ORPO enhances supervised fine tuning for combined preference learning. #MachineLearning

The content provided includes links to various repositories and resources related to advanced fine-tuning scripts, evaluation templates, inference guides, function-calling models, newsletters, and support options. The video resources cover topics such as preference and supervised fine-tuning, a history of fine-tuning methods, understanding loss functions, and the benefits of preference fine-tuning. The video also includes a notebook demo of supervised fine-tuning and odds ratio loss functions, evaluation using lm-evaluation-harness, and comparisons between different fine-tuning methods. The timestamps highlight key points discussed in the video, such as the importance of preference fine-tuning and the benefits of using ORPO. Overall, the content provides valuable information and resources for individuals interested in advanced fine-tuning methods and evaluation techniques.

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Source link: https://www.youtube.com/watch?v=OWMJ0rBUj04