Fine-Tuning Models and Large Language Models (LLMs) Training Course

Introduction to Fine-Tuning

• What is fine-tuning?
• Use cases and benefits of fine-tuning
• Overview of pre-trained models and transfer learning

Preparing for Fine-Tuning

• Collecting and cleaning datasets
• Understanding task-specific data requirements
• Exploratory data analysis and preprocessing

Fine-Tuning Techniques

• Transfer learning and feature extraction
• Fine-tuning transformers with Hugging Face
• Fine-tuning for supervised vs unsupervised tasks

Fine-Tuning Large Language Models (LLMs)

• Adapting LLMs for NLP tasks (e.g., text classification, summarization)
• Training LLMs with custom datasets
• Controlling LLM behavior with prompt engineering

Optimization and Evaluation

• Hyperparameter tuning
• Evaluating model performance
• Addressing overfitting and underfitting

Scaling Fine-Tuning Efforts

• Fine-tuning on distributed systems
• Leveraging cloud-based solutions for scalability
• Case studies: Large-scale fine-tuning projects

Best Practices and Challenges

• Best practices for fine-tuning success
• Common challenges and troubleshooting
• Ethical considerations in fine-tuning AI models

Advanced Topics (Optional)

• Fine-tuning multi-modal models
• Zero-shot and few-shot learning
• Exploring LoRA (Low-Rank Adaptation) techniques

Summary and Next Steps