Tuning AI Models
Ankur explains the distinction between instruction tuning and fine tuning, highlighting the complexities and risks associated with the latter. He notes a growing interest in open source models, although practical adoption remains limited. Elad emphasizes the importance of understanding market dynamics, particularly for enterprises deploying AI products at scale.In this clip
From this podcast
No Priors
No Priors Ep. 85 | CEO of Braintrust Ankur Goyal
Related Questions
Can you explain the differences between fine-tuning and training models, specifically LLMs vs custom model applications, and when to use each in the context of the episodes Treating Prompt Engineering More Like Code // Maxime Beauchemin // MLOps Podcast #167 and Fine-tuning Models as well as the episode Navigating Machine Learning Careers: Insights from Meta to Consulting // Ilya Reznik // #286 and the clip Fine Tuning Insights?
I have a question about the episode Navigating Machine Learning Careers: Insights from Meta to Consulting // Ilya Reznik // #286 and the clip Fine Tuning Insights. Can you explain the differences between fine-tuning and training models: LLMs vs custom model applications and when to use each?