初始化项目,由ModelHub XC社区提供模型
Model: Zappandy/dukaan-saathi-receipt-lora Source: Original Platform
This commit is contained in:
82
README.md
Normal file
82
README.md
Normal file
@@ -0,0 +1,82 @@
|
||||
---
|
||||
language:
|
||||
- en
|
||||
- te
|
||||
license: mit
|
||||
base_model: unsloth/Llama-3.2-3B-Instruct-bnb-4bit
|
||||
tags:
|
||||
- receipt-parsing
|
||||
- kirana
|
||||
- inventory
|
||||
- fine-tuned
|
||||
- lora
|
||||
- indian-retail
|
||||
pipeline_tag: text-generation
|
||||
---
|
||||
|
||||
# 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](https://huggingface.co/spaces/summerdevlin46/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:**
|
||||
```json
|
||||
{
|
||||
"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
|
||||
|
||||
```python
|
||||
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
|
||||
Reference in New Issue
Block a user