summerdevlin46/dukaan-saathi-receipt-lora
The summerdevlin46/dukaan-saathi-receipt-lora is a 3.2 billion parameter Llama-3.2-3B-Instruct fine-tune, specifically optimized for structured receipt parsing in Indian kirana (convenience) store workflows. This model excels at extracting detailed line items, quantities, prices, and supplier information from noisy, real-world receipt OCR text, including handwritten bills and informal notes. It is designed to convert unstructured receipt data into a structured JSON format, making it highly suitable for inventory management and data entry automation in this specific retail context.
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Overview
This model, summerdevlin46/dukaan-saathi-receipt-lora, is a fine-tuned version of Llama-3.2-3B-Instruct with 3.2 billion parameters, specifically developed for parsing receipts from Indian kirana (convenience) stores. It is part of the Dukaan Saathi inventory copilot demo.
Key Capabilities
- Structured Data Extraction: Converts noisy OCR text from supplier receipts into a structured JSON object.
- Handles Real-World Receipts: Designed to process various formats, including handwritten bills, printed tax invoices, and informal tally notes.
- Detailed Line Item Parsing: Extracts product names, quantities (cases/units), unit costs, and total prices for each item.
- Supplier and Invoice Information: Identifies supplier names, invoice numbers, dates, subtotals, discounts, and net totals.
Training and Limitations
- Focused Training Data: Trained on a small dataset of 6 hand-authored and 22 Modal LLM-generated synthetic examples, totaling 28 examples. This focuses on format consistency for specific receipt styles.
- Specific Use Case: Due to its specialized training, the model is not a general-purpose receipt parser and may overfit to known receipt styles (e.g., Mahalakshmi Marketing, Sri Venkateshwara Marketing, Brundavan Buns).
- Human Oversight: Designed to work within a workflow that includes owner approval before any inventory write, acknowledging its specialized nature and potential for errors outside its trained scope.
When to Use This Model
- Indian Kirana Store Automation: Ideal for automating inventory data entry from supplier receipts in the Indian convenience store context.
- Structured Receipt Data: When you need to convert unstructured receipt text into a consistent, machine-readable JSON format for specific Indian retail scenarios.