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

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

## Introduction

The Imp project aims to provide a family of highly capable yet lightweight LMMs. Our `Imp-v1.5-2B-Qwen1.5` is a strong lightweight LMM with only **2B** parameters, which is build upon [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). 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 lightweight LMMs 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 | -|
| **Imp-v1.5-2B-Qwen1.5** | 2B | **79.2** | **61.9** | **66.1**| **54.5** | **86.7**| 1304.8 | **63.8**| **61.3** |**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.

## 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 Zheng, Lihao and Gai, Zhenbiao and Wang, Mingyang and Ding, Jiajun},
  journal={arXiv preprint arXiv:2405.12107},
  year={2024}
}
```