Kevin Roitero

Tenure-Track Assistant Professor, University of Udine

Kevin Roitero is a Tenure-Track Assistant Professor (RTDb) at the University of Udine, Italy. His research interests include Artificial Intelligence, Natural Language Processing, Information Retrieval, and Crowdsourcing. He visited and collaborated with multiple top universities across the globe (The University of Sheffield, RMIT University, The University of Queensland, Pioneer Center for Artificial Intelligence & University of Copenhagen) as well as with leading industry partners (Spotify), publishing papers in top ranked (CORE A*) and selective conferences such as SIGIR, WSDM, WWW, CIKM, HCOMP, and in top-tier journals (highly ranked or Q1) such as TDKE, JDIQ, IRJ, and IP&M. As result of the his work, he received multiple grants and awards, including the participation in the 7th edition of the prestigious Heidelberg Laureate Forum (top 200 young researchers in mathematics and computer science), the “con.Scienze2020” prize for the best Ph.D. thesis in Computer Science discussed in 2020 in Italy, and multiple Best Paper Awards.



Practical Applications of Large Language Models with PyTorch and Hugging Face


This lecture aims to provide participants with a comprehensive understanding of implementing and using Large Language Models (LLMs) through PyTorch and the Hugging Face Transformers library. Attendees will explore the theoretical underpinnings of LLMs, including their architecture and training processes, before diving into hands-on exercises designed to demonstrate practical applications. The session will cover key aspects such as setting up the environment, loading pre-trained models, and customizing them for specific tasks like text generation, sentiment analysis, and language understanding. The workshop will also address challenges in deploying these models, including considerations for performance optimization. By the end of this lecture, participants will be equipped with the knowledge and skills necessary to integrate LLMs into their projects and workflows, leveraging the latest tools and techniques provided by PyTorch and Hugging Face. 


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