| Model Type | |
| Use Cases |
| Areas: | |
| Primary Use Cases: | | Researching language models for low-resource languages |
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| Limitations: | | Not suitable for translation or generating text in other languages, Not fine-tuned for downstream contexts |
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| Considerations: | | Perform risk and bias assessment before use in real-world applications. |
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| Supported Languages | |
| Training Details |
| Data Sources: | | Pt-Corpus Instruct (6.2B tokens) |
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| Data Volume: | |
| Methodology: | | Transformer-based model pre-trained via causal language modeling |
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| Context Length: | |
| Training Time: | |
| Hardware Used: | |
| Model Architecture: | |
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| Responsible Ai Considerations |
| Fairness: | | The model may produce biased or toxic content due to inherited stereotypes from the training data. |
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| Input Output |
| Accepted Modalities: | |
| Performance Tips: | | Ensure proper configuration of repetition penalty and generation parameters like temperature, top-k, and top-p to optimize outputs. |
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