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---
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license: apache-2.0
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pipeline_tag: text-generation
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datasets:
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- liuhaotian/LLaVA-Pretrain
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- liuhaotian/LLaVA-Instruct-150K
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---
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# 😈 Imp
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\[[Paper](https://arxiv.org/abs/2405.12107)\] [[Demo](https://xmbot.net/imp/)\] [[Github](https://github.com/MILVLG/imp)\]
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## Introduction
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The Imp project aims to provide a family of highly capable yet lightweight LMMs. Our `Imp-v1.5-4B-Phi3` is a strong lightweight LMMs with only **4B** parameters, which is build upon [Phi-3 ](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct)(3.8B) and a powerful visual encoder [SigLIP ](https://huggingface.co/google/siglip-so400m-patch14-384)(0.4B), and trained on 1M mixed dataset.
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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 :)
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## How to use
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**Install dependencies**
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```bash
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pip install transformers # latest version is ok, but we recommend v4.36.0
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pip install -q pillow accelerate einops
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```
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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.
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```Python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from PIL import Image
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torch.set_default_device("cuda")
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#Create model
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model = AutoModelForCausalLM.from_pretrained(
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"MILVLG/Imp-v1.5-4B-Phi3",
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torch_dtype=torch.float16,
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device_map="auto",
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trust_remote_code=True)
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tokenizer = AutoTokenizer.from_pretrained("MILVLG/Imp-v1.5-4B-Phi3", trust_remote_code=True)
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#Set inputs
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text = "<|user|>\n<image>\nWhat are the colors of the bus in the image?\n<|end|>\n<|assistant|>\n"
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image = Image.open("images/bus.jpg")
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input_ids = tokenizer(text, return_tensors='pt').input_ids
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image_tensor = model.image_preprocess(image)
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#Generate the answer
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output_ids = model.generate(
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input_ids,
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max_new_tokens=100,
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images=image_tensor,
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use_cache=True)[0]
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print(tokenizer.decode(output_ids[input_ids.shape[1]:], skip_special_tokens=True).strip())
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```
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## Model evaluation
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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.
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| Models | Size | VQAv2 | GQA | SQA(IMG) | TextVQA | POPE | MME(P) | MMB |MMB_CN|MM-Vet|
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|:--------:|:-----:|:----:|:-------------:|:--------:|:-----:|:----:|:-------:|:-------:|:-------:|:-------:|
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| Bunny-v1.0-4B| 4B | **81.5** |**63.5** | 75.1|- | 86.7| 1495.2 |**73.5** |-|-|
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| **Imp-v1.5-4B-Phi3**| 4B | **81.5** | **63.5** | **78.0**|60.2 | **86.9**| **1507.7** |73.3 |61.1|44.6|
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## License
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This project is licensed under the Apache License 2.0 - see the [LICENSE](https://www.apache.org/licenses/LICENSE-2.0) file for details.
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## Citation
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If you use our model or refer our work in your studies, please cite:
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```bibtex
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@article{imp2024,
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title={Imp: Highly Capable Large Multimodal Models for Mobile Devices},
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author={Shao, Zhenwei and Yu, Zhou and Yu, Jun and Ouyang, Xuecheng and Zheng, Lihao and Gai, Zhenbiao and Wang, Mingyang and Ding, Jiajun},
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journal={arXiv preprint arXiv:2405.12107},
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year={2024}
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}
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``` |
