| Model Type | | text generation, instruction-tuned | 
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| Use Cases | 
| Areas: | | Commercial applications, Research | 
 |  | Applications: | | Assistant-like chat, Natural language generation tasks | 
 |  | Primary Use Cases: | | Dialogue, Instruction following | 
 |  | Limitations: | | Limited to English applications, Potential outputs: inaccurate, biased, objectionable | 
 |  | Considerations: | | Safety testing and tuning required before deployment | 
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| Supported Languages |  | 
| Training Details | 
| Data Sources: | | publicly available online data, SlimPajama dataset, UltraChat dataset | 
 |  | Data Volume: | | 15 trillion tokens (pretraining), 10M human-annotated examples (fine-tuning) | 
 |  | Methodology: | | Supervised fine-tuning, Reinforcement learning with human feedback | 
 |  | Context Length: |  |  | Hardware Used: |  |  | Model Architecture: | | Auto-regressive transformer with optimized architecture | 
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| Safety Evaluation | 
| Methodologies: | | Red teaming, Adversarial evaluations | 
 |  | Risk Categories: | | CBRNE, Cybersecurity, Child Safety | 
 |  | Ethical Considerations: | | Assess responses related to adversarial risks and CBRNE threats | 
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| Responsible Ai Considerations | 
| Fairness: | | Fairness considerations are interwoven with model alignment strategies. | 
 |  | Transparency: | | Models openly released for safety evaluation and transparency. | 
 |  | Accountability: | | Model developers are accountable for ensuring alignment and safety. | 
 |  | Mitigation Strategies: | | Meta Llama Guard and Code Shield as safeguards. | 
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| Input Output | 
| Input Format: |  |  | Accepted Modalities: |  |  | Output Format: |  |  | Performance Tips: | | Apply supervised fine-tuning or RLHF for specific applications. | 
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