Join us for a hands-on workshop designed specifically for researchers and students who want to learn how to fine-tune Large Language Models using your own datasets. This workshop will introduce the fundamentals of model Fine-Tuning vs Retrieval Augmented Generation (RAG) and provide practical insights into how powerful LLMs can be tailored to your own specific (private) data requirements.

Expect a casual pace as we work through Python Notebooks to demonstrate data wrangling and AI workflows.

In this 2-hour virtual session, you will:

• Differentiating Fine-Tuning from RAG: understand the core differences between fine-tuning large language models and using Retrieval Augmented Generation (RAG) for scientific applications, helping you choose the right approach for your research.

• Practical LLM Customisation: adapt state-of-the-art (SOTA) models like Gemini and Gemma to your specific scientific datasets enabling you to unlock more accurate and relevant results.

• End-to-End AI Workflow for Scientific Data: get practical experience with the entire process, from preparing your own scientific data for model consumption to implementing and evaluating fine-tuned LLMs, all within a Python Notebook environment.

• Optimising LLMs for Scientific Research: enhance the performance of LLMs on specialised scientific tasks, allowing you to leverage these models more effectively for data analysis, hypothesis generation, and more.

The course will be all hands-on exercises and live coding demonstrations to reinforce your learning and build confidence.

Who should attend:

PhD students, researchers, industry professionals, anyone looking to accelerate your work with AI. Use case will be grounded in typical scientific workloads.

Check out all of the AI for Science, Build with AI series:

• May 7: Python and AI fundamentals

• May 14: Tailoring LLMs: RAG and Fine-Tuning

• May 21: AI Agents for Research

• May 28: AI-Assisted Research with Antigravity
  

Come to one or all sessions, everybody welcome! You will need to register for each event.

Requirements:

• A Google account to use Google Colab and Kaggle and AI Studio.

• Basic familiarity with Python (covered in the May 7 workshop) - but we will walk through the details.

• Bonus - free cloud credits for every attendee!
  

Course Content:

• https://gdgaiforscience.github.io/Build-with-AI/

As our speaker is donating their time, we want to ensure a full and engaged event. Please reserve your spot only if you are committed to joining us for this workshop.