| Model Type | | text-to-text, decoder-only, large language model | 
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| Use Cases | 
| Areas: | | Content Creation, Communication, Research, Education | 
 |  | Applications: | | Text Generation, Chatbots, Text Summarization | 
 |  | Primary Use Cases: | | Customer service, Virtual assistants, Interactive applications, NLP research | 
 |  | Limitations: | | Bias in training data, Complex open-ended tasks, Language nuances | 
 |  | Considerations: | | Continuous monitoring and content safety mechanisms encouraged | 
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| Additional Notes | | Offers open model with lightweight architecture for various text generation tasks. | 
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| Supported Languages |  | 
| Training Details | 
| Data Sources: | | Web Documents, Code, Mathematics | 
 |  | Data Volume: |  |  | Methodology: | | Uses CSAM filtering and sensitive data filtering during preprocessing | 
 |  | Hardware Used: |  |  | Model Architecture: | | Open weights for both pre-trained and instruction-tuned variants | 
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| Safety Evaluation | 
| Methodologies: | | Internal red-teaming testing, Structured evaluations | 
 |  | Findings: | | Within acceptable thresholds for safety standards | 
 |  | Risk Categories: | | Text-to-Text Content Safety, Text-to-Text Representational Harms, Memorization | 
 |  | Ethical Considerations: | | Addressed content safety, representational harms, memorization, and large-scale harms | 
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| Responsible Ai Considerations | 
| Fairness: | | Considered socio-cultural biases, evaluation, and pre-processing | 
 |  | Transparency: | | Model card provides details on architecture and evaluation processes | 
 |  | Accountability: | | Google and responsible AI toolkit recommendations | 
 |  | Mitigation Strategies: | | Automation and manual evaluation for filtering and safety guidelines | 
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| Input Output | 
| Input Format: |  |  | Accepted Modalities: |  |  | Output Format: | | Generated English-language text | 
 |  | Performance Tips: | | Use CUDA for optimal performance | 
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