Hugging Face Tutorial

Introduction

Hugging Face has emerged as one of the most important platforms in modern artificial intelligence, serving as both a hub for state-of-the-art models and a collaborative community driving AI innovation. This guide will take you through the essential components of the Hugging Face ecosystem, providing hands-on examples across various domains of AI.

What is Hugging Face?

Hugging Face began as a company focused on natural language processing but has evolved into a comprehensive platform that hosts:

• Model Hub: A repository of thousands of pre-trained models
• Datasets: A collection of datasets for training and evaluation
• Spaces: A platform for hosting and sharing machine learning demos
• Transformers library: A Python library that provides APIs to easily download and use pre-trained models
• Tokenizers: Fast and efficient tokenizers optimized for research and production
• AutoTrain: Tools for automatically training and deploying custom models
• Other libraries: Tools like Datasets, Accelerate, and Diffusers that support various AI workflows

Environment Setup

Before we begin with the hands-on examples, let’s set up our environment:

# Install the necessary libraries
pip install transformers datasets accelerate diffusers tokenizers
pip install torch torchvision torchaudio
pip install soundfile librosa
pip install evaluate sacrebleu rouge-score
pip install gradio  # For creating demos
pip install numpy pandas matplotlib seaborn scikit-learn

For GPU acceleration (optional but recommended):

# Check if GPU is available
import torch
print(f"GPU available: {torch.cuda.is_available()}")
print(f"GPU name: {torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'None'}")

Authentication

To interact with the Hugging Face Hub for uploading models, datasets, or accessing private repositories, you’ll need to authenticate:

# Login to Hugging Face Hub
from huggingface_hub import login
login(token="your_token_here")  # Get token from huggingface.co/settings/tokens

# Alternatively, use the CLI
# huggingface-cli login