Fine tune 20 Llama Models in 5 Minutes: Santosh Radha

The complexity and scarcity of deploying GPUs can bring AI development to a standstill. What if there was a better way to train, fine-tune, and serve models on accelerated compute infrastructure entirely in Python? See how during this session where we fine-tune 20 Llama models without doing any infrastructure work. Startups and enterprises can finally gain unprecedented speed and agility to build, iterate, and deploy anything that they can imagine, from multi-agent, multi-modal AI applications, to digital twins for real world simulation. What used to take weeks with dozens of best-in-class engineers can now be accomplished in hours from a single notebook. Recorded live in San Francisco at the AI Engineer World's Fair. See the full schedule of talks at https://www.ai.engineer/worldsfair/2024/schedule & join us at the AI Engineer World's Fair in 2025! Get your tickets today at https://ai.engineer/2025 About Santosh Santosh is the Head of Product/Research at Agnostiq, where he plays a pivotal role in shaping the company's product strategy, particularly through the development of Covalent which is designed to significantly enhance the scalability and performance of next-generation AI applications and large-scale scientific simulations across multi-cloud environments. Santosh holds a Ph.D in theoretical physics from Case Western Reserve University.