| 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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| Data Volume: | |
| 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 |
| Risk Categories: | |
| 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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| Accepted Modalities: | |
| Output Format: | |
| Performance Tips: | | Considerations for handling large input/output safely. |
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| Release Notes |
| Version: | |
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| Notes: | | Base model trained from scratch with bilingual data. |
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| Version: | |
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| Notes: | | Chat-based version through fine-tuning. |
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| Version: | |
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| Notes: | | Improved language abilities pre-trained on LLAMA-2. |
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| Version: | |
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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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