Modifying Foundation Models
Various methods for modifying foundation models during training are discussed, including fine-tuning and reinforcement learning from human feedback, which relies on human-generated data for optimization. Additionally, the conversation highlights deployment strategies that can enhance model responses without altering weights, such as adjusting parameters like temperature and utilizing retrieval-augmented generation. These insights provide a comprehensive understanding of both training and deployment techniques in machine learning.In this clip
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