update readme

README_en.md

@@ -27,7 +27,7 @@
 
 #### 📌 Pinned
 
-* [2024.05.28] 🚀🚀🚀 MiniCPM-Llama3-V 2.5 now fully supports its feature in [llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpm-v2.5/examples/minicpmv/README.md) and [ollama](https://github.com/OpenBMB/ollama/tree/minicpm-v2.5)! Please pull the latest code for llama.cpp & ollama. We also release GGUF in various sizes [here](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf/tree/main). FAQ list for Ollama usage is comming within a day. Please stay tuned!
+* [2024.05.28] 🚀🚀🚀 MiniCPM-Llama3-V 2.5 now fully supports its feature in [llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpm-v2.5/examples/minicpmv/README.md) and [ollama](https://github.com/OpenBMB/ollama/tree/minicpm-v2.5)! Please pull the latest code for llama.cpp & ollama. We also release GGUF in various sizes [here](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf/tree/main). FAQ list for ollama usage is comming within a day. Please stay tuned!
 * [2024.05.28] 💫 We now support LoRA fine-tuning for MiniCPM-Llama3-V 2.5, using only 2 V100 GPUs! See more statistics [here](https://github.com/OpenBMB/MiniCPM-V/tree/main/finetune#model-fine-tuning-memory-usage-statistics).
 * [2024.05.23] 🔍 We've released a comprehensive comparison between Phi-3-vision-128k-instruct and MiniCPM-Llama3-V 2.5, including benchmarks evaluations, multilingual capabilities, and inference efficiency 🌟📊🌍🚀. Click [here](./docs/compare_with_phi-3_vision.md) to view more details.
 * [2024.05.23] 🔥🔥🔥 MiniCPM-V tops GitHub Trending and Hugging Face Trending! Our demo, recommended by Hugging Face Gradio’s official account, is available [here](https://huggingface.co/spaces/openbmb/MiniCPM-Llama3-V-2_5). Come and try it out!
@@ -84,6 +84,9 @@
 - 🌏 **Multilingual Support.**
   Thanks to the strong multilingual capabilities of Llama 3 and the cross-lingual generalization technique from [VisCPM](https://github.com/OpenBMB/VisCPM), MiniCPM-Llama3-V 2.5 extends its bilingual (Chinese-English) multimodal capabilities to **over 30 languages including German, French, Spanish, Italian, Korean etc.** [All Supported Languages](./assets/minicpm-llama-v-2-5_languages.md).
 
+-  👍  **Easy Usage.**
+  In response to user demand, we have added the following convenient features: **[ollama](https://github.com/OpenBMB/ollama/tree/minicpm-v2.5/examples/minicpm-v2.5) support** for easy deployment and inference on local machines, 16 [gguf format quantized models](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf) for **[llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpm-v2.5/examples/minicpmv/README.md) inference**, **efficient [LoRA fine-tuning](https://github.com/OpenBMB/MiniCPM-V/tree/main/finetune#lora-finetuning)** with just 2 V100 GPUs, and [streaming output](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5#usage) with a simple parameter addition (stream=True). Additionally, we offer interactive demos via [Gradio](https://github.com/OpenBMB/MiniCPM-V/blob/main/web_demo_2.5.py) and [Streamlit](https://github.com/OpenBMB/MiniCPM-V/blob/main/web_demo_streamlit-2_5.py), enabling quick local WebUI setup, and online demon on [HuggingFace Spaces](https://huggingface.co/spaces/openbmb/MiniCPM-Llama3-V-2_5). 
+
 - 🚀 **Efficient Deployment.**
   MiniCPM-Llama3-V 2.5 systematically employs **model quantization, CPU optimizations, NPU optimizations and compilation optimizations**, achieving high-efficiency deployment on end-side devices. For mobile phones with Qualcomm chips, we have integrated the NPU acceleration framework QNN into llama.cpp for the first time. After systematic optimization, MiniCPM-Llama3-V 2.5 has realized a **150x acceleration in end-side MLLM image encoding** and a **3x speedup in language decoding**.
 
@@ -679,7 +682,6 @@ This project is developed by the following institutions:
 
 ## 🌟 Star History
 
-
 <div>
 <img src="./assets/Star-History.png" width="500em" ></img> 
 </div>