@manuelaschrittwieser4LLM Engineer & Tech Blogger, Founder, Self-Employed
Shipped: LLM Invoice Extractor
An end-to-end LLM-based invoice extraction system that converts unstructured invoice text into structured JSON. I built the training dataset, fine-tuned a Llama 3 model with PEFT/QLoRA, deployed it as an authenticated GPU-backed API on Modal, and added an evaluation client for validating extraction quality. The project is aimed at document-AI and automation workflows where consistent structured output matters. The accompanying dataset contains 600+ training examples, and the fine-tuned model is published on Hugging Face (manuelaschrittwieser/llama-3-invoice-extractor-merged).
LLMFine-TuningDocument AIStructured OutputsMLOpsPEFTModel Deployment
Comments2
Invoices are a pain point for so many small businesses and finance teams. Who do you picture using this first: accountants, bookkeeping firms, or companies handling their own payables? And have you had a chance to show it to anyone who deals with invoices every day?
I imagine that small businesses handling their own accounts payable and accounting firms will be the first users. They process large volumes of repetitive invoice data but often lack the infrastructure or budget for large document processing platforms. I haven't yet had the opportunity to present the project to someone who works with invoices on a daily basis, so that's one of my next steps. I'm particularly interested in identifying the actual operational weaknesses: OCR quality, inconsistent layouts, missing fields, validation, or integration with existing accounting processes. At this stage, the project has primarily been a technical exercise for fine-tuning, creating reliable, structured outputs, evaluation, and implementation. Gathering feedback from accountants or finance teams would be the next step and would help me understand what's needed to transform a technical prototype into a useful product.
2 comments
Invoices are a pain point for so many small businesses and finance teams. Who do you picture using this first: accountants, bookkeeping firms, or companies handling their own payables? And have you had a chance to show it to anyone who deals with invoices every day?
I imagine that small businesses handling their own accounts payable and accounting firms will be the first users. They process large volumes of repetitive invoice data but often lack the infrastructure or budget for large document processing platforms. I haven't yet had the opportunity to present the project to someone who works with invoices on a daily basis, so that's one of my next steps. I'm particularly interested in identifying the actual operational weaknesses: OCR quality, inconsistent layouts, missing fields, validation, or integration with existing accounting processes. At this stage, the project has primarily been a technical exercise for fine-tuning, creating reliable, structured outputs, evaluation, and implementation. Gathering feedback from accountants or finance teams would be the next step and would help me understand what's needed to transform a technical prototype into a useful product.