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README.md

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 | **[Paper](https://arxiv.org/abs/2403.19318)** | **[Training set](https://huggingface.co/datasets/RUCKBReasoning/TableLLM-SFT)** | **[Github](https://github.com/RUCKBReasoning/TableLLM)** | **[Homepage](https://tablellm.github.io/)** |
 
-We present **TableLLM**, a powerful large language model designed to handle tabular data manipulation tasks efficiently, whether they are embedded in spreadsheets or documents, meeting the demands of real office scenarios. The TableLLM series encompasses two distinct scales: [TableLLM-7B](https://huggingface.co/RUCKBReasoning/TableLLM-7b) and [TableLLM-13B](https://huggingface.co/RUCKBReasoning/TableLLM-13b), which are fine-tuned based on [Llama3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct).
+We present **TableLLM**, a powerful large language model designed to handle tabular data manipulation tasks efficiently, whether they are embedded in spreadsheets or documents, meeting the demands of real office scenarios. TableLLM is fine-tuned based on [Llama3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct).
 
 TableLLM generates either a code solution or a direct text answer to handle tabular data manipulation tasks based on different scenarios. Code generation is used for handling spreadsheet-embedded tabular data, which often involves the insert, delete, update, query, merge, and plot operations of tables. Text generation is used for handling document-embedded tabular data, which often involves the query operation of short tables.