README.md

---
license: apache-2.0
pipeline_tag: visual-question-answering
---
# 😈Imp


The Imp project aims to provide a family of  a strong multimodal `small` language models (MSLMs). Our `imp-v1-3b` is a strong MSLM with only **3B** parameters, which is build upon a small yet powerful SLM [Phi-2 ](https://huggingface.co/microsoft/phi-2)(2.7B) and a powerful visual encoder [SigLIP ](https://huggingface.co/google/siglip-so400m-patch14-384)(0.4B), and trained on the [LLaVA-v1.5](https://github.com/haotian-liu/LLaVA) training set.  

As shown in the Table below, `imp-v1-3b` significantly outperforms the counterparts of similar model sizes, and even achieves slightly better performance than the strong LLaVA-7B model on various multimodal benchmarks. 

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

You can use the following code for model inference. We minimize the required dependency libraries that only the `transformers` and `torch` packages are used. The format of text instructions is similar to [LLaVA](https://github.com/haotian-liu/LLaVA).

```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-v1-3b", 
    torch_dtype=torch.float16, 
    device_map="auto",
    trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("MILVLG/imp-v1-3b", trust_remote_code=True)

#Set inputs
text = "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWhat are the colors of the bus in the image? 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 |VizWiz  | SQA (IMG) | TextVQA | POPE |  MME | MMB  |MM-Vet|
|:--------:|:-----:|:----:|:----:|:-------------:|:--------:|:-----:|:----:|:-------:|:-------:|:-------:|
| [LLaVA-v1.5-lora](https://huggingface.co/liuhaotian/llava-v1.5-7b) | 7B |79.10 | **63.00** |47.80 |  68.40 |58.20| 86.40 | **1476.9** | 66.10  |30.2|
| [TinyGPT-V](https://huggingface.co/Tyrannosaurus/TinyGPT-V) | 3B | - | 33.60  | 24.80  |    -   |    -  | -| - | -  |-|
| [LLaVA-Phi](https://github.com/zhuyiche/llava-phi) | 3B | 71.40  | - | 35.90 |    68.40   |    48.60  | 85.00 | 1335.1 | 59.80 |28.9|
| [MobileVLM](https://huggingface.co/mtgv/MobileVLM-3B) | 3B | - | 59.00  | - |    61.00   |    47.50   | 84.90 | 1288.9 | 59.60  |-|
| [MC-LLaVA-3b](https://huggingface.co/visheratin/MC-LLaVA-3b) | 3B | 64.24 | 49.60  | 24.88 |    -   |    38.59   | 80.59 | - | -  |-|
| **Imp-v1 (ours)** | 3B | **79.45**  | 58.55 | **50.09** |**69.96**| **59.38** | **88.02**| 1434.0 | **66.49**  |**33.1**|

### Examples

![example1](images/example1.png)

## 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.