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How to fine-tune Vision-Language Models (VLMs) for domain-specific document layout extraction?

Computer Vision & Multimodal AI · 2 saved versions

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Edited by Aravind Patel · Aug 23, 2026 5:06 PM

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How to fine-tune Vision-Language Models (VLMs) for domain-specific document layout extraction?

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LoRA fine-tuning on LLaVA / Qwen-VL for parsing complex invoice and engineering blueprint images.
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### Fine-Tuning Pipeline 1. **Data Prep**: Annotate images with bounding box markdown pairs. 2. **Parameter-Efficient Tuning**: Apply QLoRA to visual adapter layers to adapt model to specialized typography and diagrams without high GPU memory cost. 3. **Evaluation**: Measure IoU (Intersection over Union) on bounding boxes and text extraction edit distance.
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https://developers.google.com/search/docs

Version 1 (Original Post)

Published by Aravind Patel · Aug 9, 2026 5:37 AM

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Original Title

How to fine-tune Vision-Language Models (VLMs) for domain-specific document layout extraction?

Original Summary
LoRA fine-tuning on LLaVA / Qwen-VL for parsing complex invoice and engineering blueprint images.
Original Content
### Fine-Tuning Pipeline 1. **Data Prep**: Annotate images with bounding box markdown pairs. 2. **Parameter-Efficient Tuning**: Apply QLoRA to visual adapter layers to adapt model to specialized typography and diagrams without high GPU memory cost. 3. **Evaluation**: Measure IoU (Intersection over Union) on bounding boxes and text extraction edit distance.
Original Sources

https://developers.google.com/search/docs