Generative Artificial Intelligence Notebooks#

This page is a collection of Jupyter Notebooks about Generative Artificial Intelligence (GenAI) - focusing on how to use certain techniques using Python. It aims at Python programmers who want to dive into GenAI for generating text, code and images using commerical and open source/weights models.

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)

  • Vision Language Models (VLMs)

  • Multi-modal Language Models

  • Text/Code/Image generation

  • Prompt Engineering

  • Retrieval-augmented-generation

  • Model fine-tuning

Covered Python libraries and software#

In these notebooks we use non-standard libraries from the GenAI field. Installation instructions can be found either in the first chapter or in the readme of the respective subchapter.

Covered models#

We will explore how these models work

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