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

Introduction to Refinement

• What constitutes refinement?
• Use cases and benefits of refinement
• Overview of pre-trained models and transfer learning

Preparing for Refinement

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

Refinement Techniques

• Transfer learning and feature extraction
• Refining transformers using Hugging Face
• Refinement for supervised versus unsupervised tasks

Refining Large Language Models (LLMs)

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

Optimisation and Evaluation

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

Scaling Refinement Efforts

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

Best Practices and Challenges

• Best practices for successful refinement
• 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