Update README_en.md

README_en.md

@@ -30,7 +30,7 @@ Join our <a href="docs/wechat.md" target="_blank"> 💬 WeChat</a>
 #### 📌 Pinned
 * [2024.08.06] 🔥🔥🔥 We open-source MiniCPM-V 2.6, which outperforms GPT-4V on single image, multi-image and video understanding. It advances popular features of MiniCPM-Llama3-V 2.5, and can support real-time video understanding on iPad. Try it now!
 * [2024.08.03] MiniCPM-Llama3-V 2.5 technical report is released! See [here](https://arxiv.org/abs/2408.01800).
-* [2024.07.19] MiniCPM-Llama3-V 2.5 supports vLLM now! See [here](#vllm).
+* [2024.07.19] MiniCPM-Llama3-V 2.5 supports vLLM now! See [here](#inference-with-vllm).
 * [2024.05.28] 🚀🚀🚀 MiniCPM-Llama3-V 2.5 now fully supports its feature in llama.cpp and ollama! Please pull the latest code **of our provided forks** ([llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpm-v2.5/examples/minicpmv/README.md), [ollama](https://github.com/OpenBMB/ollama/tree/minicpm-v2.5/examples/minicpm-v2.5)). GGUF models in various sizes are available [here](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf/tree/main). MiniCPM-Llama3-V 2.5 series is **not supported by the official repositories yet**, and we are working hard to merge PRs. 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.
@@ -45,7 +45,7 @@ Join our <a href="docs/wechat.md" target="_blank"> 💬 WeChat</a>
 * [2024.05.25] MiniCPM-Llama3-V 2.5 now supports streaming outputs and customized system prompts. Try it [here](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5#usage)!
 * [2024.05.24] We release the MiniCPM-Llama3-V 2.5 [gguf](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf), which supports [llama.cpp](#inference-with-llamacpp) inference and provides a 6~8 token/s smooth decoding on mobile phones. Try it now!
 * [2024.05.20] We open-soure MiniCPM-Llama3-V 2.5, it has improved OCR capability and supports 30+ languages, representing the first end-side MLLM achieving GPT-4V level performance! We provide [efficient inference](#deployment-on-mobile-phone) and [simple fine-tuning](./finetune/readme.md). Try it now!
-* [2024.04.23] MiniCPM-V-2.0 supports vLLM now! Click [here](#vllm) to view more details.
+* [2024.04.23] MiniCPM-V-2.0 supports vLLM now! Click [here](#inference-with-vllm) to view more details.
 * [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](#webui-demo) 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!
@@ -1517,23 +1517,87 @@ MiniCPM-V 2.6 can run with ollama now! See [our fork of ollama](https://github.c
 <details>
 <summary> vLLM now officially supports MiniCPM-V 2.0, MiniCPM-Llama3-V 2.5 and MiniCPM-V 2.6, Click to see. </summary>
 
-1. Clone the official vLLM:
+1. Install vLLM(==0.5.4):
 ```shell
-git clone https://github.com/vllm-project/vllm.git
+pip install vllm
 ```
-2. Install vLLM:
-```shell
-cd vllm
-pip install -e .
-```
-3. Install timm: (optional, MiniCPM-V 2.0 need timm)
+2. Install timm: (optional, MiniCPM-V 2.0 need timm)
 ```shell
 pip install timm==0.9.10
 ```
-4. Run the example:（Attention: If you use model in local path, please update the model code to the latest version on Hugging Face.)
-```shell
-python examples/minicpmv_example.py 
+3. Run the example(for image):
+```python
+from transformers import AutoTokenizer
+from PIL import Image
+from vllm import LLM, SamplingParams
+
+MODEL_NAME = "openbmb/MiniCPM-V-2_6"
+# Also available for previous models
+# MODEL_NAME = "openbmb/MiniCPM-Llama3-V-2_5"
+# MODEL_NAME = "HwwwH/MiniCPM-V-2"
+
+image = Image.open("xxx.png").convert("RGB")
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
+llm = LLM(
+    model=MODEL_NAME,
+    trust_remote_code=True,
+    gpu_memory_utilization=1,
+    max_model_len=2048
+)
+
+messages = [{
+    "role":
+    "user",
+    "content":
+    # Number of images
+    "(<image>./</image>)" + \
+    "\nWhat is the content of this image?" 
+}]
+prompt = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+# Single Inference
+inputs = {
+    "prompt": prompt,
+    "multi_modal_data": {
+        "image": image
+        # Multi images, the number of images should be equal to that of `(<image>./</image>)`
+        # "image": [image, image] 
+    },
+}
+# Batch Inference
+# inputs = [{
+#     "prompt": prompt,
+#     "multi_modal_data": {
+#         "image": image
+#     },
+# } for _ in 2]
+
+
+# 2.6
+stop_tokens = ['<|im_end|>', '<|endoftext|>']
+stop_token_ids = [tokenizer.convert_tokens_to_ids(i) for i in stop_tokens]
+# 2.0
+# stop_token_ids = [tokenizer.eos_id]
+# 2.5
+# stop_token_ids = [tokenizer.eos_id, tokenizer.eot_id]
+
+sampling_params = SamplingParams(
+    stop_token_ids=stop_token_ids, 
+    use_beam_search=True,
+    temperature=0, 
+    best_of=3,
+    max_tokens=1024
+)
+
+outputs = llm.generate(inputs, sampling_params=sampling_params)
+
+print(outputs[0].outputs[0].text)
 ```
+4. click [here](https://modelbest.feishu.cn/wiki/C2BWw4ZP0iCDy7kkCPCcX2BHnOf?from=from_copylink) if you want to use it with *video*, or get more details about `vLLM`.
 </details>
 
 ## Fine-tuning