Update readme, support vLLM

README.md

@@ -14,13 +14,11 @@ datasets:
 
 ## News <!-- omit in toc -->
 
+* [2024.04.23] MiniCPM-V 2.0 supports [vLLM](#vllm) now!
 * [2024.04.18] We create a HuggingFace Space to host the demo of MiniCPM-V 2.0 at [here](https://huggingface.co/spaces/openbmb/MiniCPM-V-2)!
-* [2024.04.17] MiniCPM-V-2.0 supports deploying [WebUI Demo](https://github.com/OpenBMB/MiniCPM-V/blob/8a1f766b85595a8095651eed9a44a83a965b305b/README_en.md#minicpm-v-) now!
-* [2024.04.15] MiniCPM-V-2.0 now also supports [fine-tuning](https://github.com/modelscope/swift/blob/main/docs/source/Multi-Modal/minicpm-v-2最佳实践.md) with the SWIFT framework!
+* [2024.04.17] MiniCPM-V 2.0 supports deploying [WebUI Demo](https://github.com/OpenBMB/MiniCPM-V/blob/8a1f766b85595a8095651eed9a44a83a965b305b/README_en.md#minicpm-v-) now!
+* [2024.04.15] MiniCPM-V 2.0 supports [fine-tuning](https://github.com/modelscope/swift/blob/main/docs/source/Multi-Modal/minicpm-v-2最佳实践.md) with the SWIFT framework!
 * [2024.04.12] We open-source MiniCPM-V-2.0, which achieves comparable performance with Gemini Pro in understanding scene text and outperforms strong Qwen-VL-Chat 9.6B and Yi-VL 34B on <a href="https://rank.opencompass.org.cn/leaderboard-multimodal">OpenCompass</a>, a comprehensive evaluation over 11 popular benchmarks. Click <a href="https://openbmb.vercel.app/minicpm-v-2">here</a> to view the MiniCPM-V 2.0 technical blog.
-* [2024.03.14] MiniCPM-V now supports [fine-tuning](https://github.com/modelscope/swift/blob/main/docs/source/Multi-Modal/minicpm-v最佳实践.md) with the SWIFT framework. Thanks to [Jintao](https://github.com/Jintao-Huang) for the contribution！
-* [2024.03.01] MiniCPM-V now can be deployed on Mac!
-* [2024.02.01] We open-source MiniCPM-V and OmniLMM-12B, which support efficient end-side deployment and powerful multimodal capabilities correspondingly.
 
 ## MiniCPM-V 2.0
 
@@ -86,6 +84,31 @@ Click here to try out the Demo of [MiniCPM-V 2.0](http://120.92.209.146:80).
 ## Deployment on Mobile Phone
 MiniCPM-V 2.0 can be deployed on mobile phones with Android and Harmony operating systems. 🚀 Try it out [here](https://github.com/OpenBMB/mlc-MiniCPM).
 
+## Inference with vLLM<a id="vllm"></a>
+
+<details>
+<summary>Click to see how to inference with vLLM </summary>
+Because our pull request to vLLM is still waiting for reviewing, we fork this repository to build and test our vLLM demo. Here are the steps:
+
+1. Clone our version of vLLM:
+```shell
+git clone https://github.com/OpenBMB/vllm.git
+```
+2. Install vLLM:
+```shell
+cd vllm
+pip install -e .
+```
+3. Install timm: 
+```shell
+pip install timm=0.9.10
+```
+4. Run our demo:
+```shell
+python examples/minicpmv_example.py 
+```
+</details>
+
 
 ## Usage
 Inference using Huggingface transformers on Nivdia GPUs or Mac with MPS (Apple silicon or AMD GPUs). Requirements tested on python 3.10：