summerdevlin46/dukaan-saathi-receipt-lora

Dukaan Saathi — Receipt Parser (Llama-3.2-3B fine-tune)

Fine-tuned Llama-3.2-3B-Instruct for structured receipt parsing in Indian kirana (convenience) store workflows.

Part of the Dukaan Saathi inventory copilot demo.

What it does

Takes noisy supplier receipt OCR text and returns a structured JSON object with line items, quantities, prices, and supplier info. Designed for messy real-world receipts: handwritten bills, printed tax invoices, informal tally notes.

Training data

• 6 hand-authored examples from real kirana receipt formats
• 22 Modal LLM-generated synthetic examples augmenting edge cases
• Total: 28 examples; training focuses on format consistency over broad generalisation

Example

Input:

MAHALAKSHMI MARKETING
No. 2816  Date: 27/5/26
Parle  1 X 2450 = 2450
Bingo(C)  4 X 870 = 3480
Subtotal 5930  Discount 612  Total 6542

Output:

{
  "supplier": "Mahalakshmi Marketing",
  "invoice_no": "2816",
  "date": "2026-05-27",
  "items": [
    {"product_raw": "Parle", "qty_cases": 1, "qty_units": 1, "unit_cost": 2450.0, "total": 2450.0},
    {"product_raw": "Bingo(C)", "qty_cases": 4, "qty_units": 4, "unit_cost": 870.0, "total": 3480.0}
  ],
  "subtotal": 5930.0,
  "discount": 612.0,
  "gst": 0.0,
  "net_total": 6542.0
}

Inference

from huggingface_hub import InferenceClient

client = InferenceClient()
prompt = """### Instruction:
You are a receipt parser for an Indian convenience store. Extract all line items. Return ONLY valid JSON, no markdown.

### Input:
<paste receipt text here>

### Response:
"""
result = client.text_generation(prompt, model="summerdevlin46/dukaan-saathi-receipt-lora", max_new_tokens=768)

Limitations

• Small training set; overfits to known receipt styles (Mahalakshmi Marketing, Sri Venkateshwara Marketing, Brundavan Buns)
• Owner approval gate always required before any inventory write
• Not a general-purpose receipt parser