| Model Type | |
| Use Cases |
| Areas: | |
| Applications: | | assistant-like chat, natural language generation tasks |
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| Primary Use Cases: | | Instruction tuned models for dialogue, Pretrained models for various text tasks |
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| Limitations: | | Use violating laws or policies, Use in non-English languages without compliance |
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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: | |
| Hardware Used: | | Meta's Research SuperCluster, H100-80GB GPU |
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| Model Architecture: | | auto-regressive language model with an optimized transformer architecture |
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| Safety Evaluation |
| Methodologies: | | red teaming, adversarial evaluations |
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| Findings: | | reduced residual risks with safety tools, improved refusal rates to benign prompts |
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| Risk Categories: | | misinformation, child safety, cybersecurity |
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| Ethical Considerations: | | Emphasis on responsible deployment and safety best practices |
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| Responsible Ai Considerations |
| Accountability: | |
| Mitigation Strategies: | | Provides tools like Meta Llama Guard 2 and Code Shield for developers |
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| Input Output |
| Input Format: | |
| Accepted Modalities: | |
| Output Format: | |
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| Release Notes |
| Version: | |
| Date: | |
| Notes: | | Initial release of Llama 3 family models |
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