---
license: apache-2.0
language:
- ar
- en
base_model: Qwen/Qwen2.5-0.5B-Instruct
tags:
- qwen2
- arabic
- iraqi
- government
- classification
- ner
- fine-tuned
- full-finetune
- goldennet
datasets:
- custom
pipeline_tag: text-generation
library_name: transformers
model-index:
- name: GoldenNet-Qwen2.5-0.5B-Full-v1
  results:
  - task:
      type: text-classification
      name: Document Classification
    metrics:
    - type: loss
      value: 0.3636
      name: Eval Loss
---

# GoldenNet-Qwen2.5-0.5B-Full-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/Method-Full%20Fine--tune-red?style=for-the-badge" alt="Full"/>
  <img src="https://img.shields.io/badge/Language-Arabic-green?style=for-the-badge" alt="Arabic"/>
</div>

## Model Description

**GoldenNet-Qwen2.5-0.5B-Full-v1** is a fully fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) specialized for **Iraqi Government Correspondence Processing**.

This is the **full fine-tuning** variant where all 494M parameters were trained, potentially offering the best task-specific performance.

### Tasks
1. **Document Classification** - 8 categories (طلب، شكوى، تقرير، إعلام، استفسار، دعوة، تعميم، إحالة)
2. **Named Entity Recognition** - Extracts persons, organizations, locations, dates, monetary values, laws

## Model Comparison

| Model | Method | Train Loss | Eval Loss | Training Time | Size |
|-------|--------|------------|-----------|---------------|------|
| [QLoRA-v1](https://huggingface.co/Alamori/GoldenNet-Qwen2.5-0.5B-QLoRA-v1) | 4-bit QLoRA | 0.448 | **0.2998** | 49s | 943MB |
| [LoRA-v1](https://huggingface.co/Alamori/GoldenNet-Qwen2.5-0.5B-LoRA-v1) | Standard LoRA | 0.496 | 0.3665 | 70s | 943MB |
| **Full-v1** | Full Fine-tune | **0.461** | 0.3636 | 121s | 1.9GB |

## Training Details

| Parameter | Value |
|-----------|-------|
| Base Model | Qwen/Qwen2.5-0.5B-Instruct |
| Fine-tuning Method | Full (all parameters) |
| Learning Rate | 5e-5 |
| Optimizer | AdamW 8-bit |
| Epochs | 3 |
| Batch Size | 1 (effective: 16) |
| Max Sequence Length | 1024 |
| Precision | BF16 |
| Trainable Parameters | 494M (100%) |
| Hardware | NVIDIA RTX 5070 (8GB VRAM) |

### Loss Progression
- Epoch 1: 0.983
- Epoch 2: 0.328
- Epoch 3: 0.171 (lowest among all variants!)

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

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

# Classification example
correspondence = """جمهورية العراق
وزارة التربية
العدد: 1234/ت/2025

إلى/ السيد مدير عام التعليم المحترم

م/ طلب تعيين معلمين

نرجو الموافقة على تعيين 50 معلماً.

مع التقدير"""

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

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)
print(tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True))
```

## When to Use This Model

- **Use Full-v1** when you need maximum task-specific performance and have sufficient storage/memory
- **Use QLoRA-v1** for best balance of quality and efficiency (recommended for most cases)
- **Use LoRA-v1** for comparison or when you need standard LoRA compatibility

## Related Models

- [GoldenNet-Qwen2.5-0.5B-QLoRA-v1](https://huggingface.co/Alamori/GoldenNet-Qwen2.5-0.5B-QLoRA-v1) - 4-bit quantized (best eval loss)
- [GoldenNet-Qwen2.5-0.5B-LoRA-v1](https://huggingface.co/Alamori/GoldenNet-Qwen2.5-0.5B-LoRA-v1) - Standard LoRA

## License

Apache 2.0

---

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