
Learn to manage compute for large language models by balancing 32-bit precision, memory demands, and small batch sizes during training.
Access pre-trained models from Hugging Face using the transformers library and pipeline. Load GPT-2 tokenizer and model, then generate text and explore tokenizers, datasets, and generation parameters.
Demonstrate parameter efficient fine tuning with LoRA on a Bart summarization model, achieving faster training and a 33% time reduction by updating only the left adapter.
Train a GPT-2 model from scratch on a Reuters dataset with a custom tokenizer and full articles, using a 512 context and 52k vocabulary, then push to Hugging Face hub.
Welcome to "Learn Hugging Face for Mastering Generative AI with LLMs". In today's AI-driven world, Hugging Face has become a central platform for working with Large Language Models (LLMs), which have revolutionized generative AI by enabling machines to generate human-like text, answer questions, and even create original content. This course is meticulously designed to give you a deep understanding of these models and how to harness their power using Hugging Face.
Our journey begins with a robust introduction to LLMs, exploring their intricacies and how to manage their compute requirements, all within the Hugging Face ecosystem. From there, we dive into the world of Hugging Face, which provides an extensive collection of pre-trained models that can be applied in a wide range of innovative applications.
Practical knowledge is essential, so the course transitions into a deep dive into Transformers, a key technology behind LLMs, with a special focus on Hugging Face implementations. You'll get hands-on experience with Hugging Face tools, manipulating datasets, building custom models, and mastering tokenization.
Finally, we emphasize training, fine-tuning, and quantization, with models downloaded from Hugging Face. Learn how to adjust LLMs to your needs, whether for summarization or text generation. With techniques like Instruction Fine-tuning and PEFT, you'll master the art of fine-tuning models. We’ll even show you how to train a GPT-2 from scratch using Hugging Face to generate text from a custom dataset. Then finally, we will show you how to quantize your models so that they take up less memory.