Large Language Models (Using Huggingface) Course Outline
Overview
This two-day, hands-on course introduces participants to the Hugging Face ecosystem, equipping them with practical skills to find, run, fine-tune, and deploy pre-trained models for real-world applications. Through guided exercises, attendees will explore NLP, vision, and audio models, learn the fundamentals of fine-tuning, and deploy their own models using Hugging Face Spaces.
The course covers the full workflow—from navigating the Hugging Face Hub to preparing datasets, customizing models, and optimizing deployments—ensuring participants gain both conceptual understanding and practical coding experience. By the end of the program, learners will have built and deployed functional AI applications they can adapt for their own projects.
Prerequisites
Basic Python knowledge is required. Familiarity with Jupyter or Google Colab is recommended but not required.
COURSE OUTLINE
Welcome & Course Orientation
Instructor and participant introductions
Course goals and objectives
Hugging Face at a glance: the “GitHub of AI”
Overview of the Hugging Face ecosystem: Hub, Transformers, Datasets, Spaces
Software setup check and Colab/Jupyter introduction
Hugging Face Hub Tour
Navigating the Hub: search, filters, and model cards
Popular tasks: NLP, vision, audio, multimodal
Understanding model tags, licenses, and intended use
Cloning repositories and downloading models locally
Getting Started with Pipelines
What is a pipeline?
Running a sentiment analysis model in 5 lines of code
Other built-in pipelines: summarization, translation, question answering
Parameters and customization options
Performance considerations
Practical Text-Based Use Cases
Zero-shot classification
Summarization and translation
Question answering over custom text
Hands-on: building a small “document query” notebook
Beyond Text: Vision & Audio Models
Image classification with ViT
Image generation with diffusers (Stable Diffusion Lite)
Speech-to-text with Whisper models
Hands-on: choose and run one vision or audio task
Customizing Pre-Trained Models
Changing model configurations
Tokenizer tweaks and preprocessing techniques
Using pipelines vs. model/tokenizer API directly
Exporting and reusing code for automation
Building & Sharing a Demo with Hugging Face Spaces
Spaces overview: Gradio and Streamlit
Creating a simple interface for a pre-trained model
Uploading to Spaces for public or private sharing
Fine-Tuning Fundamentals
Why fine-tune? Benefits over training from scratch
Parameter-efficient fine-tuning (LoRA, QLoRA)
Overview of the Trainer API
Using the `peft` library for LoRA-based tuning
Preparing Your Dataset
Using the datasets library
Loading public datasets from the Hub
Cleaning and tokenizing text
Train/test splits and evaluation metrics
Hands-On Fine-Tuning (Text Model)
Selecting a small model (e.g., DistilBERT)
Setting training arguments in Trainer
Running fine-tuning in Colab
Monitoring training progress
Saving and evaluating the model
Publishing to the Hugging Face Hub
Creating a model card
Uploading model weights and metadata
Versioning and setting permissions
Deployment Pathways
Inference API basics
Using Spaces for deployment
Integrating models into Python or JavaScript applications
Example: deploy fine-tuned sentiment classifier to Spaces
Optimization & Best Practices
Reducing model size for faster inference
Using quantization and pruning techniques
Keeping models updated
Managing costs in production environments
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