Course Details
Fine-Tuning OpenAI and Hugging Face Models
This intensive hands-on workshop teaches participants how to fine-tune OpenAI and Hugging Face models for real-world applications. Participants will gain practical skills in preparing datasets, selecting pre-trained models, applying fine-tuning techniques, and evaluating model performance. The workshop emphasizes project-based learning, with exercises covering domain-specific use cases such as customer support automation, content generation, and workflow optimization. Participants will deploy fine-tuned models and integrate them into real-world applications, ensuring immediate applicability. By the end of the workshop, attendees will be capable of implementing customized AI solutions tailored to organizational needs, boosting efficiency, accuracy, and innovation in business processes.
Learning Outcomes:
Course Outline:
Overview of LLMs: GPT, Hugging Face transformers
Benefits and use-cases of fine-tuned models
Case studies across industries
OpenAI account setup & API key management
Hugging Face account setup & repository creation
Installing Python, PyTorch, and Transformers library
Introduction to Jupyter Notebooks and Colab
Types of datasets (structured, unstructured, text, CSV)
Data annotation basics
Dataset cleaning and formatting
Create accounts, install tools, and load sample datasets
Explore a pre-trained Hugging Face model
Text tokenization and embeddings
Dataset splitting: training, validation, test sets
Handling large datasets efficiently
Choosing appropriate OpenAI or Hugging Face models
Understanding model size, layers, and capabilities
Transfer learning principles
Supervised fine-tuning
Low-rank adaptation (LoRA) and parameter-efficient tuning
Avoiding overfitting and underfitting
Prepare a dataset for a customer support automation task
Select a pre-trained model and configure fine-tuning parameters
Training workflow with Hugging Face Trainer
Using OpenAI fine-tuning API
Monitoring training progress and metrics
Metrics: accuracy, F1-score, perplexity
Testing models on unseen datasets
Iterative improvement through prompt adjustment
Handling errors and convergence issues
Hyperparameter tuning for optimal performance
Fine-tune a model for a real dataset (e.g., FAQ chatbot)
Evaluate and optimize outputs
Deploying Hugging Face models with API endpoints
Using OpenAI fine-tuned models in applications
Integration with web apps, dashboards, or workflow tools
Define use-case
Fine-tune, test, and deploy chatbot model
Integrate with messaging platforms
Connecting models to Google Workspace, Excel, or internal tools
Creating triggers and automated responses
Deploy fine-tuned chatbot and test workflow integration
Debug deployment issues and finalize project
Multi-task fine-tuning
Domain adaptation strategies
Combining multiple models
Identify a workplace problem (report summarization, recommendations, etc.)
Prepare dataset, fine-tune model, and deploy solution
Test and optimize for accuracy and reliability
Scaling models for production
Cost and resource optimization
Ethics, bias mitigation, and responsible AI usage
Complete capstone project
Present project to peers and receive feedback
Review and discussion of tools and concepts covered.
Q&A session to address any questions.
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