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a-mo-yehia/
Qwen3-VL-8B-Contracts-OCR

Name: Qwen3-VL-8B-Contracts-OCR
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Egyptian Company Incorporation Contract Information Extraction

This model is a fine-tuned Qwen3-VL-8B model specialized for extracting structured information from scanned Egyptian company incorporation contracts written in Arabic.

Instead of performing traditional OCR only, the model directly understands document images and generates a structured JSON representation of the contract.

GGUF Version

This repository contains the Transformers (Safetensors) version of the model.

Looking for GGUF quantizations for llama.cpp, LM Studio, or other GGUF-compatible runtimes

Check : 👉Qwen3-VL-8B-Contracts-OCR-GGUF

Features

Extracts structured information directly from scanned contract pages
Designed specifically for Egyptian company incorporation contracts
Supports Arabic legal documents
Produces valid JSON output
Preserves Arabic text exactly as it appears in the document
Extracts tables as Markdown inside the JSON response
Handles multi-page contracts by processing two pages simultaneously

Model Details

Base Model

Qwen3-VL-8B

Fine-tuning Framework

Unsloth
LoRA
Transformers
PyTorch

Task

Vision-Language Information Extraction

Training Data

The model was fine-tuned on a private dataset consisting of Egyptian company incorporation contracts.

Dataset statistics:

32 original contracts
Documents converted from PDF into page images
Sliding-window chunk generation
Each training sample consists of:

Image(page_i)
Image(page_i+1)
Target JSON

Example:

Page1 + Page2 -> JSON
Page2 + Page3 -> JSON
Page3 + Page4 -> JSON
...

The original dataset produced approximately 480 training chunks.

Extensive image augmentation was applied including:

Rotation
Brightness variation
Contrast variation
Gaussian noise
JPEG compression simulation

Final dataset size:

approximately 1900 image pairs

Training Configuration

Parameter	Value
LoRA Rank	16
LoRA Alpha	32
Dropout	0
Epochs	4
Learning Rate	2e-4
Optimizer	AdamW 8-bit
Scheduler	Cosine
Warmup Ratio	0.05
Effective Batch Size	8
Max Sequence Length	4096

The model was trained using Unsloth with gradient checkpointing for efficient memory usage.

Supported Input

The model accepts document images such as:

PNG
JPEG
PDF

PDF documents should first be converted into images.

Inference is designed to receive two consecutive pages at a time.

Output Format

The model generates structured JSON.

Characteristics:

Arabic text is copied exactly as it appears.
No spelling correction.
Missing fields are returned as empty strings.
Tables are preserved as Markdown inside the JSON.
Articles spanning multiple pages are marked as partial when applicable.

Example Output

json

{
  "company_name": "",
  "company_address": "",
  "company_type": "",
  "law_reference": {
    "law_number": "",
    "law_year": ""
  },
  "company_capital": "",
  "commercial_register_number": "",
  "approval_date": "",
  "real_estate_registry_minutes_number": "",
  "company_duration": "",
  "company_activity": "",
  "articles": [
    {
      "id": "",
      "value": ""
    }
    ,...
  ]
}

Usage

python

import gc
import json
import torch
from unsloth import FastVisionModel
from qwen_vl_utils import process_vision_info
from PIL import Image

model, tokenizer = FastVisionModel.from_pretrained(
    "a-mo-yehia/contract_OCR_model",
    load_in_4bit=False,
)

FastVisionModel.for_inference(model)

img1 = Image.open("page1.jpg").convert("RGB")
img2 = Image.open("page2.jpg").convert("RGB")

SYSTEM_PROMPT = """\
أنت نظام استخراج بيانات من عقود تأسيس شركات مصرية مسحوبة ضوئياً.

الوثيقة أمامك هي صفحتان من عقد تأسيس شركة مصرية مكتوبة بالعربية.

─── بنية المستند ───
- يبدأ العقد بديباجة (تمهيد) تذكر القانون المنظِّم للشركة.
- تأتي بعدها مواد مرقّمة (مادة ١، مادة ٢، ...) تحتوي على كل التفاصيل.
- اسم الشركة ونوعها: عادةً في المواد الأولى (١–٣).
- غرض الشركة ونشاطها: في مادة الغرض (المادة ٣ أو ٤ غالباً).
- العنوان: في المادة التي تذكر "المركز الرئيسي".
- رأس المال وتوزيعه بين الشركاء: في مادة واحدة تحتوي عادةً على جدول.
- مدة الشركة: في المادة التي تذكر السنوات أو الأجل.
- رقم القانون: في التمهيد أو أوائل المواد، صيغته "القانون رقم XXX لسنة XXXX".

─── قواعد الاستخراج ───
- انسخ كل نص عربي حرفياً كما يظهر في الصورة — لا تصحّح ولا تُعيد صياغة.
- إذا كانت مادة ممتدة وانتهت الصفحة قبل اكتمالها: "partial": true.
- إذا لم يوجد حقل في الصفحتين: اتركه سلسلة فارغة "".
- إذا لم تكن هناك مواد في الصفحتين: أرجع "articles": [].
- إذا احتوت الوثيقة على أي جداول، **يجب** استخراجها كاملة وتنسيقها حصرياً بصيغة Markdown (MD).
- يمنع منعاً باتاً تجاهل أي بيانات مجدولة أو دمجها كنص عادي.
- أرجع JSON صحيح فقط — بدون markdown، بدون شرح، بدون مفاتيح إضافية.\
"""


def run_inference(img1: Image.Image, img2: Image.Image, file_id: str, page_start: int) -> str:
    gc.collect()
    torch.cuda.empty_cache()

    messages = [
        {"role": "system", "content": SYSTEM_PROMPT},
        {"role": "user", "content": [
            {"type": "image", "image": img1},
            {"type": "image", "image": img2},
            {"type": "text",  "text": (
                f"استخرج جميع البيانات من الصفحتين "
                f"من عقد {file_id} وأرجعها كـ JSON كامل."
            )},
        ]},
    ]

    input_text      = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
    image_inputs, _ = process_vision_info(messages)

    inputs = tokenizer(
        image_inputs,
        input_text,
        add_special_tokens=False,
        return_tensors="pt",
    ).to("cuda")

    with torch.no_grad():
        out = model.generate(
            **inputs,
            max_new_tokens=4096,
            use_cache=True,
            do_sample=False,
            repetition_penalty=1.1,
        )

    generated = tokenizer.decode(
        out[0, inputs["input_ids"].shape[-1]:],
        skip_special_tokens=True,
    ).strip()

    return generated

raw_output = run_inference(img1, img2, FILE_ID, PAGE_START)

print("\n=== Raw Output ===")
print(raw_output)

Available Formats

This repository contains:

Full Precision

FP16 merged Transformers model

GGUF

F16
Q8_0
Q4_K_M

These formats allow inference using:

Transformers
Unsloth
llama.cpp
LM Studio
Ollama (after creating a Modelfile)

Intended Use

The model is intended for:

Company incorporation contract digitization
Legal document information extraction
Document automation
Arabic document understanding
OCR-assisted document processing

Limitations

This model was fine-tuned specifically for Egyptian company incorporation contracts.

Performance on other document types such as:

invoices
passports
handwritten forms
receipts
newspapers

has not been evaluated.

Acknowledgements

This project is built upon:

Qwen3-VL
Unsloth
Hugging Face Transformers
PEFT

Special thanks to the authors of these open-source projects.

Citation

If you use this model in your research or application, please cite this repository.

a-mo-yehia/Qwen3-VL-8B-Contracts-OCR