Alamori/GoldenNet-Qwen2.5-0.5B-Full-v1
Alamori/GoldenNet-Qwen2.5-0.5B-Full-v1 is a 0.5 billion parameter Qwen2.5-based model, fully fine-tuned by Golden Net AI for specialized Arabic natural language processing. It excels at document classification across 8 categories and named entity recognition for Iraqi Government Correspondence Processing. This model offers the highest task-specific performance among its variants, leveraging its 32768 token context length for detailed analysis.
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GoldenNet-Qwen2.5-0.5B-Full-v1 Overview
This model is a fully fine-tuned version of Qwen/Qwen2.5-0.5B-Instruct, developed by Golden Net AI. It is specifically optimized for Iraqi Government Correspondence Processing in Arabic, with all 494 million parameters trained for maximum task-specific performance.
Key Capabilities
- Document Classification: Categorizes government correspondence into 8 distinct types: طلب (request), شكوى (complaint), تقرير (report), إعلام (notification), استفسار (inquiry), دعوة (invitation), تعميم (circular), and إحالة (referral).
- Named Entity Recognition (NER): Extracts critical entities such as persons, organizations, locations, dates, monetary values, and laws from Arabic text.
Training and Performance
The Full-v1 variant underwent full fine-tuning with a learning rate of 5e-5 over 3 epochs, achieving the lowest training loss (0.171) among its related models. While its evaluation loss (0.3636) is slightly higher than the QLoRA-v1 variant, it represents the most comprehensive fine-tuning approach. The model was trained on an NVIDIA RTX 5070 with BF16 precision and a max sequence length of 1024.
When to Use This Model
- Optimal Performance: Choose Full-v1 when the highest possible task-specific accuracy is required for Iraqi government correspondence processing and computational resources (storage/memory) are not a limiting factor.
- Specialized Arabic NLP: Ideal for applications demanding precise classification and entity extraction from formal Arabic government documents.