This repository contains Jupyter notebooks for the courses "Hugging Face in 4 Hours" by Sinan Ozdemir. Published by Pearson, the course covers effective best practices and industry case studies in using Large Language Models (LLMs) from Hugging Face.
Hugging Face is the world’s largest hub for modern AI models and provides access for anyone to use, train, and deploy these models with ease! This course is a gateway to mastering Hugging Face's tools for NLP, offering an inclusive curriculum for non-developers and developers alike to learn the ecosystem. With a spotlight on interactive learning and practical application, attendees will acquire the skills to fine-tune pre-trained models for a variety of NLP tasks and understand how to deploy these models with efficiency.
This class covers:
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Comprehensive Introduction to Hugging Face: Discover the ins and outs of one of the most popular platforms for advanced NLP, with easy-to-follow modules tailored for beginners and valuable insights for experienced developers.
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Practical, Step-by-Step Guides: Engage with intuitive, step-by-step guides on fine-tuning and deploying AI models, focusing on real-world applications like language translation, chatbots, and text analysis.
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Inclusive Learning Environment: Benefit from a learning experience designed for a wide audience, offering both the foundational understanding necessary for business professionals and the technical depth desired by developers.
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Community and Collaboration: Learn how a vibrant community can enrich your AI projects, whether you're contributing as a hobbyist or integrating collaboration into your professional workflow.
- Jupyter notebooks can be run alongside the instructor, but you can also follow along without coding by viewing pre-run notebooks here.
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Intro to HF.ipynb: Introduction to Hugging Face -
Prototyping with HF.ipynb: Prototyping with Hugging Face -
BERT vs GPT: Fine-tuning BERT for Classification -
Multimodality with HF.ipynb: A brief workshop in using some multi-modal models from HF
- Advanced:
fine_tuning_llama_2: A workshop in fine-tuning Llama 2 with instructional data and incorporating further pre-training to update it's knowledge base
- See this README for info on how to run our streamlit app