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

Introduction to Fine-Tuning

• Defining fine-tuning: what it is and how it works.
• Key use cases and the benefits of fine-tuning.
• An overview of pre-trained models and transfer learning concepts.

Preparation for Fine-Tuning

• Methods for collecting and cleaning datasets.
• Understanding task-specific data requirements.
• Conducting exploratory data analysis and preprocessing.

Fine-Tuning Techniques

• Utilizing transfer learning and feature extraction.
• Fine-tuning transformers using Hugging Face.
• Comparing fine-tuning approaches for supervised vs unsupervised tasks.

Fine-Tuning Large Language Models (LLMs)

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

Optimization and Evaluation

• Hyperparameter tuning strategies.
• Evaluating model performance metrics.
• Mitigating overfitting and underfitting issues.

Scaling Fine-Tuning Efforts

• Fine-tuning on distributed systems.
• Leveraging cloud-based solutions for scalability.
• Case studies: Insights from large-scale fine-tuning projects.

Best Practices and Challenges

• Best practices for ensuring fine-tuning success.
• Identifying common challenges and troubleshooting methods.
• Ethical considerations in fine-tuning AI models.

Advanced Topics (Optional)

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

Summary and Next Steps