--- title: "peft vs Large-Language-Model-Notebooks-Course" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/huggingface-peft-vs-peremartra-large-language-model-notebooks-course" tools: ["huggingface-peft", "peremartra-large-language-model-notebooks-course"] --- # peft vs Large-Language-Model-Notebooks-Course *GraphCanon updated Jul 12, 2026* ## Verdict Pick peft if pEFT focuses on advanced techniques for efficiently tuning parameters in large models with Python; pick Large-Language-Model-Notebooks-Course if 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. [peft](https://huggingface.co/docs/peft) reports 21k GitHub stars, 2.4k forks, and 62 open issues, last pushed Jul 10, 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 [peft's repository](https://github.com/huggingface/peft) and [Large-Language-Model-Notebooks-Course's repository](https://github.com/peremartra/Large-Language-Model-Notebooks-Course). | | [peft](/tools/huggingface-peft.md) | [Large-Language-Model-Notebooks-Course](/tools/peremartra-large-language-model-notebooks-course.md) | | --- | --- | --- | | Tagline | State-of-the-art Parameter-Efficient Fine-Tuning | Practical course about Large Language Models. | | Stars | 21,385 | 1,814 | | Forks | 2,385 | 447 | | Open issues | 62 | 0 | | Language | Python | Jupyter Notebook | | Adopt for | PEFT focuses on advanced techniques for efficiently tuning parameters in large models with Python. | 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 | Apache-2.0 | MIT | | Categories | LLM Frameworks, Model Training | LLM Frameworks, Model Training, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [peft](/tools/huggingface-peft.md) | [Large-Language-Model-Notebooks-Course](/tools/peremartra-large-language-model-notebooks-course.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 0d | 44d | | Open issues (now) | 62 | 0 | | Owner type | Organization | User | | Full report | [trust report](/tools/huggingface-peft/trust.md) | [trust report](/tools/peremartra-large-language-model-notebooks-course/trust.md) | ## Decision facts: peft - **Adopt for:** PEFT focuses on advanced techniques for efficiently tuning parameters in large models with Python. ## 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 peft if… - peft is primarily Python; Large-Language-Model-Notebooks-Course is Jupyter Notebook. - License: peft is Apache-2.0, Large-Language-Model-Notebooks-Course is MIT. - Tags unique to peft: adapter, diffusion, fine-tuning, llm. - When you need to fine-tune large language models but are constrained by compute resources or want to avoid overfitting. ### Choose Large-Language-Model-Notebooks-Course if… - Large-Language-Model-Notebooks-Course is primarily Jupyter Notebook; peft is Python. - License: Large-Language-Model-Notebooks-Course is MIT, peft is Apache-2.0. - 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: chatbots, fine-tuning-llm, hf, huggingface. - 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 peft - If you require a tool that supports training from scratch, as PEFT is specifically designed for fine-tuning purposes only. - When working on models where the full fine-tuning of all parameters is feasible or preferred due to ample compute resources and no concern over overfitting. ## 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 peft and Large-Language-Model-Notebooks-Course? peft: State-of-the-art Parameter-Efficient Fine-Tuning. 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 peft over Large-Language-Model-Notebooks-Course? Choose peft over Large-Language-Model-Notebooks-Course when peft is primarily Python; Large-Language-Model-Notebooks-Course is Jupyter Notebook; License: peft is Apache-2.0, Large-Language-Model-Notebooks-Course is MIT; Tags unique to peft: adapter, diffusion, fine-tuning, llm; When you need to fine-tune large language models but are constrained by compute resources or want to avoid overfitting. ### When should I choose Large-Language-Model-Notebooks-Course over peft? Choose Large-Language-Model-Notebooks-Course over peft when Large-Language-Model-Notebooks-Course is primarily Jupyter Notebook; peft is Python; License: Large-Language-Model-Notebooks-Course is MIT, peft is Apache-2.0; 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: chatbots, fine-tuning-llm, hf, huggingface; 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 peft? If you require a tool that supports training from scratch, as PEFT is specifically designed for fine-tuning purposes only. When working on models where the full fine-tuning of all parameters is feasible or preferred due to ample compute resources and no concern over overfitting. ### 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 peft or Large-Language-Model-Notebooks-Course more popular on GitHub? peft has more GitHub stars (21,385 vs 1,814). Stars measure visibility, not whether either tool fits your constraints. ### Are peft and Large-Language-Model-Notebooks-Course open source? Yes - both are open-source projects on GitHub (peft: Apache-2.0, Large-Language-Model-Notebooks-Course: MIT). ### Where can I find alternatives to peft or Large-Language-Model-Notebooks-Course? GraphCanon lists graph-backed alternatives at [peft alternatives](/tools/huggingface-peft/alternatives) and [Large-Language-Model-Notebooks-Course alternatives](/tools/peremartra-large-language-model-notebooks-course/alternatives) ([peft markdown twin](/tools/huggingface-peft/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/huggingface-peft-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, peft or Large-Language-Model-Notebooks-Course? peft: 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 peft and Large-Language-Model-Notebooks-Course? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [peft trust report](/tools/huggingface-peft/trust); [Large-Language-Model-Notebooks-Course trust report](/tools/peremartra-large-language-model-notebooks-course/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=huggingface-peft`](/api/graphcanon/graph?tool=huggingface-peft) - 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/_