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README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+---
+
+<div align='center'>
+<h1>EVEv2: Improved Baselines for Encoder-Free Vision-Language Models</h1h1>
+<h3><a href="https://xxxx">EVEv2: Improved Baselines for Encoder-Free Vision-Language Models</a></h3>
+
+[Haiwen Diao*](https://scholar.google.com/citations?user=46eCjHQAAAAJ&hl=zh-CN), [Xiaotong Li*](https://scholar.google.com/citations?hl=zh-CN&user=cpCE_T4AAAAJ), [Yufeng Cui*](https://scholar.google.com/citations?user=5Ydha2EAAAAJ&hl=zh-CN&oi=ao), [Yueze Wang*](https://openreview.net/profile?id=~Yueze_Wang1), [Haoge Deng](https://scholar.google.com/citations?user=S2sbvjgAAAAJ&hl=zh-CN), [Ting Pan](https://scholar.google.com/citations?user=qQv6YbsAAAAJ&hl=zh-CN), [Wenxuan Wang](https://scholar.google.com/citations?hl=zh-CN&user=75OyC-oAAAAJ), [Huchuan Lu📧](https://scholar.google.com/citations?user=D3nE0agAAAAJ&hl=zh-CN), [Xinlong Wang📧](https://scholar.google.com/citations?user=DPz0DjYAAAAJ&hl=zh-CN)
+
+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](https://xxxx) | [Code](https://github.com/baaivision/EVE) |
+</div>
+
+Existing encoder-free vision-language models (VLMs) are rapidly narrowing the performance gap with their encoder-based counterparts, highlighting the promising potential for monolithic 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 thereby develop the most efficient strategy for developing encoder-free VLMs that rival mainstream encoder-based ones.
+After an in-depth investigation, we launch EVEv2.0, a new family of encoder-free VLMs that explore network potentials and training recipes for efficiently constructing multi-modality paradigms. 
+We identify and evaluate that: (i) Sufficient modality-aware decomposition and hierarchical association inside one unified model eliminate interference between vision and language. (ii) Comprehensive optimization towards training recipe provides an effective training pathway tailored for encoder-free VLMs. 
+On extensive evaluation, our EVEv2.0 represents a significant step towards developing a pure decoder-only architecture across modalities, demonstrating superior data-scaling efficiency and powerful vision-reasoning capability.  
+
+## Model Weights
+
+We release the instruction-tuned weights of **EVEv2**. 
+
+| Model name | Weight                                                  |
+| ---------- | ------------------------------------------------------- |
+| **EVE-7B-HD-v2.0** | [🤗 HF link](https://huggingface.co/BAAI/EVE-7B-HD-v2.0) (28GB) |
+
+
+
+
+## ✒️ Citation
+If **EVE** is helpful for your research, please consider **star** ⭐ and **citation** 📝 :
+```bibtex
+xxxx
+```