EVE-7B-HD-v2.0

EVEv2: Improved Baselines for Encoder-Free Vision-Language Models#

EVEv2: Improved Baselines for Encoder-Free Vision-Language Models#

Haiwen Diao*, Xiaotong Li*, Yufeng Cui*, Yueze Wang*, Haoge Deng, Ting Pan, Wenxuan Wang, Huchuan Lu📧, Xinlong Wang📧

Dalian University of Technology; Beijing Academy of Artificial Intelligence; Peking University; Beijing University of Posts and Telecommunications; University of Chinese Academy of Sciences; Chinese Academy of Sciences Institute of Automation

| Paper | Code |

Existing encoder-free vision-language models (VLMs) are rapidly narrowing the performance gap with their encoder-based counterparts, highlighting the promising potential for unified multimodal systems with structural simplicity and efficient deployment. We systematically clarify the performance gap between VLMs using pre-trained vision encoders, discrete tokenizers, and minimalist visual layers from scratch, deeply excavating the under-examined characteristics of encoder-free VLMs. We develop efficient strategies for encoder-free VLMs that rival mainstream encoder-based ones. After an in-depth investigation, we launch EVEv2.0, a new and improved family of encoder-free VLMs. We show that: (i) Properly decomposing and hierarchically associating vision and language within a unified model reduces interference between modalities. (ii) A well-designed training strategy enables effective optimization for encoder-free VLMs. Through extensive evaluation, our EVEv2.0 represents a thorough study for developing a decoder-only architecture across modalities, demonstrating superior data efficiency and strong vision-reasoning capability.

Model Weights#

We release the instruction-tuned weights of EVEv2.

✒️ Citation#

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@article{diao2025EVEv2,
  title={EVEv2: Improved Baselines for Encoder-Free Vision-Language Models},
  author={Diao, Haiwen and Li, Xiaotong and Cui, Yufeng and Wang, Yueze and Deng, Haoge and Pan, Ting and Wang, Wenxuan and Lu, Huchuan and Wang, Xinlong},
  journal={arXiv preprint arXiv:2502.06788},
  year={2025}
}