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license: apache-2.0 |
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pipeline_tag: visual-question-answering |
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# 😈Imp |
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\[Technical report (coming soon)\] [[Demo](https://xmbot,net/imp/)\] [[Github](https://github.com/MILVLG/imp)\] |
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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. |
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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. |
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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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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 latest version of `transformers` is ok but we recommand v4.3.0. The format of text instructions is similar to [LLaVA](https://github.com/haotian-liu/LLaVA). |
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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-v1-3b", |
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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-v1-3b", trust_remote_code=True) |
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#Set inputs |
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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:" |
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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 MSLMs of similar model sizes. |
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| Models | Size | VQAv2 | GQA |VizWiz | SQA (IMG) | TextVQA | POPE | MME | MMB |MM-Vet| |
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|:--------:|:-----:|:----:|:----:|:-------------:|:--------:|:-----:|:----:|:-------:|:-------:|:-------:| |
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| [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| |
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| [TinyGPT-V](https://huggingface.co/Tyrannosaurus/TinyGPT-V) | 3B | - | 33.60 | 24.80 | - | - | -| - | - |-| |
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| [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| |
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| [MobileVLM](https://huggingface.co/mtgv/MobileVLM-3B) | 3B | - | 59.00 | - | 61.00 | 47.50 | 84.90 | 1288.9 | 59.60 |-| |
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| [MC-LLaVA-3b](https://huggingface.co/visheratin/MC-LLaVA-3b) | 3B | 64.24 | 49.60 | 24.88 | - | 38.59 | 80.59 | - | - |-| |
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| **Imp-v1 (ours)** | 3B | **79.45** | 58.55 | **50.09** |**69.96**| **59.38** | **88.02**| 1434.0 | **66.49** |**33.1**| |
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### Examples |
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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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## About us |
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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. |
