| Model Type |  | 
| Use Cases | 
| Areas: | | commercial applications, research | 
 |  | Applications: | | chatbots, text generation, sensitivity analysis, multilingual assistance | 
 |  | Primary Use Cases: | | assistant-like chat, natural language generation tasks | 
 |  | Limitations: | | Use in languages beyond those explicitly referenced as supported is out of scope without additional fine-tuning. | 
 |  | Considerations: | | Developers may fine-tune models for unsupported languages while ensuring safe and responsible use. | 
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| Additional Notes | | Llama 3.1 models are not designed to be deployed in isolation and require additional safety guardrails when integrated into AI systems. | 
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| Supported Languages | | English (high), German (high), French (high), Italian (high), Portuguese (high), Hindi (high), Spanish (high), Thai (high) | 
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| Training Details | 
| Data Sources: | | publicly available online data | 
 |  | Data Volume: |  |  | Context Length: |  |  | Model Architecture: | | Llama 3.1 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. | 
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| Safety Evaluation | 
| Methodologies: | | fine-tuning, adversarial testing, red teaming, multi-faceted data collection | 
 |  | Findings: | | Model refusals to benign prompts as well as refusal tone have been an area of focus., Adversarial prompts and comprehensive safety data responses have been incorporated. | 
 |  | Risk Categories: | | CBRNE helpfulness, Child Safety, Cyber attack enablement | 
 |  | Ethical Considerations: | | Llama 3.1 addresses users and their needs without imposing unnecessary judgment or normativity, focusing on the values of free thought and expression. | 
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| Responsible Ai Considerations | 
| Fairness: | | The model is designed to be accessible to people across different backgrounds and experiences. | 
 |  | Transparency: | | Includes transparency tools for safety and content evaluations. | 
 |  | Accountability: | | Llama models should be part of an overall AI system with additional safety guardrails deployed by developers. | 
 |  | Mitigation Strategies: | | Strategies include a three-pronged approach to managing trust & safety risks, developer guidance, and community engagement. | 
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| Input Output | 
| Input Format: | | ChatML prompt template or Alpaca prompt template | 
 |  | Accepted Modalities: |  |  | Output Format: |  |  | Performance Tips: | | Use specific prompt templates for better performance. | 
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| Release Notes | | 
| Version: |  |  | Date: |  |  | Notes: | | Introduces new capabilities including longer context window and multilingual inputs. | 
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