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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