From 610a8827dab5db048ca049f24b1e787548e7b676 Mon Sep 17 00:00:00 2001
From: Haonan Zhang <zchiowal@gmail.com>
Date: Wed, 13 Nov 2024 20:10:43 +0800
Subject: [PATCH] Update README.md

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+<p align="center">
+    <img src=mmevol_sft_data/assets/mmevol.jpg  width="45%"> <br>
+    <!-- <span><b>Empowering Multimodal Large Language Models with Evol-Instruct</b></span> -->
+</p>
+
+# MMEvol: Empowering Multimodal Large Language Models with Evol-Instruct
+
+This is the official data collection of the paper "MMEvol: Empowering Multimodal Large Language Models with Evol-Instruct", the dataset and checkpoint will be released soon.
+
+We are continuously refactoring our code, be patient and wait for the latest updates!
+
+## 🔗 Links
+- Project Web: https://mmevol.github.io/
+- Arxiv: https://arxiv.org/pdf/2409.05840
+- Dataset: https://huggingface.co/datasets/Tongyi-ConvAI/MMEvol/tree/main
+
+## 🧪 Dataset Details
+
+The Tongyi-ConvAI generates this dataset for multi-modal supervised fine-tuning. This dataset was used to train **Evol-Llama3-8B-Instruct** and **Evol-Qwen2-7B** reported in [our paper](https://arxiv.org/pdf/2409.05840).
+
+To create this dataset, we first selected 163K Seed Instruction Tuning Dataset for Evol-Instruct, then we enhance data quality through an iterative process that involves a refined combination of fine-grained perception, cognitive reasoning, and interaction evolution. This process results in the generation of a more complex and diverse image-text instruction dataset, which in turn empowers MLLMs with enhanced capabilities.
+
+Below we showcase the detailed data distribution of the SEED-163K, which is prepared for multi-round evolution mentioned above:
+
+<p align="center">
+    <img src=mmevol_sft_data/assets/seed_dis.jpg  width="95%"> <br>
+    <span><b>Fig. 2. SEED-163K: 163K Curated Seed Instruction Tuning Dataset for Evol-Instruct</b></span>
+</p>
+
+## Usage
+We provide the example data for mmevol, which can be get with following steps:
+1. Download example [data.zip](http://alibaba-research.oss-cn-beijing.aliyuncs.com/mmevol/datasets.zip) and unzip it.
+2. Place the unzipped data folder in the `mmevol_sft_data` directory of the project.
+
+
+**License**: Please follow [Meta Llama 3.1 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE) and [Gemma License](https://www.kaggle.com/models/google/gemma/license/).
+
+## 📚 Citation
+
+```bibtex
+@article{xu2024magpie,
+    title={MMEvol: Empowering Multimodal Large Language Models with Evol-Instruct}, 
+    author={Run Luo, Haonan Zhang, Longze Chen, Ting-En Lin, Xiong Liu, Yuchuan Wu, Min Yang, Minzheng Wang, Pengpeng Zeng, Lianli Gao, Heng Tao Shen, Yunshui Li, Xiaobo Xia, Fei Huang, Jingkuan Song, Yongbin Li},
+    year={2024},
+    eprint={2409.05840},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+**Contact**:
+
+- Run Luo — r.luo@siat.ac.cn
+
+- Haonan Zhang — zchiowal@gmail.com