Online course

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.

Open University

The course will be offered free of charge through FITech in spring 2026. Enrollment for the implementation takes place via the<a href="https://fitech.io/en/studies/?fit_course_types=upcoming&amp;orderby=start_date" data-entity-type="external"> FITech</a> website.

Faculty of Information Technology and Communication Sciences

English

After the course, the students are able to<ul><li>understand the fundamental architecture, operation, and potential of Generative AI and Large Language Models, recognizing the importance of developing and deploying trustworthy AI systems</li><li>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</li><li>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</li><li>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</li><li>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.</li></ul>

Apply for study rights through the <a href="https://fitech.io/fi/">FITech</a> website. You will find a Start here button on each <a href="https://fitech.io/fi/opinnot/fine-tuning-large-language-models/" data-entity-type="external">course page</a>, which will direct you to the correct form. Once your application has been approved, you will receive instructions on, among other things, activating your IT ID and starting your studies.

Mandatory: COMP.CS.100 Programming 1: Introduction to Programming, 5 op or similar knowledgeRecommended: DATA.100 Introduction to Data Analysis, 5 op

Fine-tuning Large Language Models (LLM)

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.The course doesn't include any exams.

Independent studies

Online studies

Data Processing and Information Technology

Advanced studies

<ul><li>Goodfellow, I., Bengio, Y. and Courville, A., 2016. Deep learning. MIT press. Provides foundational knowledge in deep learning, relevant for understanding the underlying principles of LLMs.</li><li>Hapke, H., Howard, C. and Lane, H., 2019. Natural Language Processing in Action: Understanding, analyzing, and generating text with Python. Simon and Schuster. Offers practical insights into working with text data, crucial for LLM applications.</li><li>Vakkuri, V., Kemell, K.K., Jantunen, M., Halme, E. and Abrahamsson, P., 2021. ECCOLA—A method for implementing ethically aligned AI systems. Journal of Systems and Software, 182.</li><li>More literature, articles and papers will be delivered during the course.</li><li>Software and Tools:<ul><li>Python programming language: Essential for all practical exercises and projects within the course.</li><li>Jupyter notebooks: Used for coding exercises, allowing for interactive development and documentation of code, notes, and equations.</li><li>Google Colab: Provides a cloud-based Python programming environment with access to free GPU resources, beneficial for training models without requiring personal hardware.</li></ul></li><li>Datasets for Practice (optional)<ul><li>Kaggle datasets: A wide variety of text datasets suitable for fine-tuning LLMs on specific tasks or domains.</li><li>Hugging face datasets library: Offers easy access to numerous datasets, ideal for experimenting with different types of natural language processing tasks.</li></ul></li><li>Online Courses and Tutorials (optional)<ul><li>Hugging Face’s transformers library documentation and tutorials: Detailed guides on using one of the most popular open-source libraries for LLMs, ideal for practical fine-tuning exercises.</li><li>Coursera’s ”Deep Learning Specialization” by Andrew Ng: Covers deep learning fundamentals, with some courses focusing on sequence models and natural language processing. edX’s ”Ethics and Law in Data and Analytics”: Provides insights into ethical and legal considerations in handling data, relevant for AI projects.</li></ul></li></ul>

COMP.CS.530 Fine-tuning Large Language Models (LLM) (FITech), 5 - 10 cr

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Fine-tuning Large Language Models (LLM), 5 - 10 cr

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