Course provider: University of Ljubljana, Faculty of Computer and Information Science (UL FRI)
Instructors: Matej Klemen (UL FRI), Domen Vreš (UL FRI), Živa Štebljaj (UL FRI), Marko Robnik-Šikonja (UL FRI)
Learning objectives: Get practical knowledge on how to use large language models (LLMs) via API calls, their fine-tuning for specific tasks, and efficient inference.
Course contents: To use large language models (LLMs) in a research or business setting, it is necessary to i) to access them on local computers or on GPU servers via API, ii) adapt them to specific needs by fine-tuning them. The hands-on workshop will present theoretically and practically, step-by-step:
- How LLMs can be used via API on a local or remote server.
- How to fine-tune encoder-only models such as BERT.
- How to fine-tune generative models such as Llama, Gemma, and GaMS using compute-efficient techniques such as LoRA.
- How to implement efficient LLM inference.
The work will be based on practical datasets and problems from business and research environments. The use cases will cover a handful of selected tasks such as sentiment prediction, information retrieval, and question answering.
Learning outcomes: Practical knowledge of LLM API use and fine-tuning. Practical problem-solving skills with LLMs.