

EVE
Overview :
EVE is a decoder-free vision-language model jointly developed by researchers from Dalian University of Technology, Beijing Institute of Artificial Intelligence, and Peking University. It demonstrates exceptional capabilities across different image aspect ratios, outperforming Fuyu-8B and approaching the performance of modular encoder-based LVMs. EVE excels in data efficiency and training efficiency, using 33M publicly available data for pre-training and leveraging 665K LLaVA SFT data for training the EVE-7B model, along with an additional 1.2M SFT data for the EVE-7B (HD) model. The development of EVE adopts efficient, transparent, and practical strategies, paving the way for novel paradigms in cross-modal pure decoder architectures.
Target Users :
The EVE model is primarily intended for researchers and developers in the field of artificial intelligence, especially those specializing in vision-language tasks and natural language processing. Due to its efficient data processing capabilities and training efficiency, EVE is highly suitable for scenarios requiring handling of large-scale visual data and language models, and it plays a significant role in advancing the field of artificial intelligence.
Use Cases
Researchers utilize the EVE model for image captioning tasks.
Developers leverage EVE for the development of visual question answering systems.
Educational institutions employ the EVE model to teach the construction and application of vision-language models.
Features
Vision-language model design for any image aspect ratio.
Efficient pre-training using a small amount of public data.
Further optimization through the use of a large amount of SFT data.
Training efficiency achieved in approximately 9 days using two 8-A100 (40G) nodes.
Decoder-free architecture, simplifying model complexity and enhancing transparency.
Superior performance exhibited across multiple vision-language tasks.
How to Use
Visit the EVE GitHub page to access project information and code.
Read the README file to understand the model's installation and configuration requirements.
Download and install the necessary dependencies as instructed.
Clone or download the EVE model repository to your local environment.
Follow the steps outlined in the documentation to train or test the model.
Adjust model parameters as needed to suit different vision-language tasks.
Engage in community discussions to seek assistance or contribute code.
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