NVIDIA NeMo Microservices ULTIMATE Guide for Model Fine-Tuning!
AI Summary
In this tutorial, we explore Nvidia Nemo microservices for efficiently managing data processing, model customization, and evaluation. The focus is on setting up the data flywheel utilizing various Nemo components: The Nemo curator for data processing, Nemo customizer for fine-tuning, Nemo evaluator for assessing model results, and Nemo retriever for managing data pipelines. We demonstrate the installation of Nvidia microservices and preparation of the environment with required APIs and GPUs, including a setup through Jupyter Lab. The tutorial details how to prepare a dataset for training a model using the Llama 3.2 instruct model while introducing function calling abilities. Key steps include downloading necessary data, configuring URLs, and executing fine-tuning processes. Overall, the video illustrates the ease of implementing advanced AI applications using Nemo, with practical examples and stats showing improvements in speed and compliance. The final output allows users to fine-tune models seamlessly and utilize them for tool calling tasks.