| Model Type | | text-generation, instruction-tuned |
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| Use Cases |
| Areas: | | Research, Commercial applications |
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| Applications: | | Assistant-like chat, Natural language generation tasks |
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| Primary Use Cases: | | Instruction tuned models for dialogue, Pretrained models for various tasks |
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| Limitations: | | Use in languages other than English, Any manner violating laws or regulations |
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| Considerations: | | Fine-tuning allowed for languages beyond English under compliance |
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| Additional Notes | | Pretraining data cutoff March 2023 for 8B, December 2023 for 70B. |
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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), Reinforcement Learning with Human Feedback (RLHF) |
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| Context Length: | |
| Hardware Used: | | Meta's Research SuperCluster, H100-80GB GPUs |
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| Model Architecture: | | Auto-regressive language model, optimized transformer architecture |
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| Safety Evaluation |
| Methodologies: | | Red teaming, Adversarial evaluations |
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| Findings: | | Fewer false refusals compared to Llama 2 |
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| Risk Categories: | | CBRNE threats, Cyber Security risks, Child Safety risks |
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| Ethical Considerations: | | Residual risks remain, developers should assess them for specific use cases |
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| Responsible Ai Considerations |
| Transparency: | | Open approach to AI, encourage community engagement |
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| Mitigation Strategies: | | Updated Responsible Use Guide, use of Meta Llama Guard 2 and Code Shield |
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| Input Output |
| Input Format: | |
| Output Format: | |
| Performance Tips: | | Follow prompt template provided by Llama-3 |
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