Using Huggingface for Specialized Models and Fine-tuning

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