- 🔥 [05.12] Online demo and API available on Replicate!
- 🔥 [05.05] We released code and dataset for instruction tuning.
- 🔥 [05.05] Online demo on HuggingFace is available. Thank Huggingface for providing us with free computing resources!
- 🔥 [05.05] Online demo on HuggingFace now supports recieve video! Demo on ModelScope will support soon.
- 🔥 [05.05] We upload our visually-related evaluation set OwlEval.
- [04.26] We provide an online demo on modelscope for the public to experience.
- [04.26] We released code of mPLUG-Owl🦉 with its pre-trained and instruction tuning checkpoints.
- A new training paradigm with a modularized design for large multi-modal language models.
- Learns visual knowledge while support multi-turn conversation consisting of different modalities (images/videos/texts).
- Observed abilities such as multi-image correlation and scene text understanding, vision-based document comprehension.
- Release a visually-related instruction evaluation set OwlEval.
- Our outstanding works on modularization:
- E2E-VLP, mPLUG and mPLUG-2, were respectively accepted by ACL 2021, EMNLP 2022 and ICML 2023.
- mPLUG is the first to achieve the human parity on VQA Challenge.
- comming soon
- Publish on Huggingface Hub
- Multi-lingustic support (e.g., Chinese, Japanese, Germen, French, etc.)
- Instruction tuning on interleaved data (multiple images and videos).
- Huggingface space demo.
- Instruction tuning code and pre-training code.
- A visually-related evaluation set OwlEval to comprehensively evaluate various models.
| Model | Phase | Download link |
|---|---|---|
| mPLUG-Owl 7B | Pre-training | Download link |
| mPLUG-Owl 7B | Instruction tuning | Download link |
| Tokenizer model | N/A | Download link |
The evaluation dataset OwlEval can be found in ./OwlEval.
- Create conda environment
conda create -n mplug_owl python=3.10
conda activate mplug_owl- Install PyTorch
conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia
-
Install apex (remove apex dependency in the next release)
Apex needs to be manually compiled from source code, because mPLUG-Owl rely on its cpp extension (MixedFusedLayerNorm).
Considering that the code in the apex repository changes frequently, we have included a fixed copy of the apex in our repository, which can be installed using the following command:
cd apex_22.01_pp
TORCH_CUDA_ARCH_LIST='5.2 6.0 6.1 7.0 7.5 8.0 8.6' pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./- Install other dependencies
pip install -r requirements.txtWe provide a script to deploy a simple demo in your local machine.
python -m server_mplug.owl_demo --debug --port 6363 --checkpoint_path 'your checkpoint path' --tokenizer_path 'your tokenizer path'Build model, toknizer and processor.
from interface import get_model
model, tokenizer, img_processor = get_model(
checkpoint_path='checkpoint path', tokenizer_path='tokenizer path')Prepare model inputs.
# We use a human/AI template to organize the context as a multi-turn conversation.
# <image> denotes an image placehold.
prompts = [
'''The following is a conversation between a curious human and AI assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
Human: <image>
Human: Explain why this meme is funny.
AI: ''']
# The image paths should be placed in the image_list and kept in the same order as in the prompts.
# We support urls, local file paths and base64 string. You can custom the pre-process of images by modifying the mplug_owl.modeling_mplug_owl.ImageProcessor
image_list = ['https://xxx.com/image.jpg',]For multiple images inputs, as it is an emergent ability of the models, we do not know which format is the best. Below is an example format we have tried in our experiments. Exploring formats that can help models better understand multiple images could be beneficial and worth further investigation.
prompts = [
'''The following is a conversation between a curious human and AI assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
Human: <image>
Human: <image>
Human: Do the shirts worn by the individuals in the first and second pictures vary in color? If so, what is the specific color of each shirt?
AI: ''']
image_list = ['https://xxx.com/image_1.jpg', 'https://xxx.com/image_2.jpg']Get response.
# generate kwargs (the same in transformers) can be passed in the do_generate()
from interface import do_generate
sentence = do_generate(prompts, image_list, model, tokenizer,
img_processor, max_length=512, top_k=5, do_sample=True)The training samples are stored in xxx.jsonl and orgnized in the following format:
{"text": "The following is a conversation between a curious human and AI assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.\nHuman: Construct a sentence using the given verb in the past tense\nshudder\nAI: She shuddered at the thought of being alone in the dark.", "task_type": "gpt4instruct_sft"}
{"image": ["xxx.jpg"], "text": "The following is a conversation between a curious human and AI assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.\nHuman: <image>\nHuman: Where is the man located in the image?\nAI: The man is located in a small restaurant, sitting and enjoying his meal.\nHuman: What type of food is the man eating in the image?\nAI: The man is eating a burrito in the image.\nHuman: What can be seen next to the man in the restaurant?\nAI: Next to the man in the restaurant, there is a fridge filled with cans of soda.\nHuman: How does the man feel as he is about to eat his meal?\nAI: The man looks happy and is smiling as he is about to take a bite of his meal.\nHuman: What unique aspect of the photo makes the scene appear distorted or unusual?\nAI: The unique aspect of this photo is that it was taken using a fish-eye lens, which creates a distorted, wide-angle view of the scene, making it appear different from a standard perspective.", "task_type": "llava_sft"}The task_type can be in one of {'quora_chat_sft', 'sharegpt_chat_sft', 'llava_sft', 'gpt4instruct_sft'}.
Prepare your own train.jsonl and dev.jsonl and modify data_files in configs/instruction_tuning/v0.yaml.
Put downloaded pretrained.pth in the root folder.
Execute the training script.
bash train_it.sh
You can also now run the model and demo locally with cog, an open source ML tool maintained by Replicate. To get started, follow the instructions in this cog fork of mPLUG-Owl.
The comparison results of 50 single-turn responses (left) and 52 multi-turn responses (right) between mPLUG-Owl and baselines with manual evaluation metrics. A/B/C/D denote the rate of each response.
- LLaMA. A open-source collection of state-of-the-art large pre-trained language models.
- Baize. An open-source chat model trained with LoRA on 100k dialogs generated by letting ChatGPT chat with itself.
- Alpaca. A fine-tuned model trained from a 7B LLaMA model on 52K instruction-following data.
- LoRA. A plug-and-play module that can greatly reduce the number of trainable parameters for downstream tasks.
- MiniGPT-4. A multi-modal language model that aligns a frozen visual encoder with a frozen LLM using just one projection layer.
- LLaVA. A visual instruction tuned vision language model which achieves GPT4 level capabilities.
- mPLUG. A vision-language foundation model for both cross-modal understanding and generation.
- mPLUG-2. A multimodal model with a modular design, which inspired our project.
If you found this work useful, consider giving this repository a star and citing our paper as followed:
@misc{ye2023mplugowl,
title={mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality},
author={Qinghao Ye and Haiyang Xu and Guohai Xu and Jiabo Ye and Ming Yan and Yiyang Zhou and Junyang Wang and Anwen Hu and Pengcheng Shi and Yaya Shi and Chaoya Jiang and Chenliang Li and Yuanhong Xu and Hehong Chen and Junfeng Tian and Qian Qi and Ji Zhang and Fei Huang},
year={2023},
eprint={2304.14178},
archivePrefix={arXiv},
primaryClass={cs.CL}
}