HandsOnLLM/Hands-On-Large-Language-Models

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.

Evidence and sources

Open original GitHub project