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Why Fine Tuning is Dead w/Emmanuel Ameisen

Arguments for why fine-tuning has become less useful over time, as well as some opinions as to where the field is going with Emmanuel Ameisen. Slides, notes, and additional resources are here: https://parlance-labs.com/education/f... 00:00: Introduction and Background 01:23: Disclaimers and Opinions 01:53: Main Themes: Trends, Performance, and Difficulty 02:53: Trends in Machine Learning 03:16: Evolution of Machine Learning Practices 06:03: The Rise of Large Language Models (LLMs) 08:18: Embedding Models and Fine-Tuning 11:17: Benchmarking Prompts vs. Fine-Tuning 12:23: Fine-Tuning vs. RAG: A Comparative Analysis 25:03: Adding Knowledge to Models 33:14: Moving Targets: The Challenge of Fine-Tuning 38:10: Essential ML Practices: Data and Engineering 44:43: Trends in Model Prices and Context Sizes 47:22: Future Prospects of Fine-Tuning