| Model Type | | bilingual, text generation, NLP |
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| Use Cases |
| Areas: | |
| Applications: | | Natural language understanding and generation, Chat applications, Sentiment analysis, Summarization |
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| Primary Use Cases: | | Research by Arabic NLP practitioners, Chat assistants for Arabic-speaking users |
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| Limitations: | | Non-generalization to all languages, High-stakes decisions |
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| Considerations: | | Should not be used to generate harmful content or handle sensitive information |
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| Additional Notes | | The models are focused on Arabic NLP and have been fine-tuned for dialog and instruction following. The models are designed to be powerful for Arabic and English but are not intended for high-stakes decision making. |
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| Supported Languages | | Arabic (MSA), English (Strong proficiency) |
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| Training Details |
| Data Sources: | | Web, Code, Books, Scientific, Synthetic |
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| Data Volume: | |
| Methodology: | | Instruction fine-tuning, adaptive pre-training |
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| Context Length: | |
| Hardware Used: | | Condor Galaxy supercomputer platform, Cerebras CS-2 Wafer-Scale Engines |
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| Model Architecture: | | Auto-regressive, transformer-based, decoder-only |
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| Input Output |
| Input Format: | |
| Accepted Modalities: | |
| Output Format: | |
| Performance Tips: | | Use `trust_remote_code=True` during implementation |
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