Type: GitHub Repository Original link: https://github.com/HandsOnLLM/Hands-On-Large-Language-Models?tab=readme-ov-file Publication date: 2026-01-28
Summary #
Introduction #
Imagine you are a data scientist who needs to analyze a huge dataset of product reviews. You need to extract useful information, such as customer opinions on various aspects of the product, but the dataset is too large to be managed manually. Or, imagine you are a machine learning engineer who needs to develop a chatbot system for an e-commerce company. The chatbot must be able to answer complex customer questions in real-time, but you have no idea where to start.
These are just two examples of situations where large language models (LLM) can make a difference. LLMs are artificial intelligence models that can understand and generate text in a way very similar to a human. However, working with these models can be complex and requires in-depth knowledge of various concepts and tools. This is where the “Hands-On Large Language Models” project comes into play.
This project, available on GitHub, is the official repository of the O’Reilly book “Hands-On Large Language Models.” It offers a practical and visually educational approach to learning how to use LLMs. With nearly 300 custom figures, the book and the repository guide you through the fundamental concepts and practical tools needed to work with LLMs today. Thanks to this project, you can transform complex data into useful information and create advanced artificial intelligence systems in a simple and intuitive way.
What It Does #
The “Hands-On Large Language Models” project is a repository that contains the code for all the examples in the eponymous book. The repository is structured into various chapters, each covering a specific topic related to LLMs. For example, there are chapters dedicated to the introduction to language models, tokens and embeddings, text classification, prompt engineering, and much more.
The project primarily uses Jupyter Notebook, an interactive development environment that allows you to run Python code and view the results in real-time. This makes the learning process much more interactive and accessible, especially for those new to the field of LLMs. Additionally, the repository includes detailed guides for installing and configuring the working environment, making it easy for anyone to start working with LLMs.
Why It’s Amazing #
The “wow” factor of this project lies in its ability to make complex concepts accessible through a practical and visually educational approach. It is not just a textbook or a code repository: it is a complete learning experience that guides you step-by-step into the world of LLMs.
Dynamic and contextual: #
One of the most amazing aspects of this project is its dynamic and contextual nature. Each example in the repository is designed to be run in an interactive environment, such as Google Colab. This means you can immediately see the results of your code and understand how LLMs work in practice. For example, in the chapter dedicated to text classification, you can load your dataset of reviews and see how the model automatically classifies customer opinions. This approach makes learning much more engaging and effective.
Real-time reasoning: #
Another strength of the project is its ability to enable real-time reasoning. Thanks to the use of Jupyter Notebook and Google Colab, you can run the code and see the results in real-time. This is particularly useful when working with large language models, which can be complex and difficult to understand. For example, you can load a pre-trained model and see how it responds to different questions in real-time. This allows you to experiment and better understand how LLMs work.
Concrete examples and practical applications: #
The project is rich in concrete examples and practical applications. Each chapter includes real examples that show you how to apply theoretical concepts to real-world problems. For example, in the chapter dedicated to text generation, you can see how to create a chatbot that answers complex customer questions. Or, in the chapter dedicated to semantic search, you can see how to improve information retrieval in a dataset of documents. These concrete examples make the project much more useful and applicable to real life.
Community and support: #
Finally, the project benefits from an active community and continuous support. The authors of the book and the repository are actively involved in the community and respond to user questions and feedback. This makes the project much more reliable and supported, making it easier for anyone to start working with LLMs.
How to Try It #
To start working with the “Hands-On Large Language Models” project, follow these steps:
-
Clone the repository: You can find the code on GitHub at the following address: Hands-On Large Language Models. Clone the repository to your computer using the command
git clone https://github.com/HandsOnLLM/Hands-On-Large-Language-Models.git. -
Prerequisites: Make sure you have Python installed on your computer. Additionally, we recommend using Google Colab to run the notebooks, as it offers a free and powerful development environment with GPU access.
-
Setup: Follow the instructions in the
.setup/folder to install all necessary dependencies. You can find a complete guide on how to configure the working environment in the.setup/conda/folder. -
Documentation: The main documentation is available in the repository and in the book “Hands-On Large Language Models.” We recommend reading the documentation carefully to better understand how to use the project.
There is no one-click demo, but the setup process is well-documented and easy to follow. Once the environment is configured, you can start exploring the various chapters and running the interactive examples.
Final Thoughts #
The “Hands-On Large Language Models” project represents a significant step forward in how we can learn and work with large language models. Thanks to its practical and visually educational approach, it makes complex concepts accessible to a wider audience. This is particularly important in an era where artificial intelligence is becoming increasingly central in various sectors.
The project not only teaches you how to use LLMs but also shows you how to apply them to real-world problems. This makes it a valuable resource for data scientists, machine learning engineers, and anyone interested in exploring the potential of LLMs.
In conclusion, “Hands-On Large Language Models” is a project that has the potential to revolutionize the way we learn and work with artificial intelligence. With its active community and continuous support, it is a project worth exploring and adopting. Happy work and happy exploration!
Use Cases #
- Private AI Stack: Integration into proprietary pipelines
- Client Solutions: Implementation for client projects
- Development Acceleration: Reduction of time-to-market for projects
Resources #
Original Links #
- GitHub - HandsOnLLM/Hands-On-Large-Language-Models: Official code repo for the O’Reilly Book - “Hands-On Large Language Models” - Original link
Article recommended and selected by the Human Technology eXcellence team, processed through artificial intelligence (in this case with LLM HTX-EU-Mistral3.1Small) on 2026-01-28 07:49 Original source: https://github.com/HandsOnLLM/Hands-On-Large-Language-Models?tab=readme-ov-file
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