

Tablegpt2 7B
Overview :
TableGPT2-7B is a large-scale decoder model developed by Zhejiang University, specifically designed for data-intensive tasks, particularly the interpretation and analysis of tabular data. Based on the Qwen2.5 architecture, it is optimized through Continuous Pretraining (CPT) and Supervised Fine-tuning (SFT) to handle complex table queries and business intelligence (BI) applications. It supports Chinese queries and is suitable for enterprises and research institutions that need to efficiently process structured data. The model is currently open-source and free; more professional versions may be released in the future.
Target Users :
TableGPT2-7B is designed for enterprises, data analysts, business intelligence specialists, and researchers who need to process large amounts of tabular data. It helps users quickly understand and analyze tabular data, generate code, and answer complex questions, ultimately improving efficiency and decision-making.
Use Cases
Analyze sales data tables using TableGPT2-7B to quickly generate sales trend reports.
Generate SQL query code using the model to extract specific data from a database.
Integrate TableGPT2-7B into a business intelligence platform to achieve automated data interpretation and visualization.
Features
Supports tabular data and text input, with optimized text output suitable for coding, data analysis, and BI Q&A.
Provides Continuous Pretraining (CPT) and Supervised Fine-tuning (SFT) to enhance understanding of complex queries and tables.
Supports various tabular data sources, including multimodal data and BI-specific examples, improving model generalization.
Provides quick-start code examples to facilitate user adoption and integration into existing workflows.
Supports model deployment via vLLM, providing a Chat API interface similar to OpenAI.
How to Use
1. Install necessary libraries, such as `transformers` and `pandas`.
2. Use pandas to read the tabular data and convert it into a format supported by the model.
3. Load the TableGPT2-7B model and its corresponding tokenizer.
4. Construct a prompt template containing the table information and user's question.
5. Use the model to generate answers or code, and perform further processing as needed.
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