| Model Type | |
| Use Cases |
| Areas: | | Information synthesis, Intelligent agents, Natural language understanding, SQL generation |
|
| Applications: | | Data synthesis, Information extraction, SQL generation, Structured data output |
|
| Primary Use Cases: | | Enterprise internal data processing, Building intelligent agents for business scenarios |
|
| Limitations: | | Model is not intended for direct public services, Unforeseen issues may arise due to model complexity |
|
| Considerations: | | Strongly recommend using within controlled environments with additional security measures. |
|
|
| Additional Notes | | Primarily designed for enterprise internal use rather than public environments. |
|
| Supported Languages | | zh (Advanced), en (Advanced) |
|
| Training Details |
| Data Sources: | | bigcode/the-stack, mc4, pleisto/wikipedia-cn-20230720-filtered, gsm8k, OpenAssistant/oasst1, b-mc2/sql-create-context, niv0, BAAI/COIG, wenhu/TheoremQA, zjunlp/KnowLM-IE |
|
| Methodology: | | Continuously trained based on Llama 2 with a focus on information synthesis and data-centric approaches. |
|
| Context Length: | |
| Training Time: | |
| Model Architecture: | | Extended Llama architecture with 13 billion parameters. |
|
|
| Responsible Ai Considerations |
| Mitigation Strategies: | | Using additional security measures such as input/output filtering, reviewing, or restricting is advised. |
|
|
| Input Output |
| Input Format: | | Prompts primarily in structured data or natural language queries for text generation models. |
|
| Accepted Modalities: | |
| Output Format: | | SQL statements, structured data responses, standard text completion formats. |
|
| Performance Tips: | | Use in controlled environments with pre-validated input formats for optimal performance. |
|
|
| Release Notes |
| Version: | |
| Date: | |
| Notes: | | Initial release with training and performance optimizations. |
|
|
|