| Model Type | | transformers, language model, causal language modeling |
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| Use Cases |
| Areas: | | research, text generation |
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| Applications: | | text generation, language modeling |
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| Primary Use Cases: | | generating texts from prompts |
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| Limitations: | | Cannot distinguish fact from fiction, Potential bias in outputs |
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| Considerations: | | Ensure deployment readiness with an understanding of biases. |
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| Supported Languages | |
| Training Details |
| Data Sources: | | Reddit outbound links with 3+ karma |
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| Data Volume: | | Over 40 GB (WebText dataset) |
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| Methodology: | | Self-supervised training with causal language modeling |
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| Context Length: | |
| Hardware Used: | |
| Model Architecture: | | Transformers architecture with 50,257-token vocabulary |
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| Safety Evaluation |
| Ethical Considerations: | | Includes biases inherent to training data; caution advised for sensitive use-cases. |
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| Responsible Ai Considerations |
| Fairness: | | Model reflects biases present in training data; conduct studies on bias in intended use cases. |
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| Transparency: | | OpenAI released a model card highlighting limitations and ethical considerations. |
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| Accountability: | | Deployers are responsible for usage and bias evaluation. |
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| Mitigation Strategies: | | Approach deployment with caution in bias-sensitive applications; consider fine-tuning carefully. |
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| Input Output |
| Input Format: | | Continuous text sequences |
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| Accepted Modalities: | |
| Output Format: | |
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