---
title: TableInstruct
canonical_url: "https://www.modelscope.cn/datasets/osunlp/TableInstruct"
md_url: "https://www.modelscope.cn/datasets/osunlp/TableInstruct.md"
repository: osunlp/TableInstruct
last_updated: 2025-07-04
license: cc-by-4.0
storage_size: "35 GB"
downloads: 379
stars: 0
---

# TableInstruct

> TableInstruct - osunlp 在 ModelScope 开源的数据集。--- TableLlama: Towards Open Large Generalist Models for Tables

osunlp/TableInstruct 是 ModelScope 魔搭社区上的数据集，存储大小 35 GB，采用 cc-by-4.0 许可。

- **Repository**: osunlp/TableInstruct
- **License**: cc-by-4.0
- **Storage size**: 35 GB
- **Downloads**: 379
- **Stars**: 0
- **Last updated**: 2025-07-04

Source: https://www.modelscope.cn/datasets/osunlp/TableInstruct

---

---
# TableLlama: Towards Open Large Generalist Models for Tables

Project Page: [https://osu-nlp-group.github.io/TableLlama/](https://osu-nlp-group.github.io/TableLlama/)

Paper: [https://arxiv.org/abs/2311.09206](https://arxiv.org/abs/2311.09206)

Model: [https://huggingface.co/osunlp/TableLlama/](https://huggingface.co/osunlp/TableLlama/)

Code: [https://osu-nlp-group.github.io/TableLlama/](https://osu-nlp-group.github.io/TableLlama/)


## Introduction
We introduce TableLlama, an open-source large generalist model specifically tailored for various table-based tasks. The TableLlama model is trained on TableInstruct Dataset, a meticulously curated instruction tuning dataset for tables. TableLlama is tuned on 2.6 million table-based task data, and can handle up to 8K context!


## Model 

🤗 [TableLlama-7B](https://huggingface.co/osunlp/TableLlama/)


## Data
The models are trained on the 🤗 [TableInstruct Dataset](https://huggingface.co/datasets/osunlp/TableInstruct), which includes a comprehensive table-based instruction tuning dataset that covers a variety of real-world tables and realistic tasks. We include 14 datasets of 11 tasks in total. Check out the dataset card for more details.


## Training Procedure
The models are fine-tuned with the TableInstruct dataset using LongLoRA (7B), fully fine-tuning version as the base model, which replaces the vanilla attention mechanism of the original Llama-2 (7B) with shift short attention. The training takes 9 days on a 48*A100 cluster. Check out our paper for more details.

## Evaluation
The models are evaluated on 8 in-domain datasets of 8 tasks and 6 out-of-domain datasets of 4 tasks. 


## Usage
You can use the models through Huggingface's Transformers library. 
Check our Github repo for more advanced use: [https://osu-nlp-group.github.io/TableLlama/](https://osu-nlp-group.github.io/TableLlama/)

## Prompt Format
```
Below is an instruction that describes a task, paired with an input that provides further context. Write a response that
appropriately completes the request.

### Instruction:
{instruction}

### Input:
{input}

### Question:
{question}

### Response:
```

## Citation
If you use the models, data, or code from this project, please cite the original paper:

```
@misc{zhang2023tablellama,
  title={TableLlama: Towards Open Large Generalist Models for Tables}, 
  author={Tianshu Zhang and Xiang Yue and Yifei Li and Huan Sun},
  year={2023},
  eprint={2311.09206},
  archivePrefix={arXiv},
  primaryClass={cs.CL}
}
```