raalr/qwen2.5-1.5b-arabic-sft-3epoch
The raalr/qwen2.5-1.5b-arabic-sft-3epoch is a 1.5 billion parameter language model, fine-tuned for Arabic language tasks. This model is based on the Qwen2.5 architecture and has undergone 3 epochs of supervised fine-tuning (SFT). It is designed for general Arabic natural language processing applications, leveraging its compact size for efficient deployment.
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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.