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Fine-Tuning vLLMs for Document Understanding
- This article delves into the fine-tuning of VLMs like Qwen 2.5 VL 7B to optimize performance on tasks like extracting handwritten text.
- The main focus of the article is to fine-tune a VLM on a dataset to improve machine learning techniques and achieve efficient outcomes.
- Topics covered include motivation, advantages of VLMs, dataset overview, annotation, fine-tuning, SFT technical details, results, and plots.
- Motivation revolves around showcasing the process of fine-tuning VLMs for specific tasks like extracting handwritten text for valuable applications like climate research.
- Utilizing VLMs over traditional OCR engines is advantageous due to better performance in extracting text, handling handwriting variations, and providing specific instructions for data extraction.
- Fine-tuning involves a three-step process of prediction, reviewing and correcting mistakes, and retraining the model to improve performance using annotated data efficiently.
- Supervised fine-tuning (SFT) involves updating model weights to improve performance, considering challenges like similar-looking characters, image background noise, and annotation correctness.
- Hyperparameter search and balancing data sets are crucial for optimizing model parameters, and selecting layers for fine-tuning based on specific task requirements, such as OCR for handwritten text extraction.
- Results show that fine-tuning of Qwen model enhances performance over the base model, displayed through better performance on test sets.
- The article concludes with insights into a phenology dataset, the process of extracting handwritten text, model fine-tuning pipeline, results, and data visualization.
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