Model Type | |
Use Cases |
Areas: | research on large language models |
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Primary Use Cases: | question answering, natural language understanding, reading comprehension, evaluating and mitigating risks from language models |
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Limitations: | not trained with human feedback, prone to generate toxic or offensive content, incorrect information or unhelpful answers possible |
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Supported Languages | supported_languages_list (/), bg, ca, cs, da, de, en, es, fr, hr, hu, it, nl, pl, pt, ro, ru, sl, sr, sv, uk (/), proficiency_level (/) |
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Training Details |
Data Sources: | CCNet [67%], C4 [15%], GitHub [4.5%], Wikipedia [4.5%], Books [4.5%], ArXiv [2.5%], Stack Exchange[2%] |
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Data Volume: | |
Training Time: | |
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Safety Evaluation |
Ethical Considerations: | LLaMA is expected to exhibit biases from the training data, which might contain offensive, harmful, and biased content. |
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Responsible Ai Considerations |
Fairness: | Model performance may vary with the language and possibly for different dialects. Model reflects biases from its web-based training data. |
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Mitigation Strategies: | Data was filtered based on its proximity to Wikipedia text and references using the Kneser-Ney language model with a fastText linear classifier. |
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Input Output |
Input Format: | |
Accepted Modalities: | |
Output Format: | |
Performance Tips: | Use a GPU with sufficient VRAM for optimal performance. |
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