tools/linkedin-liger-kernel/trust); [Large-Language-Model-Notebooks-Course trust report](/tools/peremartra-large-language-model-notebooks-course/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=linkedin-liger-kernel`](/api/graphcanon/graph?tool=linkedin-liger-kernel) - 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/_