Train an LLM From Scratch On NVIDIA Jetson Nano (Step-by-Step Guide)

Timestamps: 00:00 - Intro 01:08 - Setting Up Our Environment 05:30 - Installing Pytorch 13:50 - Testing Pytorch 16:03 - Understanding Tokenization 23:24 - Data Set Prep 24:20 - Training Intro 27:33 - Training Setup 33:48 - Jetson Optimizations 36:10 - Training The Model 40:05 - Running The Model 45:38 - Closing Thoughts In this video, we take the NVIDIA Jetson Nano to the next level by training a Large Language Model (LLM) completely from scratch! Using the lightweight and efficient NanoGPT framework, we walk you through the entire process, from setting up your environment to running your very own trained model. We begin by preparing the Jetson Nano with all the necessary tools, including installing and testing PyTorch. Then, we dive into the exciting world of tokenization, dataset preparation, and optimizing the Jetson Nano for training efficiency. Finally, we showcase the training process and demonstrate how to run the trained model directly on the device. This guide is perfect for beginners and AI enthusiasts looking to explore LLM training on affordable hardware. With a focus on step-by-step instructions, you’ll be able to replicate the process and unlock the power of local LLMs on your Jetson Nano.