peremartra/Large-Language-Model-Notebooks-Course: a notebook course for engineers who want to build with LLMs

What the course covers and who it is written for

This is a teaching repository, not a package. The README describes it as a free hands-on course about large language models and their applications, aimed at engineers, researchers and developers, and it says the notebooks are being updated while new examples and chapters are added. The intended reader already writes Python and is comfortable in a notebook environment; the course does not explain Python itself, it explains how to call models, wire chains, evaluate outputs, fine tune and prune.

The course is split into three parts. The first, Techniques and Libraries, is a set of small examples. The second, Projects, builds larger applications and explains design decisions, with the README noting that a project may have more than one valid implementation. The third, Enterprise Solutions, treats a model as one component inside a larger corporate system rather than a standalone answer. The top-level directories map onto this: 1-Introduction to LLMs with OpenAI, 2-Vector Databases with LLMs, 3-LangChain, 4-Evaluating LLMs, 5-Fine Tuning, 6-PRUNING, plus project folders P1-NL2SQL, P2-MHF, P3-CustomFinancialLLM and enterprise folders E1-NL2SQL for big Databases and E2-Transforming Banks With Embeddings.

The README is explicit that the GitHub course is not the whole book. It is the unofficial repository for an Apress book based on this content, and it points readers who want the original notebooks, exactly as printed, to the Apress repository. That distinction matters: if you want a stable artifact that matches a printed text, this is the wrong repository.

Notebook plus article: the actual mechanism of the course

There is no runtime, no CLI and no service. The unit of learning is a lesson, and each lesson pairs one or more notebooks with a Medium article. The README states that notebooks contain enough information to understand the code inside them, while the article gives more detailed explanation of the code and the topic, and it advises keeping the article open alongside the notebook and following along.

Most notebooks are hosted on Colab, a few on Kaggle. The README gives the reasoning: Kaggle offers more memory in its free tier, but copying and sharing notebooks is simpler on Colab and not everyone has a Kaggle account. It also warns that some notebooks need more memory than free Colab provides, which is a normal situation when working with large language models, and suggests running them in your own environment or using Colab Pro.

The practical consequence is that the repository is a set of .ipynb files plus prose. There is no lockfile pinning library versions and no test suite that would tell you a notebook has drifted from the current API of OpenAI, transformers or LangChain. The README says the author is updating notebooks and incorporating new examples, which cuts both ways: the material improves, and a lesson you read six months ago may no longer match the file now on main.

Installing nothing: running your first lesson

Because the deliverable is notebooks, there is nothing to pip install for the course itself. You need a Python environment with Jupyter, and then you install whatever a given lesson imports. The README does not publish a single requirements file or a setup command, so the honest starting point is to clone the repository and open the first lesson.

git clone https://github.com/peremartra/Large-Language-Model-Notebooks-Course.git
cd Large-Language-Model-Notebooks-Course
jupyter notebook

After that, open the folder 1-Introduction to LLMs with OpenAI and the notebook 1_1-First_Chatbot_OpenAI.ipynb. That lesson, per the README, builds a fast food restaurant chatbot with OpenAI GPT-3.5 and Panel, and it is where prompt engineering basics are introduced: the OpenAI roles, temperature, and how to avoid prompt injections.

The lesson depends on the OpenAI API, so you need a key in your environment before the first cell will run. The README does not document the exact variable name or the client setup for this notebook, so read the first cells of the notebook and follow what they import and read. Do not copy a variable name from a blog post; the notebook is the source of truth here.

If you would rather not manage an environment, the README's own route is to open the notebook on Colab or Kaggle from the lesson page. That is also the fastest way to discover the memory ceiling the README warns about, because a large model loaded in a free Colab session is the failure you are most likely to hit first.

Where the course breaks down in practice

The clearest limitation is stated by the author: the course on GitHub does not contain all the information that is in the book. If you are following the repository alone, you are reading a subset, and the README does not list which chapters are missing.

Memory is the second constraint. The README says plainly that some notebooks require more memory than the free version of Colab provides, and that this is a recurring situation when working with large language models. Fine tuning, PEFT and pruning lessons are the ones most likely to hit that wall, since they load model weights rather than just call an API. The README's suggested workarounds are your own environment or Colab Pro; there is no smaller-model fallback documented in the repository.

Reproducibility is the third. Nothing in the README promises pinned dependency versions, and the repository has no releases. A notebook that calls a hosted API can stop working when that API changes, and a notebook that imports transformers or LangChain can break when those libraries change their interfaces. The author's habit of updating notebooks over time is an improvement for readers arriving today and a source of drift for anyone who bookmarked a lesson earlier.

Finally, this is not a reference implementation you can lift into production. The Projects section explains design decisions, but the README frames LLMOps as a secondary topic rather than the focus. If you need deployment, monitoring or evaluation harnesses for a live system, the course will not supply them.

How it compares with a framework-first path such as LangChain's own documentation

The obvious alternative is to skip the course and work directly from the documentation of the libraries it teaches, LangChain and Hugging Face in particular. The difference in approach is real. Library documentation is organized around the library's own API surface: it tells you what a chain, a retriever or a trainer does and how to call it. This course is organized around tasks and decisions. A lesson starts from a problem, such as turning natural language into SQL or building a restaurant chatbot, and then chooses tools for it.

That ordering has a cost. Library docs stay closer to current versions because they are maintained by the projects themselves; a task-oriented course written across many notebooks ages less evenly. It also has a benefit. The README says each project may have more than one possible implementation and that there is often not just one perfect solution, which is closer to how the work actually feels than an API reference is.

A second alternative is the book itself, or its official Apress repository. That gives you a fixed text with the original notebooks, at the price of the book and without the later additions the author mentions making here. Choose based on whether you want a stable artifact or a living one.

Licence, maintenance and what upgrading costs you

The repository is MIT licensed, per LICENSE.md. For a collection of notebooks that is a permissive choice: you can reuse and adapt the code in your own work, subject to the usual condition of preserving the licence notice. That is a description of the licence text, not legal advice, and it does not cover the third-party models and APIs the notebooks call. Using OpenAI models, or downloading weights from Hugging Face, is governed by those providers' own terms, which the README does not discuss.

The last push to the repository was on 2026-05-28, so the project is not archived and has been touched within the last few months. The README describes the course as in permanent development, which is consistent with that. There are no releases, so there is no version to pin and no changelog to read before an upgrade. Upgrading, in this repository, means pulling main and re-running the notebook you care about.

That is the real maintenance cost. You are not upgrading a dependency you installed; you are re-checking whether a lesson still executes against today's OpenAI client, transformers version and LangChain API. The repository includes a clean_notebooks.py script at the top level, which suggests the author processes notebook outputs before committing, but the README does not document what it does or how to run it.

Editorial conclusion

Adopt this if you learn by reading a notebook next to an article and you want working examples of OpenAI calls, LangChain chains, vector database use, evaluation, PEFT and pruning without paying for a course. Do not adopt it if you need a supported library with a versioned API, a fixed syllabus, or a downloadable PDF: the README points to the Apress repository for the original book notebooks, and the course on GitHub does not contain all the book material. Before you commit time, open one lesson such as 3-LangChain and check whether its notebook still runs in your environment, because the README states some notebooks need more memory than the free Colab tier provides.

Frequently asked questions

Official sources

1. Issues
2. License: MIT
3. peremartra/Large-Language-Model-Notebooks-Course on GitHub
4. Project website
5. README