Fine-tuning large language models 10 ECTS

Tampere University: Fine-tuning large language models, 10 ECTS. Explore generative AI’s potential in our accessible course: Learn to fine-tune large language models and embrace ethical AI principles for real-world impact. Course contents This course provides a detailed exploration into the practical application, ethical considerations, and open-source landscape of fine-tuning large language models (LLMs) for students with basic programming skills. Beginning with foundational data management techniques, the curriculum emphasizes the critical role of high-quality, privacy-conscious dataset preparation, specifically tailored to enhance open-source LLMs. Through detailed instruction on the architecture and functionality of these models, students will gain insight into the vast potential and challenges of working with open-source AI technologies. Throughout the course, participants will engage in practical exercises and projects that apply fine-tuning techniques to open-source LLMs, emphasizing the creation of AI systems that are not only technologically advanced but also ethically sound, transparent, and secure. This hands-on experience is designed to equip students with the technical skills necessary to contribute to and innovate within the open-source AI community. Learning outcomes After the course, the students are able to -understand the fundamental architecture, operation, and potential of Generative AI and Large Language Models, recognizing the importance of developing and deploying trustworthy AI systems -acquire skills in dataset creation, cleaning, and preparation, emphasizing the importance of data quality and relevance for effective model training, with a focus on leveraging open-source LLMs -apply practical fine-tuning techniques to large language models, focusing on creating systems that are fair, transparent, secure, and aligned with ethical guidelines, utilizing open-source tools and frameworks for model enhancement -evaluate the ethical implications of deploying large language models, advocating for and implementing practices that ensure their trustworthiness and beneficial use in society, particularly through the use of open-source models to foster innovation and accessibility -implement safeguards for an open-source language model, ensuring its responsible use, protecting against misuse, and maintaining the integrity and security of the model within various applications -apply generative AI and LLM fine-tuning options on industry-defined problems -account for LLM trustworthiness, governance, and deployment constraints in a real-world setting -collaborate in a group to design, implement, and justify technical choices of an LLM solution, balancing performance, requirements, and stakeholder expectations.