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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