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

Introduction to Refining

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

Preparing for Refining

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

Refining Techniques

• Transfer learning and feature extraction
• Refining transformers with Hugging Face
• Refining for supervised vs unsupervised tasks

Refining 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 Refining Efforts

• Refining on distributed systems
• Leveraging cloud-based solutions for scalability
• Case studies: Large-scale refining projects

Best Practices and Challenges

• Best practices for refining success
• Common challenges and troubleshooting
• Ethical considerations in refining AI models

Advanced Topics (Optional)

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

Summary and Next Steps