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Daemontatox / PixelParse_AI
Model's Last Updated: Dezembro 30 2024
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Introduction of PixelParse_AI
Model Details of PixelParse_AI
Vision-Language Model for Document Data Extraction
- Developed by: Daemontatox
- License: apache-2.0
- Finetuned from model: unsloth/Llama-3.2-11B-Vision-Instruct
Overview
This Vision-Language Model (VLM) is purpose-built for extracting structured and unstructured data from various types of documents, including but not limited to:
- Invoices
- Timesheets
- Contracts
- Forms
- Receipts
By utilizing advanced multimodal learning capabilities, this model understands both text and visual layout features, enabling it to parse even complex document structures.
Key Features
-
Accurate Data Extraction:
- Automatically detects and extracts key fields such as dates, names, amounts, itemized details, and more.
- Outputs data in clean and well-structured JSON format.
-
Robust Multimodal Understanding:
- Processes both text and visual layout elements (tables, headers, footers).
- Adapts to various document formats and layouts without additional fine-tuning.
-
Optimized Performance:
- Fine-tuned using Unsloth , enabling 2x faster training.
- Employs Hugging Face’s TRL library for parameter-efficient fine-tuning.
-
Flexible Deployment:
- Compatible with a wide range of platforms for integration into document processing pipelines.
- Optimized for inference on GPUs and high-performance environments.
Use Cases
- Enterprise Automation: Automate data entry and document processing tasks in finance, HR, and legal domains.
- E-invoicing: Extract critical invoice details for seamless integration with ERP systems.
- Compliance: Extract and structure data for auditing and regulatory compliance reporting.
Training and Fine-Tuning
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.
Acknowledgments
This model was fine-tuned using the powerful capabilities of the Unsloth framework, which significantly accelerates the training of large models.
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