| Model Type | |
| Use Cases |
| Areas: | |
| Applications: | |
| Primary Use Cases: | | English text and code generation |
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| Limitations: | | Out-of-scope for languages other than English without compliance, Risk of misuse if violated acceptable use policy |
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| Considerations: | | Developers should tune model for safety based on specific applications. |
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| Additional Notes | | Focused on safe and inclusive text generation practices. Special measures for sensitive applications implemented. |
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| Supported Languages | |
| Training Details |
| Data Sources: | | publicly available online data |
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| Data Volume: | | 15T+ tokens pretraining, 10M human-annotated examples fine-tuning |
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| Methodology: | | 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 language model with transformer architecture |
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| Safety Evaluation |
| Methodologies: | | red teaming, adversarial evaluations |
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| Findings: | | Residual risks remain; improved model helpfulness and reduced false refusals compared to Llama 2. |
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| Risk Categories: | | child safety, cybersecurity |
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| Ethical Considerations: | | Residual risks highlighted |
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| Responsible Ai Considerations |
| Fairness: | | Emphasis on inclusivity and openness |
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| Transparency: | | Open source license and transparency on safety standards |
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| Accountability: | | Meta and developers accountable for use adhering to license terms |
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| Mitigation Strategies: | | Llama Guard 2 and Code Shield tools for safety |
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| Input Output |
| Input Format: | |
| Accepted Modalities: | |
| Output Format: | |
| Performance Tips: | | Use Reinforcement Learning with Human Feedback for optimal tuning. |
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| Release Notes |
| Version: | |
| Date: | |
| Notes: | | Initial release with enhanced helpfulness and safety measures. |
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