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Summary of ChangesHello, 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 addresses a bug in video multimedia processing by ensuring that video data is preprocessed using the correct dedicated function. Additionally, it refines the handling of multimodal input features by dynamically incorporating VLM-specific fields, which enhances the robustness and extensibility of the data transformation process for vision-language models. Highlights
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Code Review
This pull request addresses a bug in multimedia video processing. The change in src/twinkle/template/base.py is a good fix, correctly using preprocess_videos for video data. The update in src/twinkle/processor/base.py improves robustness by using the VLM_CONCAT_FIELDS set, making the code more maintainable. I have left one comment suggesting a minor performance optimization. To ensure the stability of this fix, consider adding a unit test case for video data processing to tests/processor/test_processor.py.
| _keys = [ | ||
| 'input_ids', 'input_embeddings', 'attention_mask', 'position_ids', 'labels', 'completion_mask', | ||
| 'pixel_values', 'image_grid_thw' | ||
| ] | ||
| 'input_ids', | ||
| 'input_embeddings', | ||
| 'attention_mask', | ||
| 'position_ids', | ||
| 'labels', | ||
| 'completion_mask', | ||
| ] + list(InputProcessor.VLM_CONCAT_FIELDS) |
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