Model Type | text-to-text, decoder-only, large language models |
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Use Cases |
Areas: | Content Creation and Communication, Research and Education |
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Applications: | Text Generation, Chatbots and Conversational AI, Text Summarization, Natural Language Processing Research, Language Learning Tools, Knowledge Exploration |
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Primary Use Cases: | Question answering, Summarization, Reasoning |
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Limitations: | Biases from training data, Complex task handling limitations, Figurative language and nuances issues, Factual inaccuracies |
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Considerations: | Continuous monitoring, content safety guidelines, and education around privacy. |
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Supported Languages | |
Training Details |
Data Sources: | Web Documents, Code, Mathematics |
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Data Volume: | 6 trillion tokens |
Context Length: | 8192 |
Hardware Used: | |
Model Architecture: | text-to-text, decoder-only large language model |
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Safety Evaluation |
Methodologies: | structured evaluations, internal red-teaming |
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Findings: | acceptable thresholds for internal policies |
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Risk Categories: | Text-to-Text Content Safety, Text-to-Text Representational Harms, Memorization, Large-scale harm |
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Ethical Considerations: | Bias and Fairness, Misinformation, Transparency |
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Responsible Ai Considerations |
Fairness: | The model underwent input data pre-processing and evaluations to assess socio-cultural biases. |
Transparency: | Summarizes details on architecture, capabilities, and limitations. |
Accountability: | Open model development aims to share innovation with developers and researchers. |
Mitigation Strategies: | De-biasing techniques, content safety mechanisms, and end-user education. |
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Input Output |
Input Format: | Text string |
Accepted Modalities: | |
Output Format: | Generated English-language text |
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