| Model Type | | text generation, dialogue optimization |
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| Use Cases |
| Areas: | | research, commercial applications |
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| Applications: | | assistant-like chat, natural language generation |
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| Primary Use Cases: | | Instruction tuned for chat-oriented tasks, Pretrained model adaptation |
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| Limitations: | |
| Considerations: | | Follow Responsible Use Guide and implement safety tools. |
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| Additional Notes | | Developed with openness, inclusivity, and helpfulness in mind. |
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| Supported Languages | |
| Training Details |
| Data Sources: | | publicly available online data |
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| Data Volume: | |
| Methodology: | | Supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) |
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| Context Length: | |
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| Hardware Used: | |
| Model Architecture: | | Auto-regressive transformer |
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| Safety Evaluation |
| Methodologies: | | red-teaming, adversarial evaluations |
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| Findings: | | Improved safety measures than previous versions |
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| Risk Categories: | | misinformation, cybersecurity, child safety |
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| Ethical Considerations: | | Ensures transparency and responsibility in deployment. |
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| Responsible Ai Considerations |
| Fairness: | | Developers are encouraged to implement safety tools and assess risks. |
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| Transparency: | | Open source tooling and contribution encouraged. |
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| Accountability: | | Meta provides safety benchmarks and regular updates. |
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| Mitigation Strategies: | | Implement safeguards using Meta Llama Guard. |
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| Input Output |
| Input Format: | |
| Accepted Modalities: | |
| Output Format: | |
| Performance Tips: | | Use specified transformers libraries for enhanced performance. |
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| Release Notes |
| Version: | |
| Date: | |
| Notes: | | Introduced new variants with fine-tuning optimizations. |
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