GoldenNet-Qwen2.5-0.5B-QLoRA-v1

GoldenNet-Qwen2.5-0.5B-QLoRA-v1

<div align="center"> <img src="https://img.shields.io/badge/Golden%20Net%20AI-Iraqi%20Gov%20NLP-gold?style=for-the-badge" alt="Golden Net AI"/> <img src="https://img.shields.io/badge/Language-Arabic-green?style=for-the-badge" alt="Arabic"/> <img src="https://img.shields.io/badge/Base-Qwen2.5--0.5B-blue?style=for-the-badge" alt="Qwen2.5"/> </div>

Model Description

GoldenNet-Qwen2.5-0.5B-QLoRA-v1 is a fine-tuned version of Qwen/Qwen2.5-0.5B-Instruct specialized for Iraqi Government Correspondence Processing.

This model performs two key tasks:

1. Document Classification - Classifies government correspondence into 8 categories
2. Named Entity Recognition - Extracts entities like persons, organizations, locations, dates, monetary values, and legal references

Supported Categories (التصنيفات)

Training Details

Configuration

Training Results

Loss Progression

• Epoch 1: 0.912
• Epoch 2: 0.319
• Epoch 3: 0.200

Usage

With Transformers (Python)

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "Alamori/GoldenNet-Qwen2.5-0.5B-QLoRA-v1",
    device_map="auto",
    torch_dtype="auto"
)
tokenizer = AutoTokenizer.from_pretrained(
    "Alamori/GoldenNet-Qwen2.5-0.5B-QLoRA-v1"
)

# Example: Classification
correspondence = """جمهورية العراق
وزارة التربية
مديرية تربية بغداد
العدد: 4521/ت/2025
التاريخ: 2025/05/15

إلى/ السيد مدير عام التعليم العام المحترم

م/ طلب تعيين معلمين

تحية طيبة...

نرجو الموافقة على تعيين 50 معلماً في المدارس الابتدائية.

مع التقدير
مدير التربية"""

instruction = "صنّف المراسلة الحكومية التالية إلى إحدى الفئات: طلب، شكوى، تقرير، إعلام، استفسار، دعوة، تعميم، إحالة. أجب بصيغة JSON تتضمن الفئة ودرجة الثقة والتبرير."

messages = [
    {"role": "user", "content": f"{instruction}\n\n{correspondence}"}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.1)
response = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
print(response)
# Output: {"category": "طلب", "confidence": 0.96, "reasoning": "المراسلة تطلب تعيين موظفين..."}

With Ollama

# Create the model
ollama create goldennet-iraqi-gov -f Modelfile

# Run inference
ollama run goldennet-iraqi-gov

With vLLM

vllm serve Alamori/GoldenNet-Qwen2.5-0.5B-QLoRA-v1 --port 8000

Example Outputs

Classification Task

Input:

صنّف المراسلة الحكومية التالية...
[تعميم من مجلس الوزراء بشأن الدوام الرسمي]

Output:

{"category": "تعميم", "confidence": 0.97, "reasoning": "المراسلة تعميم قانوني يتضمن توجيهات إلزامية"}

Entity Extraction Task

Input:

استخرج جميع الكيانات المسماة من المراسلة الحكومية التالية...
[تقرير صحي من دائرة صحة البصرة]

Output:

{
  "persons": ["السيد وزير الصحة", "د. سعاد الموسوي"],
  "organizations": ["وزارة الصحة", "دائرة صحة البصرة"],
  "locations": ["محافظة البصرة", "الزبير", "الفاو"],
  "dates": ["2025/06/10"],
  "reference_numbers": ["7823/ص/2025"],
  "monetary_values": ["5 مليار دينار"],
  "quantities": ["3 مراكز صحية", "120 طبيباً"],
  "projects": [],
  "laws_regulations": []
}

Limitations

• Optimized specifically for Iraqi government correspondence format
• Best performance on formal Arabic administrative documents
• May require adaptation for other Arabic dialects or document types
• Recommended max input length: 2048 tokens

Intended Use

• Government document processing and automation
• Administrative workflow optimization
• Document routing and prioritization
• Metadata extraction from official correspondence
• Research on Arabic NLP for government applications

Ethical Considerations

This model is designed for legitimate government administrative purposes. Users should:

• Ensure compliance with data privacy regulations
• Use appropriate access controls for sensitive documents
• Validate model outputs before making critical decisions
• Not use for surveillance or unauthorized data collection

Citation

@misc{goldennet-qwen-qlora-v1,
  author = {Golden Net AI},
  title = {GoldenNet-Qwen2.5-0.5B-QLoRA-v1: Iraqi Government Correspondence Classifier},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/Alamori/GoldenNet-Qwen2.5-0.5B-QLoRA-v1}
}

About Golden Net AI

Golden Net AI is dedicated to developing AI solutions for Arabic language processing, with a focus on government and enterprise applications in Iraq and the MENA region.

License

This model is released under the Apache 2.0 License.

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<div align="center"> <b>Developed by Golden Net AI</b><br> <i>Empowering Iraqi Government Digital Transformation</i> </div>