Large Language Model Fine-Tuning using NVIDIA NeMo (Presented by Domino Data Lab)
, Senior Product Manager, AI, Domino Data Lab
, Director of Solution Architecture, Tech Alliances, Domino Data Lab
We all recognize the immense business opportunity from generative AI and large language models (LLMs) — particularly those trained or developed on proprietary company data. However, developing them is resource-intensive, time-consuming, and requires deep technical expertise. The NVIDIA NeMo Framework accelerates LLM training by up to 30% (models ranging from 22 billion to 1 trillion parameters) — a quick, efficient, containerized framework for model training, evaluation, and inference. In this session, we'll use NVIDIA NeMo with Megatron-gpt — a powerful transformer developed by the Applied Deep Learning Research team at NVIDIA — to fine-tune using parameter efficient fine tuning (PEFT) on Domino’s Enterprise AI Platform. We’ll walk through the end-to-end model life cycle, starting with NVIDIA’s NeMo Toolkit in Domino’s AI Project Hub, customizing a data science environment for fine-tuning in Domino, then using NeMo to encode text prompts and generate task-specific virtual tokens.