Model Type | Language Model, Text Generation |
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Use Cases |
Areas: | Research, Commercial applications |
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Applications: | Chat assistants, Sentiment analysis, Document summarization |
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Primary Use Cases: | Arabic NLP research, Chat generation |
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Limitations: | Limited to Arabic and English., Cannot be used for harmful content generation. |
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Considerations: | Improved cultural understanding for Arabic. Not suited for other languages. |
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Additional Notes | Particularly efficient at processing Arabic language contexts, aiming to cater to Arabic-speaking audiences specifically. |
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Supported Languages | Arabic (High), English (High) |
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Training Details |
Data Sources: | Web, Code, Books, Scientific articles, Synthetic translations |
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Data Volume: | |
Methodology: | Scratch pre-training and adaptation from Llama-2. Enhanced training with the SwiGLU activation function and ALiBi position encoding. |
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Context Length: | |
Hardware Used: | 64 Cerebras CS-2 Wafer-Scale Engines |
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Model Architecture: | Transformer-based, decoder-only architecture with SwiGLU for Jais-family and RoPE embedding for adapted models. |
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Responsible Ai Considerations |
Fairness: | Techniques implemented to reduce bias are not specified in detail. |
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Transparency: | Basic preprocessing and role of language-specific techniques mentioned. |
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Accountability: | Users are responsible for applications. |
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Mitigation Strategies: | |
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Input Output |
Input Format: | |
Accepted Modalities: | |
Output Format: | |
Performance Tips: | Ensure the appropriate prompt design for task adaptation. |
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