| Model Type | | text generation, multilingual |
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| Use Cases |
| Areas: | |
| Applications: | | Assistant-like chat, Natural language generation, Synthetic data generation |
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| Primary Use Cases: | | Multilingual dialogue, Instruction tuning |
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| Limitations: | | Usage in unsupported languages, Under circumstances violating applicable laws |
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| Considerations: | | Appropriate safeguards and responsible usage. |
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| Additional Notes | | Encouraged for research and commercial applications with responsibility and adherence to guidelines. |
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| Supported Languages | | English (Supported), German (Supported), French (Supported), Italian (Supported), Portuguese (Supported), Hindi (Supported), Spanish (Supported), Thai (Supported) |
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| Training Details |
| Data Sources: | | publicly available sources, synthetically generated examples |
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| Data Volume: | |
| Methodology: | | Auto-regressive transformer with supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) |
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| Context Length: | |
| Training Time: | |
| Hardware Used: | |
| Model Architecture: | | Auto-regressive transformer |
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| Safety Evaluation |
| Methodologies: | | Safety fine-tuning, Adversarial testing, Red-teaming |
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| Risk Categories: | | CBRNE helpfulness, Child Safety, Cyber attack enablement |
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| Ethical Considerations: | | Ensuring safe and responsible use. |
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| Responsible Ai Considerations |
| Fairness: | | Emphasis on non-discrimination and safe engagement across languages. |
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| Transparency: | | Information on methodology and safety evaluations are shared. |
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| Accountability: | | Meta holds responsibility with guidelines for safe integration. |
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| Mitigation Strategies: | | Safety guardrails and community guidelines. |
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| Input Output |
| Accepted Modalities: | |
| Output Format: | | Multilingual Text and code |
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| Performance Tips: | | Ensure use of safeguards and compliance with guidelines. |
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| Release Notes |
| Version: | |
| Date: | |
| Notes: | | Release of multilingual large models with improved tuning for safety and performance. |
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