| Model Type | | text generation, code generation |
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| Use Cases |
| Areas: | |
| Applications: | | Dialogue use cases, Natural language generation |
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| Primary Use Cases: | | Assistant-like chat, Adaptation for various tasks |
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| Limitations: | | Use in non-English languages bypassing the license, Violation of laws or use policies |
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| Considerations: | | Developers can fine-tune for other languages following the license and use policy. |
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| Additional Notes | | In developing these models, the goal was to optimize for helpfulness and safety. Emphasis on community contributions for improving the Llama technology. |
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| Supported Languages | |
| Training Details |
| Data Sources: | | Publicly available online data |
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| Data Volume: | |
| Methodology: | | Supervised fine-tuning and reinforcement learning with human feedback |
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| Hardware Used: | | Meta's Research SuperCluster, Third-party cloud compute |
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| Model Architecture: | | Auto-regressive language model with transformer architecture, tuned using SFT and RLHF. |
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| Safety Evaluation |
| Methodologies: | | Red-teaming, Adversarial evaluations |
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| Findings: | | Residual risks remain, mitigations implemented |
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| Risk Categories: | | Cybersecurity, Child Safety |
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| Ethical Considerations: | | Developed considering responsible AI practices, with guides and resources provided to the community. |
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| Responsible Ai Considerations |
| Mitigation Strategies: | | Llama Guard and Code Shield safeguards provided for safety mitigation. |
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| Input Output |
| Input Format: | |
| Accepted Modalities: | |
| Output Format: | |
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| Release Notes |
| Version: | |
| Notes: | | Meta released Llama 3 models optimized for helpfulness and safety in two sizes, 8B and 70B parameters. |
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