ScaDS.AI Summer School LLM Training

ScaDS.AI Summer School LLM Training#

This is a collection of Jupyter Notebooks about basic and advanced LLM usage, focusing on what’s practicslly possible with Large Language Models. We demonstrate most utilities using the ScaDS.AI Large Language Models (LLM) Servicer (TU Dresden VPN required). Most exercises should also work with other providers such as GWDG KISSKI and Helmholtz Blablador. The tutorial aims at Python programmers who want to dive into LLMs for generating text, code and data using open source/weights models. After this course they will be able to integrate LLMs into their software applications.

Contributions and feedback are very welcome! In case you see room for improvement, please create a github issue and/or consider contributing.

Topics#

The notebook collection aims covering these topics:

Large Language Models (LLMs)
Text/Code/Data generation
Prompt Engineering
Retrieval-augmented-generation
Vision
Function calling
Agents

Covered Python libraries and software#

In these notebooks we use libraries from the GenAI field:

Slide decks#

Also training slides can be downloaded from zenodo: https://doi.org/10.5281/zenodo.15743126

Videos#

The materials provided here were also discussed in a couple of recorded Online Lectures

Acknowledgements#

We acknowledge the financial support by the Federal Ministry of Education and Research of Germany and by Sächsische Staatsministerium für Wissenschaft, Kultur und Tourismus in the programme Center of Excellence for AI-research „Center for Scalable Data Analytics and Artificial Intelligence Dresden/Leipzig“, project identification number: ScaDS.AI