Fine-Tuning with Llama 3

By taking this course, you'll learn how to fine-tune Llama models for specific tasks using fine-tuning libraries such as TorchTune, and Hugging Face's SFTTrainer. You'll gain practical skills in configuring fine-tuning recipes, preprocessing datasets, and implementing techniques like LoRA and quantization to optimize model training on limited hardware.

This course is designed for data scientists, ML and AI engineers, and developers who have had an introduction to working with the Llama model and are interested in advancing their skills in fine-tuning. It’s also ideal for those who want to explore how to run fine-tuning more efficiently.

This course focuses on fine-tuning Llama models using state-of-the-art libraries and practical techniques like recipe-based fine-tuning, LoRA, and quantization. Unlike other general AI courses, it provides hands-on experience with specific tools tailored for Llama, with a focus on customization of the models for real-world applications.

The skills learned in this course can be applied to a variety of domains, such as developing customer service chatbots, domain-specific text generation, and improving the performance of large language models for specialized tasks like summarization, translation, or question-answering.

Yes! The course includes practical exercises where you’ll configure fine-tuning recipes, preprocess datasets, and run fine-tuning tasks using libraries like TorchTune. You’ll also work with real-world datasets and explore advanced techniques like LoRA and quantization to make models more efficient, including using GPUs to load models using different format configurations to save memory.