Model Type | |
Use Cases |
Areas: | |
Applications: | |
Primary Use Cases: | Dialogue, Text generation |
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Limitations: | Use beyond English, prohibited by license |
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Considerations: | Developers can fine-tune for languages beyond English within policy constraints. |
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Additional Notes | Optimized for efficiency using 8-bit quantization by bitsandbytes. |
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Supported Languages | |
Training Details |
Data Sources: | Publicly available online data |
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Data Volume: | |
Methodology: | Pretrained and instruction tuned with supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) |
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Context Length: | |
Training Time: | |
Hardware Used: | |
Model Architecture: | Auto-regressive transformer model |
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Safety Evaluation |
Methodologies: | Red teaming, Adversarial evaluations |
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Findings: | Conducted extensive risk assessments and implemented mitigation techniques |
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Risk Categories: | Misinformation, Bias, Child Safety, Cybersecurity |
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Ethical Considerations: | Emphasis placed on responsible AI development. |
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Responsible Ai Considerations |
Fairness: | Aimed at inclusivity and helpfulness without unnecessary judgment. |
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Transparency: | Open approach to AI intended for a wide range of applications. |
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Accountability: | Developers are advised to incorporate additional safety tools. |
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Mitigation Strategies: | Purple Llama solutions, Llama Guard for safeguards. |
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Input Output |
Input Format: | |
Accepted Modalities: | |
Output Format: | |
Performance Tips: | Use proper prompt structuring for best results. |
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Release Notes |
Version: | |
Date: | |
Notes: | Initial release of 8-bit quantized version |
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