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license: apache-2.0
pipeline_tag: image-text-to-text
library_name: transformers
paper: https://arxiv.org/abs/2409.03277
---
<div align='center'>
<h1>This is a reproduction of ChartMoE according to its official github repo, which has better performance on ChartQA(with/without PoT).</h1>
</div>
<p align="center">
<b><font size="6">ChartMoE</font></b>
<p>
<p align="center">
<b><font size="4">ICLR2025 Oral </font></b>
<p>
<div align='center'>
[Project Page](https://chartmoe.github.io/)
[Github Repo](https://github.com/IDEA-FinAI/ChartMoE)
[Paper](https://arxiv.org/abs/2409.03277)
</div>
**ChartMoE** is a multimodal large language model with Mixture-of-Expert connector, based on [InternLM-XComposer2](https://github.com/InternLM/InternLM-XComposer/tree/main/InternLM-XComposer-2.0) for advanced chart 1)understanding, 2)replot, 3)editing, 4)highlighting and 5)transformation.
## Import from Transformers
To load the ChartMoE model using Transformers, use the following code:
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
ckpt_path = "IDEA-FinAI/chartmoe"
tokenizer = AutoTokenizer.from_pretrained(ckpt_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(ckpt_path, trust_remote_code=True).half().cuda().eval()
```
## Quickstart & Gradio Demo
We provide a simple example and a gradio webui demo to show how to use ChartMoE. Please refer to [https://github.com/IDEA-FinAI/ChartMoE](https://github.com/IDEA-FinAI/ChartMoE).
## Open Source License
The code is licensed under Apache-2.0. |