| Model Type | | Decoder, causal-lm, text-generation |
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| Use Cases |
| Areas: | |
| Applications: | | Chat assistants, Sentiment analysis, Summarization |
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| Primary Use Cases: | | Arabic NLP research, Development of chat assistants |
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| Limitations: | | Limited to Arabic and English, Potential for bias and misinformation |
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| Considerations: | | Proficiency assumed only for targeted languages. |
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| Additional Notes | | Suitable for Arabic and English NLP. Improved context handling at extended sequence lengths. |
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| Supported Languages | | Arabic (MSA), English (proficient) |
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| Training Details |
| Data Sources: | | Web, Code, Books, Scientific, Synthetic |
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| Data Volume: | | up to 1.6 Trillion tokens |
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| Methodology: | | Instruction fine-tuned, progressive context expansion |
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| Context Length: | |
| Hardware Used: | | Condor Galaxy supercomputer, 64 Cerebras CS-2 Wafer-Scale Engines (WSE-2), 960 PetaFLOP/s |
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| Model Architecture: | | Transformer-based, decoder-only (GPT-3) |
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| Responsible Ai Considerations |
| Fairness: | | Efforts made to reduce bias, but model may still exhibit bias. |
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| Transparency: | |
| Accountability: | | Users are responsible for generated content's use. |
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| Mitigation Strategies: | | Reduction of bias through techniques. |
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| Input Output |
| Input Format: | |
| Accepted Modalities: | |
| Output Format: | |
| Performance Tips: | | Enable `trust_remote_code=True` for model loading. |
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| Release Notes |
| Version: | |
| Notes: | | Includes new adaptations over Llama-2, enhanced Arabic capabilities. |
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