Hugging Face Tutorial

Hugging Face is an open-source AI platform that provides pre-trained models, datasets and tools to build Natural Language Processing, computer vision, audio and generative AI applications. It helps developers to use AI models without training everything from scratch.

Hugging Face mainly focuses on Models, Datasets and Libraries, along with tools for training, evaluation, prompting and deployment.

Getting Started

This section introduces the platform, environment setup and how pre-trained models are used.

Introduction

• Introduction
• Creating Hugging Face account

Core Tools

• Installing Transformers
• Loading pretrained models from Hub
• Using pipeline API
• Using Inference API
• Saving and loading models locally

Transformer Architecture

• Understanding Transformers
• Attention mechanism basics
• Encoder–decoder architecture
• Model configuration and Parameters
• AutoModel and Auto classes

Tokenization, LLM Foundations and RAG

It covers how text is converted into tokens for model understanding, the basic working principles of large language models and methods to connect them with external knowledge sources to produce more accurate and context-aware responses.

Tokenization and Representations

• What is tokenization
• Types of Tokenizers
• Embeddings and Vector databases

Large Language Models

• What are LLMs
• Encoder models like BERT
• Decoder models like GPT
• Modern open models like LLaMA
• Pre-training objectives
• Transfer learning with Hugging Face

RAG

• Introduction
• RAG vs Closed-Book Models
• RAG Architecture
• Chunking strategies

Core Components

It covers the key tools from Hugging Face such as the Model Hub, Dataset Hub and Model Cards which help developers access models and data, understand their details and build AI applications more easily.

Hugging Face Datasets

• Exploring Datasets Hub
• Dataset Inspection and Visualization
• Creating custom datasets
• Preprocessing and tokenization

Model Hub and Cards

• Exploring the Models Hub
• Understanding model cards

Prompt Engineering and Reasoning with LLMs

It is the practice of designing inputs to guide LLMs toward accurate and relevant responses. It also focuses on helping models think step-by-step so they can solve problems more logically instead of giving surface-level answers.

Prompt Engineering Basics

• What is an AI prompt
• Zero-shot prompting
• One-shot prompting
• Few-shot prompting

Prompting Techniques

• Chain-of-Thought prompting
• Zero Shot Chain-of-Thought prompting
• Role-based prompting
• Contextual prompting

Advanced Prompting Strategies

• ReAct prompting (Reason + Action)
• Self-consistency prompting
• Tree-of-Thought reasoning
• Retrieval-augmented prompting

Prompt Optimization and Guardrails

• Prompt optimization methods
• Prompt Debiasing
• Prompt Injection
• Guardrails in AI systems
• Unified structure for reliable prompts

Building Applications with Hugging Face

This section covers real-world NLP tasks and projects.

Core NLP Tasks

• Text Classification
• Named Entity Recognition
• Question Answering
• Summarization
• Text Generation
• Translation

Computer Vision Tasks

• Object d=Detection Pipelines
• Image Segmentation Tasks
• Text-to-Image Generation
• Text to Video

Audio Tasks

• Audio preprocessing basics
• Whisper speech model
• Wav2Vec2 introduction
• Automatic Speech Recognition
• Audio classification
• Audio Generation
• Building voice translation

Evaluation and Metrics

This module focuses on measuring model performance.

Model Evaluation Techniques

• Using evaluate library
• Human evaluation strategies
• A/B testing models

Evaluation Metrics

• BLEU and ROUGE
• Accuracy, Precision and F1-score
• Task-specific metrics

Fine-Tuning and Advanced Training

It focuses on adapting pre-trained models to specific tasks using custom data, along with training methods that improve performance, efficiency and task accuracy beyond basic model usage.

Fine-Tuning Models

• Fine-tuning workflow
• LoRA and QLoRA
• Using PEFT methods

RL-Based Fine-Tuning

• Reinforcement Learning from Human Feedback (RLHF)
• Instruction tuning
• Constitutional AI
• LLM distillation

Deployment, Interfaces and Production AI

This module explains how to move models to real-world applications.

Deploying Hugging Face Models

• Deploy demo on Spaces
• Creating REST APIs
• Integration with FastAPI and Flask

User Interfaces for AI Apps

• Building apps with Streamlit
• Interactive demos with Gradio

Monitoring and Scaling Models

• Performance Drift
• Monitoring and Scaling AI Models

Resources:

• For learning how large language models, diffusion models and modern AI systems are built, refer to the Generative AI Tutorial for a complete introduction to GenAI concepts and applications.
• To understand how autonomous AI systems plan, reason and use tools, refer to the Agentic AI Tutorial to explore agents, workflows and real-world AI automation.