diff --git a/README.md b/README.md
index 337d2f3..a2891d8 100644
--- a/README.md
+++ b/README.md
@@ -2516,103 +2516,15 @@ See [our fork of ollama](https://github.com/OpenBMB/ollama/blob/minicpm-v2.6/exa
vLLM now officially supports MiniCPM-V 2.6, MiniCPM-Llama3-V 2.5 and MiniCPM-V 2.0. And you can use our fork to run MiniCPM-o 2.6 for now. Click to see.
-1. For MiniCPM-o 2.6
- 1. Clone our fork of vLLM:
- ```shell
- git clone https://github.com/OpenBMB/vllm.git
- cd vllm
- git checkout minicpmo
- ```
- 2. Install vLLM from source:
- ```shell
- VLLM_USE_PRECOMPILED=1 pip install --editable .
- ```
- 3. Run MiniCPM-o 2.6 in the same way as the previous models (shown in the following example).
+1. Install vLLM(>=0.7.1):
+```shell
+pip install vllm
+```
-2. For previous MiniCPM-V models
- 1. Install vLLM(>=0.5.4):
- ```shell
- pip install vllm
- ```
- 2. Install timm: (optional, MiniCPM-V 2.0 need timm)
- ```shell
- pip install timm==0.9.10
- ```
- 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"
- # MODEL_NAME = "openbmb/MiniCPM-o-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
- "(./)" + \
- "\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]
- },
- }
- # 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`.
-
+2. Run Example:
+* [Vision Language](https://docs.vllm.ai/en/latest/getting_started/examples/vision_language.html)
+* [Audio Language](https://docs.vllm.ai/en/latest/getting_started/examples/audio_language.html)
+
## Fine-tuning
diff --git a/README_zh.md b/README_zh.md
index f0f4834..058d9c6 100644
--- a/README_zh.md
+++ b/README_zh.md
@@ -2396,103 +2396,17 @@ llama.cpp 用法请参考[我们的fork llama.cpp](https://github.com/OpenBMB/ll
ollama 用法请参考[我们的fork ollama](https://github.com/OpenBMB/ollama/blob/minicpm-v2.6/examples/minicpm-v2.6/README.md)， 在iPad上可以支持 16~18 token/s 的流畅推理（测试环境：iPad Pro + M4）。
-点击查看, vLLM 现已官方支持MiniCPM-V 2.6、MiniCPM-Llama3-V 2.5 和 MiniCPM-V 2.0，MiniCPM-o 2.6 模型也可以临时用我们的 fork 仓库运行。
-1. MiniCPM-o 2.6
- 1. 克隆我们的 vLLM fork 仓库:
- ```shell
- git clone https://github.com/OpenBMB/vllm.git
- cd vllm
- git checkout minicpmo
- ```
- 2. 从源码进行安装:
- ```shell
- VLLM_USE_PRECOMPILED=1 pip install --editable .
- ```
- 3. 用和之前同样的方式运行（下有样例）.
-
-2. 之前版本的 MiniCPM-V
- 1. 安装 vLLM(>=0.5.4):
- ```shell
- pip install vllm
- ```
- 3. 安装 timm 库: （可选，MiniCPM-V 2.0需安装）
- ```shell
- pip install timm=0.9.10
- ```
- 4. 运行示例代码:（注意：如果使用本地路径的模型，请确保模型代码已更新到Hugging Face上的最新版)
- ```python
- from transformers import AutoTokenizer
- from PIL import Image
- from vllm import LLM, SamplingParams
-
- MODEL_NAME = "openbmb/MiniCPM-V-2_6"
- # MODEL_NAME = "openbmb/MiniCPM-o-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
- "(./)" + \
- "\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]
- },
- }
- # 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. [点击此处](https://modelbest.feishu.cn/wiki/C2BWw4ZP0iCDy7kkCPCcX2BHnOf?from=from_copylink)查看带视频推理和其他有关 `vLLM` 的信息。
+点击查看, vLLM 现已官方支持MiniCPM-o 2.6、MiniCPM-V 2.6、MiniCPM-Llama3-V 2.5 和 MiniCPM-V 2.0。
+1. 安装 vLLM(>=0.7.1):
+
+```shell
+pip install vllm
+```
+
+2. 运行示例代码:（注意：如果使用本地路径的模型，请确保模型代码已更新到Hugging Face上的最新版)
+
+ * [图文示例](https://docs.vllm.ai/en/latest/getting_started/examples/vision_language.html)
+ * [音频示例](https://docs.vllm.ai/en/latest/getting_started/examples/audio_language.html)