---
license: mit
---

<p align="center" width="100%">
<img src="https://i.postimg.cc/MKmyP9wH/new-banner.png"  width="80%" height="80%">
</p>


<div>
<div align="center">
    <a href='https://brianboli.com/' target='_blank'>Bo Li*<sup>1</sup></a>&emsp;
    <a href='https://zhangyuanhan-ai.github.io/' target='_blank'>Yuanhan Zhang*<sup>,1</sup></a>&emsp;
    <a href='https://cliangyu.com/' target='_blank'>Liangyu Chen*<sup>,1</sup></a>&emsp;
    <a href='https://king159.github.io/' target='_blank'>Jinghao Wang*<sup>,1</sup></a>&emsp;
    <a href='https://pufanyi.github.io/' target='_blank'>Fanyi Pu*<sup>,1</sup></a>&emsp;
    </br>
    <a href='https://jingkang50.github.io/' target='_blank'>Jingkang Yang<sup>1</sup></a>&emsp;
    <a href='https://chunyuan.li/' target='_blank'>Chunyuan Li<sup>2</sup></a>&emsp;
    <a href='https://liuziwei7.github.io/' target='_blank'>Ziwei Liu<sup>1</sup></a>
</div>
<div>
<div align="center">
    <sup>1</sup>S-Lab, Nanyang Technological University&emsp;
    <sup>2</sup>Microsoft Research, Redmond
</div>

This weight is for **initilizing training for Otter-MPT7B**. It's directly converted from [Openflamingov2](https://huggingface.co/openflamingo/OpenFlamingo-9B-vitl-mpt7b), we added a `<answer>` token for Otter's downstream instruction tuning.

You can load and try this model using
```python
model = OtterForConditionalGeneration.from_pretrained("luodian/OTTER-MPT7B-Init", device_map="sequential")
model.text_tokenizer.padding_side = "left"
tokenizer = model.text_tokenizer
image_processor = transformers.CLIPImageProcessor()
model.eval()
```

Leave us a message if you have any error or question. You can follow [Otter code](https://github.com/Luodian/Otter) (see training section) to further tune your model on top of it.