| Model Type | | text-to-text, decoder-only, large language model |
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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 |
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| Primary Use Cases: | | Content Creation, Knowledge Exploration |
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| Limitations: | | Biases or gaps in training data can lead to limitations in model responses, Lack of common sense reasoning, May generate incorrect or outdated factual statements |
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| Considerations: | | Requires clear prompts and instructions for optimal task performance. |
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| Additional Notes | | The models are designed from the ground up for Responsible AI development compared to similarly sized models. |
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| Supported Languages | |
| Training Details |
| Data Sources: | | Web Documents, Code, Mathematics |
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| Data Volume: | |
| Context Length: | |
| Hardware Used: | |
| Model Architecture: | | text-to-text, decoder-only |
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| Safety Evaluation |
| Methodologies: | | Red-Teaming, Human Evaluation, Automated Evaluation |
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| Findings: | | Results within acceptable thresholds for categories such as child safety, content safety, representational harms, memorization, large-scale harms |
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| Risk Categories: | | Text-to-Text Content Safety, Representational Harms, Memorization, Large-scale harm |
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| Responsible Ai Considerations |
| Fairness: | | These models underwent careful scrutiny, input data pre-processing described and posterior evaluations reported. |
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| Transparency: | |
| Accountability: | | This model card summarizes details on the models' architecture, capabilities, limitations, and evaluation processes. |
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| Mitigation Strategies: | | Continuous monitoring and exploration of de-biasing techniques; guidelines for content safety, prohibited uses policy. |
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| Input Output |
| Input Format: | | Text string, such as a question, a prompt, or a document to be summarized. |
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| Accepted Modalities: | |
| Output Format: | | Generated English-language text in response to the input. |
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