😈 Imp

[Technical report]  [Demo]  [Huggingface]

This repository contains the official training/evaluation code of the Imp project, which 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 (2.7B) and a powerful visual encoder SigLIP (0.4B), and trained on the LLaVA-v1.5 training set.

As shown in the Evaluation, 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 also release the model weights a running example of Imp-v1-3B on Huggingface. Technical report will be released soon. We will persistently improve our model and release the next versions to further improve model performance :)

Updates

• May 21, 2024: The technical report and corresponding Imp-v1.5-2B/3B/4B model series are released.
• February 9, 2024: Training and evaluation codes of the Imp-v1-3B model are released.

Table of Contents

• Prerequisites
• Model Zoo
• Training
• Evaluation
• Deployment
• License
• Citation

Prerequisites

1. Clone this repository and navigate to the folder

git clone https://github.com/MILVLG/imp.git
cd imp

2. Install Package

We recommend using Anaconda to create a new environment for the project, and install the requirements with the following commands:

conda create -n imp python=3.10 -y
conda activate imp
pip install -r requirements.txt
pip install flash-attn==2.4.2 --no-build-isolation

3. Download the pretrained base models (i.e., Phi-2 and SigLIP) to your local directories. Note that the latest version of the Phi-2 model is not compatible with this repository. We strongly recommend using the following script to download the specific versions of the base models.

python scripts/download_models.py

The base models will be stored in checkpoints/base in default.

checkpoints
└── base
    └── siglip-so400m-patch14-384
    └── phi-2

Model-zoo

The checkpoints of different Imp models are provided in Model_Zoo.md .

Training

The training pipeline and datasets of imp-v1-3b are directly inherited from LLaVA-v1.5. The training

• Multimodal pretraining: train a projector on a subset of ∼558K image-text pairs to connect a frozen pretrained vision encoder and a frozen LLM.
• Multimodal instruction tuning: fine-tune the projector and LoRA in the LLM with multimodal instruction data and VQA-formatted data to empower the MLSM the ability of multimodal instruction following.

Imp is trained on 8 A100 (40G) GPUs. You can reduce the per_device_train_batch_size and increase the gradient_accumulation_steps to match your resources. .But always keep the global batch size the same: global_batch_size = per_device_train_batch_size $\times$ gradient_accumulation_steps $\times$ num_gpus.

Training scripts

Stage-1: Multimodal pretraining

Please download the caption annotations blip_laion_cc_sbu_558k.json and images from here. Move the downloaded files to the ./datasets folder, with image folder unzipped and renamed to pretrain_images. Then run the following command to start the training process:

bash scripts/pretrain.sh

After that, a checkpoint file will be stored in ./checkpoints/imp-v1-3b-stage1.

Stage-2: Multimodal instruction tuning

Please download the annotation file of the mixed instruction tuning data llava_v1_5_mix665k.json, and download the images from constituting datasets:

• COCO: train2017
• GQA: images
• OCR-VQA: download script, save all files as .jpg
• TextVQA: train_val_images
• VisualGenome: part1, part2

After downloading all of them, organize the data as follows:

datasets
├── llava_v1_5_mix665k.json
└── finetune_images
    ├── coco
    │   └── train2017
    ├── gqa
    │   └── images
    ├── ocr_vqa
    │   └── images
    ├── textvqa
    │   └── train_images
    └── vg
        ├── VG_100K
        └── VG_100K_2

Then, you can start the training process by the following script. If you use your custom dataset, you can refer to llava_v1_5_mix665k.json to format your data.

bash scripts/finetune_lora.sh
# bash scripts/finetune.sh # fully finetuning is not recommended

You will get a trained model imp-v1-3b-stage2-lora (a LoRA diff if you use finetune_lora.sh) under ./checkpoints/ when the training is done.

Submodel merging

After the above model training, the model checkpoint consists of multiple sub-models. You can use the following script to merge the stage2 sub-models into a single one for release. Our evaluation script supports both the sub-models and merged model checkpoints. However, if you want to fine-tune the model on your own custom dataset, only the merged model is supported.

bash scripts/merge.sh

After that, a checkpoint file will be stored in ./checkpoints/imp-v1-3b.

Finetuning on custom datasets

You also can finetune Imp using your own custom dataset use finetune_lora_custom.sh. The custom dataset should be in the LLaVA-1.5 format.

bash scripts/finetune_lora_custom.sh

Evaluation

We follow the evaluation of LLaVA-v1.5 and conduct experiments on 9 commonly-used benchmarks, including 5 academic VQA benchmarks and 4 popular MLLM benchmarks. All evaluation scripts are placed in the scripts/eval folder.

Before preparing task-specific data, you should download eval.zip and unzip it to ./playground/data/eval. For more specific instructions, please refer to LLaVA's Evaluation.md.

It is supported to evaluate your reproduced model checkpoints or our released model. For more detailed evaluation scripts, please refer to Evaluation.md.

Using our provided model, you can reproduce the following results. Our imp-v1-3b model significantly outperforms existing MSLMs of similar model sizes, and is comparable with the strong LLaVA-v1.5-7b model.

Models	VQAv2	GQA	VizWiz	SQA(IMG)	TextVQA	POPE	MME(P)	MMB	MM-Vet
LLaVA-v1.5-lora (7B)	79.10	63.00	47.80	68.40	58.20	86.40	1476.9	66.10	30.2
TinyGPT-V (3B)	-	33.60	24.80	-	-	-	-	-	-
LLaVA-Phi (3B)	71.40	-	35.90	68.40	48.60	85.00	1335.1	59.80	28.9
MobileVLM (3B)	-	59.00	-	61.00	47.50	84.90	1288.9	59.60	-
MC-LLaVA (3B)	64.24	49.60	24.88	-	38.59	80.59	-	-	-
Imp-v1 (3B, ours)	79.45	58.55	50.09	69.96	59.38	88.02	1434.0	66.49	33.1

Deployment

Based on MLC-LLM, we provide a lightweight deployment solution so that imp can inference efficiently on the mobile device.

• After 4-bit quantification, imp only takes up about 1.9G of storage space and is fully capable of running on mobile phones.
• All Android devices are supported and IOS will come soon.
• Textual and visual modalities are supported.

More details can be found in MILVLG/mlc-imp.

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

About us

This project is maintained by the 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:

@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}
}