| Model Type | |
| Use Cases |
| Areas: | |
| Primary Use Cases: | | Developing language models for low-resource languages |
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| Limitations: | | Not suitable for human-facing interactions, Not intended for deployment, Limited to Brazilian Portuguese |
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| Considerations: | | Users should conduct risk and bias assessment before any real-world application |
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| Additional Notes | | Pre-trained model released under Apache 2.0; comprehensive evaluations available. |
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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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| Input Output |
| Input Format: | | Tokenizer input as text for generation |
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| Accepted Modalities: | |
| Output Format: | |
| Performance Tips: | | Review repetition penalty settings to avoid verbosity and repetition |
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