---
datasets:
- SpursgoZmy/MMTab
- liuhaotian/LLaVA-Instruct-150K
- liuhaotian/LLaVA-Pretrain
language:
- en
metrics:
- accuracy
- bleu
- f1
pipeline_tag: image-text-to-text
---
# Table LLaVA Model Card

<!-- Provide a quick summary of what the model is/does. -->

Table LLaVA 7B is an open-source multimodal chatbot for understanding different table images and fulfilling diverse table-related requests, e.g., question answering, table cell description and structure understanding. 

See the ACL 2024 paper for more details: [Multimodal Table Understanding](https://arxiv.org/abs/2406.08100)

## Model Details

<!-- Provide a longer summary of what this model is. -->

**Model Type:** Table LLaVA strictly follows the [LLaVA-v1.5](https://arxiv.org/abs/2310.03744) model architecture and training pipeline, 
with [CLIP-ViT-L-336px](https://huggingface.co/openai/clip-vit-large-patch14-336) as visual encoder (336*336 image resolution), 
[Vicuna-v1.5-7B](https://huggingface.co/lmsys/vicuna-7b-v1.5) as base LLM and a two-layer MLP as vision-language connector. 

It was trained with a two-stage pipeline as LLaVA:

1. Pre-training: train the vision-language connector with image-caption data and table recognition data.
2. Instruction tuning: train the vision-language connector and the base LLM with multimodal instruction following data of tabular and non-tabular tasks.

**Code Base:** We use the official code of [LLaVA-v1.5](https://github.com/haotian-liu/LLaVA) for model training and inference, 
and the saved model checkpoint is uploaded to this repository. 

**Model Date:** Table-LLaVA 7B was trained in January 2024.

**Where to send questions or comments about the model:** https://github.com/SpursGoZmy/Table-LLaVA/issues

## Training dataset

The training data includes original LLaVA-1.5 data and specially constructed 
multimodal instruction-following data from the [MMTab dataset](https://huggingface.co/datasets/SpursgoZmy/MMTab), 
which is a large-scale dataset covering a wide range of table images and table-related tasks.

| Training Stage | Data Description | Data Size | Hugging Face Dataset |
| :---: | :---: | :---: | :---: | 
| Pre-training | 558K original LLaVA-1.5 pre-training data | 558K | [blip_laion_cc_sbu_558k.json](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain) |
|              | 150K table recognition data | 150K | [MMTab-pre_pretrain_data_llava_format_150K.json](https://huggingface.co/datasets/SpursgoZmy/MMTab) |
| Instruction Fine-tuning | 665K original LLaVA-1.5 fine-tuning data | 665K | [llava_v1_5_mix665k.json](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K) |
|              | 232K multimodal instruction tuning data of 14 tabular tasks | 232K | [MMTab-instruct_sft_data_llava_format_232K.json](https://huggingface.co/datasets/SpursgoZmy/MMTab) |

We also provide the merged pre-training and instruction fine-tuning data in the MMTab dataset, 
i.e., enhanced_llava_pretrain_data_708K.json and enhanced_llava_sft_data_898K.json.

## Evaluation dataset

A collection of 17 held-in and 7 held-out tabular benchmarks, including 15 table-related tasks, e.g., table question answering and table2text generation. 
We also evaluate Table LLaVA on two non-tabular benchmarks: 
[TextVQA](https://textvqa.org/) and [llava-bench-in-the-wild](https://huggingface.co/datasets/liuhaotian/llava-bench-in-the-wild).

## License

Table LLaVA is based on LLaVA-1.5 and thus follows its license. Llama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved.

## Intended use

**Primary intended uses:** The primary use of Table LLaVA is research on large multimodal models and chatbots, especially for multimodal table understanding.

**Primary intended users:** The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.

## Limitations

Though the proposed Table-LLaVA demonstrates
great performance on a wide range of table-based
tasks, the resolution of input images (336*336) is relatively
low and may limit the upper bound of its capacity. Luckily, with the emergence of MLLMs which
possess higher input image resolution (e.g., Monkey (Li et al., 2023d), LLaVA-Next (Liu et al.,
2024)), we can use MMTab to develop more powerful tabular MLLM in the future research.