A tutorial repository providing runnable Jupyter notebooks that accompany the book 'Hands-On Large Language Models', spanning introductory LLM concepts to advanced topics like semantic search, multimodal models, and fine-tuning. It is an educational collection of examples, not a production-ready application.
Project overview
Provides code for all book examples alongside almost 300 custom-made figures for visually learning practical LLM usage.
Project type
Infrastructure · Learning Resources
Use cases
Learning & Education
Deployment
Refer to project documentation
License
Apache-2.0
Best for
Developers, researchers, and educators seeking a structured, visual, and code-driven approach to learning practical LLM tools and concepts.
Key capabilities
Runnable Jupyter notebooks covering introductory LLM concepts to advanced topics like semantic search, multimodal models, and fine-tuning.
Additional complimentary guides going more in-depth into new and exciting topics like Mamba, Quantization, and Mixture of Experts.
Limitations and risks
The repository is a collection of educational examples rather than a production-ready application.
While local orchestration and execution are possible, the data boundary is external optional. Utilizing hosted paths like Google Colab involves external environments, and no direct end-to-end local-only data boundary is documented.
Telemetry and cost dependencies are not documented. Users should be aware of potential hosting or compute costs associated with external platforms if they choose hosted execution paths.
Getting started
Setup is rated as easy. The first success path is to run examples through Google Colab, which provides a web GUI and a T4 GPU with 16GB of VRAM. While coding is generally optional for execution, users will interact with Python code.