Model Type | Text Generation, Financial Analysis |
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Use Cases |
Areas: | Finance, General text processing |
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Applications: | Financial analysis, General AI chatbot applications |
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Primary Use Cases: | Answering financial questions, Engaging in general conversations |
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Limitations: | May not perform well beyond financial and general use scopes. |
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Considerations: | General chatbot ethics and data privacy should be considered. |
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Additional Notes | The XuanYuan model series is specifically optimized for financial texts while maintaining a broad applicability to general language processing tasks. |
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Supported Languages | Chinese (High), English (High) |
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Training Details |
Data Sources: | General web sources, Financial news, Company announcements, Financial books, Certification exam questions |
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Methodology: | Incremental pre-training with both general and financial-specific data. Enhanced vocabulary to maintain English proficiency and improve Chinese understanding. |
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Context Length: | |
Hardware Used: | 100x GPU Cluster with 8x A800 (80G) per node |
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Model Architecture: | Enhanced version of Llama2 architecture, optimized for distributed training and extended context handling. |
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Input Output |
Input Format: | Usual NLP input formats, token-based input for extended context processing. |
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Accepted Modalities: | |
Output Format: | |
Performance Tips: | For optimal performance, ensure adequate GPU memory and adhere to training methodologies when further tuning. |
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