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--- |
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base_model: unsloth/Llama-3.2-11B-Vision-Instruct |
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tags: |
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- text-generation-inference |
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- transformers |
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- unsloth |
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- mllama |
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- vision-language |
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- document-understanding |
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- data-extraction |
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license: apache-2.0 |
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language: |
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- en |
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library_name: transformers |
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--- |
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# Vision-Language Model for Document Data Extraction |
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- **Developed by:** Daemontatox |
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- **License:** apache-2.0 |
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- **Finetuned from model:** unsloth/Llama-3.2-11B-Vision-Instruct |
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## Overview |
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This Vision-Language Model (VLM) is purpose-built for extracting structured and unstructured data from various types of documents, including but not limited to: |
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- Invoices |
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- Timesheets |
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- Contracts |
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- Forms |
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- Receipts |
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By utilizing advanced multimodal learning capabilities, this model understands both text and visual layout features, enabling it to parse even complex document structures. |
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## Key Features |
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1. **Accurate Data Extraction:** |
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- Automatically detects and extracts key fields such as dates, names, amounts, itemized details, and more. |
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- Outputs data in clean and well-structured JSON format. |
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2. **Robust Multimodal Understanding:** |
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- Processes both text and visual layout elements (tables, headers, footers). |
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- Adapts to various document formats and layouts without additional fine-tuning. |
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3. **Optimized Performance:** |
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- Fine-tuned using [Unsloth](https://github.com/unslothai/unsloth), enabling 2x faster training. |
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- Employs Hugging Face’s TRL library for parameter-efficient fine-tuning. |
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4. **Flexible Deployment:** |
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- Compatible with a wide range of platforms for integration into document processing pipelines. |
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- Optimized for inference on GPUs and high-performance environments. |
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## Use Cases |
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- **Enterprise Automation:** Automate data entry and document processing tasks in finance, HR, and legal domains. |
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- **E-invoicing:** Extract critical invoice details for seamless integration with ERP systems. |
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- **Compliance:** Extract and structure data for auditing and regulatory compliance reporting. |
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## Training and Fine-Tuning |
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The fine-tuning process leveraged Unsloth's efficiency optimizations, reducing training time while maintaining high accuracy. The model was trained on a diverse dataset of scanned documents and synthetic examples to ensure robustness across real-world scenarios. |
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## Acknowledgments |
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This model was fine-tuned using the powerful capabilities of the [Unsloth](https://github.com/unslothai/unsloth) framework, which significantly accelerates the training of large models. |
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) |
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