Daemontatox/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

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.

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Open LLM Leaderboard Evaluation Results

Detailed results can be found here! Summarized results can be found here!

Metric	Value (%)
Average	22.87
IFEval (0-Shot)	43.83
BBH (3-Shot)	29.03
MATH Lvl 5 (4-Shot)	14.43
GPQA (0-shot)	9.84
MuSR (0-shot)	9.25
MMLU-PRO (5-shot)	30.87