Learn to generate and utilize synthetic data to enhance Large Language Model pretraining and fine-tuning. This course covers techniques for creating high-quality artificial datasets, improving model performance, and addressing data scarcity.
Prerequisites Basic Python, LLM familiarity
Level:
Synthetic Data Generation Techniques
Understand and implement various methods for generating synthetic text data suitable for LLMs.
LLM Pretraining with Synthetic Data
Apply synthetic datasets to augment and improve the pretraining phase of Large Language Models.
LLM Fine-Tuning with Synthetic Data
Utilize synthetic data to effectively fine-tune LLMs for specific tasks and behaviors, including instruction following.
Synthetic Data Quality Evaluation
Develop skills to assess the quality, diversity, and utility of generated synthetic data.
Practical Implementation
Gain hands-on experience in building pipelines for synthetic data generation and its application in LLM workflows.