Model Type | |
Use Cases |
Areas: | |
Applications: | Instruction tuned models for assistant-like chat |
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Primary Use Cases: | Natural language generation, Multilingual dialogue interactions |
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Limitations: | Out-of-the-box use only in English, Potential inaccurate or biased responses |
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Considerations: | Developers should fine-tune based on specific needs. |
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Additional Notes | 100% carbon emissions offset by Metaβs sustainability program. |
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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: | H100-80GB GPU with a cumulative 7.7M GPU hours |
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Model Architecture: | Auto-regressive transformer architecture |
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Safety Evaluation |
Methodologies: | Red teaming exercises, Adversarial evaluations |
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Risk Categories: | CBRNE, Cyber Security, Child Safety |
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Ethical Considerations: | Leverages best practices for safety and responsible deployment. |
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Responsible Ai Considerations |
Fairness: | Inclusive and open approach, aiming to serve diverse user needs and perspectives. |
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Accountability: | Developers responsible for end-user safety evaluations. |
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Mitigation Strategies: | Tools like Meta Llama Guard 2 and Code Shield for layering safety measures. |
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Input Output |
Input Format: | |
Accepted Modalities: | |
Output Format: | |
Performance Tips: | Fine-tune with language-specific data where appropriate. |
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Release Notes |
Version: | |
Date: | |
Notes: | Initial release of pre-trained and instruction tuned variants. |
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