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Fine-tuning Open Source LLMs with Mistral

Tuesday, July 16 11AM ET

Key Takeaways

Learn how to fine-tune a large language model using the Hugging Face Python ecosystem.
Learn about the steps to prepare for fine-tuning and how to evaluate your success.
Learn about best practices for fine-tuning models.

Your Presenter(s)

Andrea Valenzuela

Junior Fellow at CMS, CERN

Andrea Valenzuela is currently working on the CMS experiment at the particle accelerator (CERN) in Geneva, Switzerland. With expertise in data engineering and analysis for the past six years, her duties include data analysis and software development. She is now working towards democratizing the learning of data-related technologies through the Medium publication ForCode'Sake.

She holds a BS in Engineering Physics from the Polytechnic University of Catalonia, as well as an MS in Intelligent Interactive Systems from Pompeu Fabra University. Her research experience includes professional work with previous OpenAI algorithms for image generation, such as Normalizing Flows.

Josep Ferrer

Freelance Data Scientist at NECSTouR

Josep is a freelance Data Scientist specializing in European projects, with expertise in data storage, processing, advanced analytics, and impactful data storytelling.

As an educator, he teaches Big Data in the Master’s program at the University of Navarra and shares insights through articles on platforms like Medium, KDNuggets, and DataCamp. Josep also writes about Data and Tech in his newsletter Databites (databites.tech).

He holds a BS in Engineering Physics from the Polytechnic University of Catalonia and an MS in Intelligent Interactive Systems from Pompeu Fabra University.

Why this matters

While cutting-edge large language models can write almost any text you like, they are expensive to run. You can get the same performance for less money by using a smaller model and fine-tuning it to your needs.

In this session, Andrea, a Computing Engineer at CERN, and Josep, a Data Scientist at the Catalan Tourist Board, will walk you through the steps needed to customize the open-source Mistral LLM. You'll learn about choosing a suitable LLM, getting training data, tokenization, evaluating model performance, and best practices for fine-tuning.

To code along live with us, make sure you have accounts for both Lightning AI and Hugging Face.

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Fine-tuning Open Source LLMs with Mistral

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