Model Overview

The raalr/qwen2.5-1.5b-arabic-sft-3epoch is a 1.5 billion parameter language model built upon the Qwen2.5 architecture. This model has been specifically fine-tuned for Arabic language understanding and generation, undergoing 3 epochs of supervised fine-tuning (SFT).

Key Characteristics

• Architecture: Qwen2.5 base model.
• Parameter Count: 1.5 billion parameters, offering a balance between performance and computational efficiency.
• Language Focus: Primarily designed and fine-tuned for Arabic language tasks.
• Training: Supervised fine-tuning (SFT) over 3 epochs to enhance its capabilities in Arabic contexts.
• Context Length: Supports a context length of 32768 tokens.

Intended Use Cases

This model is suitable for a variety of Arabic NLP applications where a smaller, efficient model is preferred. Potential use cases include:

• Arabic text generation.
• Arabic language understanding tasks.
• Integration into applications requiring Arabic language processing with moderate resource requirements.