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Using Hugging Face for Specialized Models and Fine-tuning
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General
Announcements
Welcome & Course Orientation
Overview: 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
Review: Welcome & Course Orientation
Hugging Face Hub Tour
Overview: 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
Review: Hugging Face Hub Tour
Getting Started with Pipelines
Overview: 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
Review: Getting Started with Pipelines
Practical Text-Based Use Cases
Overview: Practical Text-Based Use Cases
Zero-Shot Classification
Summarization and Translation
Question Answering Over Custom Text
Lab: Building a Small Document Query Notebook
Review: Practical Text-Based Use Cases
Beyond Text: Vision & Audio Models
Overview: Beyond Text: Vision & Audio Models
Image Classification with ViT
Image Generation with Diffusers (Stable Diffusion Lite)
Speech-to-Text with Whisper Models
Lab: Choose and Run One Vision or Audio Task
Review: Beyond Text: Vision & Audio Models
Customizing Pre-Trained Models
Overview: 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
Review: Customizing Pre-Trained Models
Building & Sharing a Demo with Hugging Face Spaces
Overview: Building & Sharing a Demo with Hugging Face Spaces
Spaces Overview: Gradio and Streamlit
Creating a Simple Interface for a Pre-Trained Model
Lab: Uploading to Spaces for Public or Private Sharing
Review: Building & Sharing a Demo with Hugging Face Spaces
Fine-Tuning Fundamentals
Overview: 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
Review: Fine-Tuning Fundamentals
Preparing Your Dataset
Overview: Preparing Your Dataset
Using the Datasets Library
Loading Public Datasets from the Hub
Cleaning and Tokenizing Text
Train/Test Splits and Evaluation Metrics
Review: Preparing Your Dataset
Lab: Fine-Tuning (Text Model)
Overview: Lab: Fine-Tuning (Text Model)
Selecting a Small Model (e.g., DistilBERT)
Setting Training Arguments in Trainer
Running Fine-Tuning in Colab
Monitoring Training Progress
Lab: Saving and Evaluating the Model
Review: Lab: Fine-Tuning (Text Model)
Publishing to the Hugging Face Hub
Overview: Publishing to the Hugging Face Hub
Creating a Model Card
Uploading Model Weights and Metadata
Versioning and Setting Permissions
Review: Publishing to the Hugging Face Hub
Deployment Pathways
Overview: Deployment Pathways
Inference API Basics
Using Spaces for Deployment
Integrating Models into Python or JavaScript Applications
Lab: Deploy Fine-Tuned Sentiment Classifier to Spaces
Review: Deployment Pathways
Optimization & Best Practices
Overview: Optimization & Best Practices
Reducing Model Size for Faster Inference
Using Quantization and Pruning Techniques
Keeping Models Updated
Managing Costs in Production Environments
Review: Optimization & Best Practices
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All categories
Course Demonstrations
Free Mini Courses
AI Learning Hub
AI Essentials
Level 1: Generative AI Foundation
Level 2: Grounding AI with your Data
Level 3: Automating AI Workflows
Office
Webinars
Power BI
Programming
WordPress
Microsoft Official
Accounting
CompTIA
Google
Upskilling Series
ONLC Staff Training
Generative AI Skills
Instructor Review
Private Classes
Home
Course info
Using Hugging Face for Specialized Models and Fine-tuning
A two-day hands-on course introducing participants to the Hugging Face ecosystem, covering how to find, run, fine-tune, and deploy pre-trained models for practical NLP, vision, and audio applications.
Skill Level
:
Beginner
