Model Type | text generation, causal-lm |
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Use Cases |
Areas: | |
Applications: | Development of chat assistants, Sentiment analysis, Summarization |
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Primary Use Cases: | Natural language understanding and generation tasks |
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Limitations: | Limited to Arabic and English, Not suitable for high-stakes decisions |
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Considerations: | Not to rely solely on model's outputs. |
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Additional Notes | The strategies for pre-training, fine-tuning and adaptation to Arabic are extensible to other low and medium resource languages. |
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Supported Languages | Arabic (High proficiency), English (Strong proficiency) |
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Training Details |
Data Sources: | Web, Code, Books, Scientific, Synthetic |
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Data Volume: | |
Methodology: | SwiGLU non-linear activation, ALiBi position encoding |
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Context Length: | |
Hardware Used: | 64 Cerebras CS-2 Wafer-Scale Engines (WSE-2) |
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Model Architecture: | Transformer-based, decoder-only architecture |
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Safety Evaluation |
Methodologies: | Various dimensions including knowledge, reasoning, misinformation/bias |
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Risk Categories: | |
Ethical Considerations: | Understanding limitations and potential misuse. |
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Responsible Ai Considerations |
Mitigation Strategies: | Various techniques to reduce bias. |
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Input Output |
Input Format: | |
Accepted Modalities: | |
Output Format: | |
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Release Notes |
Version: | |
Notes: | Release of the Jais family with improved cultural understanding for Arabic. |
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