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PixelParse_AI

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mllama
text-generation-inference
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metadata
base_model: unsloth/Llama-3.2-11B-Vision-Instruct
tags:
  - text-generation-inference
  - transformers
  - unsloth
  - mllama
  - vision-language
  - document-understanding
  - data-extraction
license: apache-2.0
language:
  - en
library_name: transformers

image

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

  1. 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.

  2. Robust Multimodal Understanding:

    • Processes both text and visual layout elements (tables, headers, footers).
    • Adapts to various document formats and layouts without additional fine-tuning.

  3. Optimized Performance:

    • Fine-tuned using Unsloth, enabling 2x faster training.
    • Employs Hugging Face’s TRL library for parameter-efficient fine-tuning.

  4. 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.