Data Generation Strategies
Hamel discusses the importance of curating data to avoid wasting compute resources and highlights that even a modest dataset of around 1,000 examples can yield impressive results. He shares insights on using powerful models like GPT-4 to synthetically expand datasets, emphasizing the flexibility of modern approaches compared to classical machine learning's reliance on extensive labeling. Additionally, he introduces Lora as a valuable technique for fine-tuning models, providing an alternative to traditional methods.In this clip
From this podcast
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Building Real-World LLM Products with Fine-Tuning and More with Hamel Husain - 694
Related Questions
Have you seen any good techniques for automatically optimizing few-shot examples for large language models (LLMs) in the episode Building Real-World LLM Products with Fine-Tuning and More with Hamel Husain - 694?
Have you seen any good techniques for automatically optimizing few-shot examples for large language models (LLMs) in the episode Building Real-World LLM Products with Fine-Tuning and More with Hamel Husain - 694 and the clip Data Generation Strategies?