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---
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
---
# 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](https://github.com/unslothai/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](https://github.com/unslothai/unsloth) framework, which significantly accelerates the training of large models.
[<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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