Model Type | chat-based, large language model |
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Use Cases |
Areas: | academic, bilingual chatbot |
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Applications: | chatbots, language understanding |
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Primary Use Cases: | chat applications, bilingual assistance |
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Limitations: | may produce unexpected outputs, probabilistic generation can lead to biases or discrimination |
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Considerations: | Use with caution for generating ethical and aligned content. |
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Additional Notes | Bilingual focus improves model adaptability to both English and Chinese cultures. |
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Supported Languages | Chinese (high proficiency), English (high proficiency), Multilingual (moderate proficiency) |
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Training Details |
Data Sources: | over 1.6TB tokens of English, Chinese, multilingual data |
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Methodology: | Supervised fine-tuning via curriculum learning |
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Context Length: | |
Model Architecture: | Based on LLaMA and LLaMA-2 |
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Safety Evaluation |
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Ethical Considerations: | Please do not propagate harmful content generated by the model. |
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Responsible Ai Considerations |
Fairness: | Efforts made to reduce potential biases and discrimination. |
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Transparency: | Model weights and differences provided. |
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Accountability: | Users are responsible for avoiding dissemination of harmful content. |
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Mitigation Strategies: | Encouragement to generate ethical and legal text. |
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Input Output |
Input Format: | Supports up to 4096 context tokens. |
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Performance Tips: | Considerations for handling large input/output safely. |
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Release Notes |
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Notes: | Base model trained from scratch with bilingual data. |
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Notes: | Chat-based version through fine-tuning. |
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Notes: | Improved language abilities pre-trained on LLAMA-2. |
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Notes: | Includes advancements in vocabulary and processing. |
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Version: | |
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Notes: | Initial release of the chat model series. |
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