Prince Sahu@princesahu0004

In the rapidly evolving landscape of document processing, businesses are continually seeking innovative solutions to enhance efficiency, reduce manual workload, and ensure the accuracy of data extraction from crucial documents like invoices. The document parsing application, powered by Falcon LLM, emerges as a standout solution in this domain, delivering unparalleled precision in interpreting and extracting information from varied invoice formats. Falcon LLM, a cutting-edge language learning model, is renowned for its capability to grasp and interpret the complexities of human language. This application harnesses the full potential of Falcon LLM, but it goes a step further by employing advanced fine-tuning techniques such as Parameter Efficient Fine-tuning (PEFT) and Quantized Low-Rank Adaptation(QLORA). These techniques enable the model to adapt to the specific nuances and variations present in different invoice formats, ensuring a high level of accuracy across diverse datasets. Hosting the application on Streamlit brings an additional layer of user-friendliness and accessibility to the table. Streamlit is known for its ability to rapidly deploy data applications with minimal setup, and in this case, it provides an intuitive web interface for interacting with the document parsing application. Users can upload invoices directly through the Streamlit interface, initiate the parsing process, and receive the extracted data in real-time. This not only simplifies the user experience but also makes the powerful capabilities of Falcon LLM and the fine-tuned model accessible to a broader audience, regardless of their technical expertise. The implementation of this document parsing application represents a significant leap forward in automating and optimizing the invoice processing workflow.By leveraging Falcon LLM, fine-tuning with PEFT and QLORA, providing an API endpoint for easy integration, and hosting the solution on Streamlit,it provides a friendly solution