README.md

---
license: apache-2.0
language:
- en
base_model: Qwen/Qwen2.5-7B-Instruct
pipeline_tag: text-generation
tags:
- finance
- document-parsing
- qlora
- qwen2.5
- invoice
- sap
- structured-extraction
- json
---

# 🏦 Multi-Format Finance Document Parser

A production-grade financial document parser fine-tuned on **Qwen2.5-7B-Instruct** using **QLoRA (4-bit NF4 quantization)**. Given raw text from any financial document, it outputs structured JSON — ready for downstream processing, ERP integration, or analytics pipelines.

---

## 🚀 Live Demo

👉 [Try it on HuggingFace Spaces](https://huggingface.co/spaces/ratulsur/finance-parser-demo)

---

## 📄 Supported Document Types

| Format | Examples |
|---|---|
| **Invoice** | Vendor invoices, GST bills, service bills |
| **SAP Report** | ALV exports, FI vendor payment reports |
| **Income Statement** | P&L statements, quarterly earnings |
| **Balance Sheet** | Assets, liabilities, equity statements |
| **Bank Statement** | Transaction records, account summaries |
| **Purchase Order** | PO documents, procurement records |
| **SQL Result** | Query outputs from finance databases |
| **CSV / Excel** | Tabular finance data |

---

## 🧠 Model Details

| Property | Value |
|---|---|
| **Base model** | Qwen/Qwen2.5-7B-Instruct |
| **Model size** | 8B parameters |
| **Fine-tuning method** | QLoRA (PEFT) |
| **Quantization** | 4-bit NF4 + double quantization |
| **Compute dtype** | bfloat16 |
| **LoRA rank** | r=8, alpha=16 |
| **Max sequence length** | 512 tokens |
| **Training hardware** | L40S 48GB GPU (Lightning AI) |
| **Training time** | ~1 hour |
| **License** | Apache 2.0 |

---

## 📊 Training Data

| Dataset | Samples | Type |
|---|---|---|
| [CORD-v2](https://huggingface.co/datasets/naver-clova-ix/cord-v2) | 454 | Real receipt images + structured JSON |
| Synthetic invoices | 300 | Generated with realistic Indian/global vendors |
| Synthetic SAP reports | 100 | ALV-style pipe-delimited exports |
| Synthetic income statements | 100 | P&L with revenue, COGS, EBIT, net income |
| **Total** | **954** | Train: 812 · Eval: 95 · Test: 47 |

---

## ⚙️ Quantization Techniques

| Technique | Purpose |
|---|---|
| **NF4 4-bit quantization** | Stores weights in 4-bit NormalFloat format — ~4x model size reduction |
| **Double quantization** | Quantizes the quantization constants — additional ~0.4 bits/param saving |
| **bfloat16 compute** | Full precision operations, 4-bit storage |
| **LoRA adapters (r=8)** | Only 0.5% of parameters trained — 99.5% frozen |
| **Paged AdamW 8-bit** | Optimizer state memory reduction |
| **Gradient checkpointing** | ~40% activation memory reduction |

---

## 📤 Output Schema

```json
{
  "document_type": "invoice|balance_sheet|income_stmt|sap_report|sql_result|bank_statement|purchase_order",
  "vendor": "string or null",
  "client": "string or null",
  "date": "YYYY-MM-DD or null",
  "due_date": "YYYY-MM-DD or null",
  "document_id": "string or null",
  "currency": "USD|EUR|INR|GBP|...",
  "subtotal": "float or null",
  "tax_amount": "float or null",
  "tax_rate_pct": "float or null",
  "total_amount": "float or null",
  "line_items": [
    {
      "description": "string",
      "quantity": "float or null",
      "unit_price": "float or null",
      "amount": "float"
    }
  ],
  "payment_terms": "string or null",
  "notes": "string or null",
  "metadata": {}
}
```

---

## 💻 Usage

### Via HuggingFace Inference API

```python
import requests
import json
import re

API_URL = "https://api-inference.huggingface.co/models/ratulsur/multi-format-finance-parser"
HF_TOKEN = "hf_xxxxxxxxxxxx"

SYSTEM_PROMPT = """You are a production financial document parser.
Given raw text from any financial document, output ONLY a single valid JSON object.
Schema: {document_type, vendor, client, date (YYYY-MM-DD), due_date, document_id,
currency, subtotal, tax_amount, tax_rate_pct, total_amount,
line_items:[{description,quantity,unit_price,amount}], payment_terms, notes, metadata}.
All monetary values must be floats. Unknown fields → null. No explanation."""

def parse_document(text: str) -> dict:
    prompt = (
        f"<|im_start|>system\n{SYSTEM_PROMPT}<|im_end|>\n"
        f"<|im_start|>user\nParse this financial document:\n\n{text}<|im_end|>\n"
        f"<|im_start|>assistant\n"
    )
    headers = {"Authorization": f"Bearer {HF_TOKEN}"}
    payload = {
        "inputs": prompt,
        "parameters": {
            "max_new_tokens": 512,
            "temperature": 0.05,
            "return_full_text": False,
            "do_sample": False,
        }
    }
    resp = requests.post(API_URL, headers=headers, json=payload, timeout=120)
    raw = resp.json()[0]["generated_text"].strip()
    raw = re.sub(r"```json\s*|```\s*", "", raw).strip()
    return json.loads(raw)

# Example
invoice = """
INVOICE
Vendor: Tata Consultancy Services Ltd.
Invoice No: TCS-2024-8821
Date: 2024-11-15
Service: Cloud Infrastructure Management   INR 42,500.00
GST @ 18%:                                 INR  7,650.00
TOTAL DUE:                                 INR 50,150.00
Payment Terms: Net 30
"""

result = parse_document(invoice)
print(json.dumps(result, indent=2))
```

### Expected output

```json
{
  "document_type": "invoice",
  "vendor": "Tata Consultancy Services Ltd.",
  "client": null,
  "date": "2024-11-15",
  "due_date": null,
  "document_id": "TCS-2024-8821",
  "currency": "INR",
  "subtotal": 42500.0,
  "tax_amount": 7650.0,
  "tax_rate_pct": 18.0,
  "total_amount": 50150.0,
  "line_items": [
    {
      "description": "Cloud Infrastructure Management",
      "quantity": 1,
      "unit_price": 42500.0,
      "amount": 42500.0
    }
  ],
  "payment_terms": "Net 30",
  "notes": null,
  "metadata": {}
}
```

### Load locally with transformers

```python
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True,
    bnb_4bit_compute_dtype=torch.bfloat16,
)

model = AutoModelForCausalLM.from_pretrained(
    "ratulsur/multi-format-finance-parser",
    quantization_config=bnb_config,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(
    "ratulsur/multi-format-finance-parser",
    trust_remote_code=True,
)
```

---

## 🏗️ Training Setup

```python
# QLoRA config
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True,
    bnb_4bit_compute_dtype=torch.bfloat16,
)

lora_config = LoraConfig(
    r=8,
    lora_alpha=16,
    target_modules=["q_proj","k_proj","v_proj","o_proj",
                    "gate_proj","up_proj","down_proj"],
    lora_dropout=0.05,
    bias="none",
    task_type=TaskType.CAUSAL_LM,
)

# Training args
SFTConfig(
    num_train_epochs=3,
    per_device_train_batch_size=1,
    gradient_accumulation_steps=8,
    learning_rate=2e-4,
    lr_scheduler_type="cosine",
    optim="paged_adamw_8bit",
    bf16=True,
    gradient_checkpointing=True,
    max_length=512,
)
```

---

## 📁 Repository Structure

```
ratulsur/multi-format-finance-parser/
├── model.safetensors      # Merged model weights (15.2 GB)
├── config.json            # Model configuration
├── tokenizer.json         # Tokenizer
├── tokenizer_config.json  # Tokenizer configuration
├── chat_template.jinja    # Chat template
└── generation_config.json # Generation configuration
```

---

## ⚠️ Limitations

- Trained primarily on English financial documents
- Best performance on structured text (not handwritten documents)
- OCR quality affects accuracy for scanned documents
- SAP reports tested on ALV-style exports only
- 954 training samples — production use should involve more data

---

## 🔗 Links

- **Live Demo:** [HuggingFace Spaces](https://huggingface.co/spaces/ratulsur/finance-parser-demo)
- **Base Model:** [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)
- **Training Dataset:** [CORD-v2](https://huggingface.co/datasets/naver-clova-ix/cord-v2)

---

## 👤 Author

**Ratul Sur**
- HuggingFace: [ratulsur](https://huggingface.co/ratulsur)

---

*If you find this model useful, please give it a ⭐ like on HuggingFace!*