| Model Type | | text-to-text, decoder-only, large language model |
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| Use Cases |
| Areas: | | Research, Commercial Applications |
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| Applications: | | Text Generation, Chatbots and Conversational AI, Text Summarization |
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| Primary Use Cases: | | Content Creation and Communication, Research and Education |
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| Limitations: | | Context and Task Complexity, Language Ambiguity, Factual Inaccuracies |
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| Considerations: | | Adhering to privacy regulations, continuous monitoring for bias, content safety mechanisms. |
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| Additional Notes | | Training used multilingual, diverse data including code and mathematics. |
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| Supported Languages | |
| Training Details |
| Data Sources: | | Web Documents, Code, Mathematics |
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| Data Volume: | | 2 trillion tokens for 2B model |
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| Methodology: | | Text-to-text, decoder-only |
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| Hardware Used: | |
| Model Architecture: | | Lightweight state-of-the-art open model |
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| Safety Evaluation |
| Methodologies: | | Red-teaming, Human evaluation, Automated evaluation |
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| Risk Categories: | | Misinformation, Bias, Dangerous capabilities, Memorization |
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| Ethical Considerations: | | Bias and fairness concerns, misinformation risks, transparency and accountability. |
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| Responsible Ai Considerations |
| Fairness: | | Models evaluated for socio-cultural biases. |
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| Transparency: | | Summary details on architecture, capabilities, and limitations provided. |
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| Accountability: | | Guidelines provided for responsible use. |
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| Mitigation Strategies: | | Evaluations and automated techniques to filter sensitive data from training sets. |
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| Input Output |
| Input Format: | |
| Accepted Modalities: | |
| Output Format: | | Generated English-language text |
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| Performance Tips: | | Better performance with clear prompts and instruction. |
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| Release Notes |
| Version: | |
| Notes: | | Lightweight open model, trained on 2 trillion tokens. |
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