feat: add code samples for preference tuning #13644
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Summary of ChangesHello @kuirensu-crypto, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request introduces a new code sample for Preference Tuning within the Google GenAI SDK. This is crucial for users looking to leverage Vertex Tuning's advanced optimization methods for generative AI models, specifically demonstrating how to fine-tune models like Highlights
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Code Review
This pull request introduces a new code sample for preference tuning. The overall structure is good, but there are several areas for improvement. Most importantly, the new sample is missing tests, which are crucial for ensuring correctness and preventing future regressions. I've also suggested refactoring the code to accept parameters instead of using hardcoded values, which will improve reusability and make it easier for users to run the sample. Additionally, I've pointed out a potential issue with the API version being used and offered some minor suggestions for improving code style and maintainability.
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Description
Preference Optimization is a new tuning method offered by Vertex Tuning.
Fixes #
Note: Before submitting a pull request, please open an issue for discussion if you are not associated with Google.
Checklist
nox -s py-3.9(see Test Environment Setup)nox -s lint(see Test Environment Setup)