README.md

---

license: apache-2.0
pipeline_tag: text-generation
datasets:
- liuhaotian/LLaVA-Pretrain
- liuhaotian/LLaVA-Instruct-150K
---

# 😈 Imp

> A very small man can cast a very large shadow.
> 

>           ——*George R.R. Martin, A Clash of Kings*


\[[Paper](https://arxiv.org/abs/2405.12107)\]  [[Demo](https://xmbot.net/imp/)\]  [[Github](https://github.com/MILVLG/imp)\]

## Introduction

The Imp project aims to provide a family of  a strong multimodal `small` language models (MSLMs). Our `Imp-v1.5-2B-Qwen1.5` is a strong MSLM with only **2B** parameters, which is build upon a small yet powerful SLM [Qwen1.5-1.8B-Chat ](https://huggingface.co/Qwen/Qwen1.5-1.8B-Chat)(1.8B) and a powerful visual encoder [SigLIP ](https://huggingface.co/google/siglip-so400m-patch14-384)(0.4B), and trained on on 1M mixed dataset.   

As shown in the Table below, `Imp-v1.5-2B-Qwen1.5` significantly outperforms the counterparts of similar model sizes. 

We release our model weights and provide an example below to run our model . Detailed technical report and corresponding training/evaluation code will be released soon on our [GitHub repo](https://github.com/MILVLG/imp). We will persistently improve our model and release the next versions to further improve model performance :) 


## How to use


**Install dependencies**
```bash

pip install transformers # latest version is ok, but we recommend v4.36.0

pip install -q pillow accelerate einops

```

You can use the following code for model inference. The format of text instruction is similar to [LLaVA](https://github.com/haotian-liu/LLaVA). A Colab page to run this example is provided [here](https://colab.research.google.com/drive/1EBYky6xIPjnlPppo2gZaiNK6gEsjXgom?usp=drive_link#scrollTo=2-VpU6QzWCVZ). Note that the example can only be run on GPUs currently.

```Python

import torch

from transformers import AutoModelForCausalLM, AutoTokenizer

from PIL import Image



torch.set_default_device("cuda")



#Create model

model = AutoModelForCausalLM.from_pretrained(

    "MILVLG/Imp-v1.5-2B-Qwen1.5", 

    torch_dtype=torch.float16, 

    device_map="auto",

    trust_remote_code=True)

tokenizer = AutoTokenizer.from_pretrained("MILVLG/Imp-v1.5-2B-Qwen1.5", trust_remote_code=True)



#Set inputs

text = "<|im_start|>system\nA chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.<|im_end|>\n<|im_start|>user\n<image>\nWhat are the colors of the bus in the image?<|im_end|>\n<|im_start|>assistant"

image = Image.open("images/bus.jpg")



input_ids = tokenizer(text, return_tensors='pt').input_ids

image_tensor = model.image_preprocess(image)



#Generate the answer

output_ids = model.generate(

    input_ids,

    max_new_tokens=100,

    images=image_tensor,

    use_cache=True)[0]

print(tokenizer.decode(output_ids[input_ids.shape[1]:], skip_special_tokens=True).strip())

```

## Model evaluation
We conduct evaluation on 9 commonly-used benchmarks, including 5 academic VQA benchmarks and 4 popular MLLM benchmarks, to compare our Imp model with LLaVA (7B) and existing MSLMs of similar model sizes.

| Models | Size | VQAv2 | GQA | SQA(IMG) | TextVQA | POPE |  MME(P) | MMB  |MMBCN  |MM-Vet|
|:--------:|:-----:|:----:|:-------------:|:--------:|:-----:|:----:|:-------:|:-------:|:-------:|:-------:|
| [Mini-Gemini-2B](https://github.com/dvlab-research/MGM) | 2B |- | -|  56.2 |-| -|  1341 | 59.8 |- | 31.1|
| [Bunny-v1.0-2B-zh](https://huggingface.co/BAAI/Bunny-v1_0-2B-zh) | 2B |76.6 | 59.6|  64.6 |-| 85.8 |  1300.8 | 59.1 |58.5 | 31.1|
| **Imp-v1.5-2B-Qwen1.5** | 3B | 79.2 | 61.93 | 66.14| 54.52 | 86.74| 1304.8 | 63.83| 61.34 |33.5|

## License
This project is licensed under the Apache License 2.0 - see the [LICENSE](https://www.apache.org/licenses/LICENSE-2.0) file for details.

## About us
This project is maintained by the [MILVLG](https://github.com/MILVLG)@Hangzhou Dianzi University (HDU) led by Prof. Zhou Yu and Jun Yu, and is mainly developed by Zhenwei Shao and Xuecheng Ouyang. We hope our model may serve as a strong baseline to inspire future research on MSLM, as well as its derivative applications on mobile devices and robots. 

## Citation

If you use our model or refer our work in your studies, please cite:

```bibtex

@article{imp2024,

  title={Imp: Highly Capable Large Multimodal Models for Mobile Devices},

  author={Shao, Zhenwei and Yu, Zhou and Yu, Jun and Ouyang, Xuecheng and Lihao, Zheng and Zhenbiao, Gai and Mingyang, Wang and Jiajun, Ding},

  journal={arXiv preprint arXiv:2405.12107},

  year={2024}

}

```