δΈζ | English
We introduce RLAIF-V, a novel framework that aligns MLLMs in a fully open-source paradigm for super GPT-4V trustworthiness. RLAIF-V maximally exploits the open-source feedback from two key perspectives, including high-quality feedback data and online feedback learning algorithm. Notable features of RLAIF-V include:
We present the RLAIF-V Dataset, which is an AI generated preference dataset covering diverse range of tasks and domains. This open-source multimodal preference datasets contains more than 30K high-quality comparison pairs.
Clone this repository and navigate to RLAIF-V folder
git clone https://github.com/RLHF-V/RLAIF-V.git
cd RLAIF-V
Install package
conda create -n rlaifv python=3.10 -y
conda activate rlaifv
pip install -e .
Install required spaCy model
wget https://github.com/explosion/spacy-models/releases/download/en_core_web_trf-3.7.3/en_core_web_trf-3.7.3.tar.gz
pip install en_core_web_trf-3.7.3.tar.gz
Model | Description | Download |
---|---|---|
RLAIF-V 7B | The most trustworthy variant on LLaVA 1.5 | π€ |
RLAIF-V 12B | Based on OmniLMM-12B, achieving super GPT-4V trustworthiness. | π€ |
We provide a simple example to show how to use RLAIF-V.
from chat import RLAIFVChat, img2base64
chat_model = RLAIFVChat('openBMB/RLAIF-V-7B') # or 'openBMB/RLAIF-V-12B'
image_path="./examples/test.jpeg"
msgs = "Describe in detail the people in the picture."
inputs = {"image": image_path, "question": msgs}
answer = chat_model.chat(inputs)
print(answer)
You can also run this example by executing the following script:
python chat.py
We provide the OmniLMM 12B model and the MiniCPM-Llama3-V 2.5 model for feedback generation. If you wish to use the MiniCPM-Llama3-V 2.5 for giving feedback, please configure its inference environment according to the instructions in the MiniCPM-V GitHub repository.
Please download our fine-tuned Llama3 8B models: split model and question transformation model, and store them in the ./models/llama3_split
folder and the ./models/llama3_changeq
folder respectively.
The following script demonstrates using the LLaVA-v1.5-7b model to generate candidate answers and the OmniLMM 12B model to provide feedback.
mkdir ./results
bash ./script/data_gen/run_data_pipeline_llava15_omni.sh
The following script demonstrates using the LLaVA-v1.5-7b model to generate candidate answers and the MiniCPM-Llama3-V 2.5 model to provide feedback. First, replace minicpmv_python
in ./script/data_gen/run_data_pipeline_llava15_minicpmv.sh
with the Python path of the MiniCPM-V environment you created.
mkdir ./results
bash ./script/data_gen/run_data_pipeline_llava15_minicpmv.sh
If you can access huggingface dataset, you can skip this step, we will automatically download the RLAIF-V Dataset.
If you already downloaded the dataset, you can replace 'openbmb/RLAIF-V-Dataset' to your dataset path here at Line 38.
Run the following command to start training.
bash ./script/train/llava15_train.sh
The evaluation of Object HalBench relies on the caption and segmentation annotations from the COCO2014 dataset. Please first download the COCO2014 dataset from the COCO dataset's official website.
mkdir coco2014
cd coco2014
wget http://images.cocodataset.org/annotations/annotations_trainval2014.zip
unzip annotations_trainval2014.zip
Please replace {YOUR_OPENAI_API_KEY}
with a valid OpenAI api-key.
# cd RLAIF-V
bash ./script/eval_rlaif_objhal.sh ./RLAIF-V_weight ./results/RLAIF-V ./coco2014/annotations {YOUR_OPENAI_API_KEY}
Please download the MMHal evaluation data here, and save the file in eval/data
.
# cd RLAIF-V
bash ./script/eval_rlaifv_mmhal.sh ./RLAIF-V_weight ./results/RLAIF-V {YOUR_OPENAI_API_KEY}
Usage and License Notices: The data, code, and checkpoint are intended and licensed for research use only. They are also restricted to uses that follow the license agreement of LLaMA, Vicuna, and Chat GPT. The dataset is CC BY NC 4.0 (allowing only non-commercial use) and models trained using the dataset should not be used outside of research purposes.
If you find our model/code/data/paper helpful, please consider cite our papers π and star us βοΈοΌ
@article{yu2023rlhf,
title={Rlhf-v: Towards trustworthy mllms via behavior alignment from fine-grained correctional human feedback},
author={Yu, Tianyu and Yao, Yuan and Zhang, Haoye and He, Taiwen and Han, Yifeng and Cui, Ganqu and Hu, Jinyi and Liu, Zhiyuan and Zheng, Hai-Tao and Sun, Maosong and others},
journal={arXiv preprint arXiv:2312.00849},
year={2023}
}
@article{yu2024rlaifv,
title={RLAIF-V: Aligning MLLMs through Open-Source AI Feedback for Super GPT-4V Trustworthiness},
author={Yu, Tianyu and Zhang, Haoye and Yao, Yuan and Dang, Yunkai and Chen, Da and Lu, Xiaoman and Cui, Ganqu and He, Taiwen and Liu, Zhiyuan and Chua, Tat-Seng and Sun, Maosong},
journal={arXiv preprint arXiv:2405.17220},
year={2024},
}