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diff --git a/docker/Qwen2VL_Dockerfile b/docker/Qwen2VL_Dockerfile
@@ -0,0 +1,14 @@
+ARG CUDA_VERSION=cu12
+
+FROM openmmlab/lmdeploy:latest-cu12 AS cu12
+ENV CUDA_VERSION_SHORT=cu123
+
+FROM openmmlab/lmdeploy:latest-cu11 AS cu11
+ENV CUDA_VERSION_SHORT=cu118
+
+FROM ${CUDA_VERSION} AS final
+
+# we use transformers to load vision part of qwen2_vl and it needs transformers > v4.44.2
+RUN python3 -m pip install git+https://github.com/huggingface/transformers.git
+
+RUN python3 -m pip install qwen_vl_utils
diff --git a/docs/en/multi_modal/qwen2_vl.md b/docs/en/multi_modal/qwen2_vl.md
@@ -1 +1,172 @@
 # Qwen2-VL
+
+LMDeploy supports the following Qwen-VL series of models, which are detailed in the table below:
+
+|    Model     |  Size  | Supported Inference Engine |
+| :----------: | :----: | :------------------------: |
+| Qwen-VL-Chat |   -    |     TurboMind, Pytorch     |
+|   Qwen2-VL   | 2B, 7B |          PyTorch           |
+
+The next chapter demonstrates how to deploy an Qwen-VL model using LMDeploy, with [Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct) as an example.
+
+## Installation
+
+Please install LMDeploy by following the [installation guide](../get_started/installation.md), and install other packages that Qwen2-VL needs
+
+```shell
+pip install qwen_vl_utils
+```
+
+Or, you can build a docker image to set up the inference environment. If the CUDA version on your host machine is `>=12.4`, you can run:
+
+```
+git clone https://github.com/InternLM/lmdeploy.git
+cd lmdeploy
+docker build --build-arg CUDA_VERSION=cu12 -t openmmlab/lmdeploy:qwen2vl . -f ./docker/Qwen2VL_Dockerfile
+```
+
+Otherwise, you can go with:
+
+```shell
+docker build --build-arg CUDA_VERSION=cu11 -t openmmlab/lmdeploy:qwen2vl . -f ./docker/Qwen2VL_Dockerfile
+```
+
+## Offline inference
+
+The following sample code shows the basic usage of VLM pipeline. For detailed information, please refer to [VLM Offline Inference Pipeline](./vl_pipeline.md)
+
+```python
+from lmdeploy import pipeline
+from lmdeploy.vl import load_image
+
+pipe = pipeline('Qwen/Qwen2-VL-2B-Instruct')
+
+image = load_image('https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg')
+response = pipe((f'describe this image', image))
+print(response)
+```
+
+More examples are listed below:
+
+<details>
+  <summary>
+    <b>multi-image multi-round conversation, combined images</b>
+  </summary>
+
+```python
+from lmdeploy import pipeline, GenerationConfig
+
+pipe = pipeline('Qwen/Qwen2-VL-2B-Instruct', log_level='INFO')
+messages = [
+    dict(role='user', content=[
+        dict(type='text', text='Describe the two images in detail.'),
+        dict(type='image_url', image_url=dict(url='https://raw.githubusercontent.com/QwenLM/Qwen-VL/master/assets/mm_tutorial/Beijing_Small.jpeg')),
+        dict(type='image_url', image_url=dict(url='https://raw.githubusercontent.com/QwenLM/Qwen-VL/master/assets/mm_tutorial/Chongqing_Small.jpeg'))
+    ])
+]
+out = pipe(messages, gen_config=GenerationConfig(top_k=1))
+
+messages.append(dict(role='assistant', content=out.text))
+messages.append(dict(role='user', content='What are the similarities and differences between these two images.'))
+out = pipe(messages, gen_config=GenerationConfig(top_k=1))
+```
+
+</details>
+
+<details>
+  <summary>
+    <b>image resolution for performance boost</b>
+  </summary>
+
+```python
+from lmdeploy import pipeline, GenerationConfig
+
+pipe = pipeline('Qwen/Qwen2-VL-2B-Instruct', log_level='INFO')
+
+min_pixels = 64 * 28 * 28
+max_pixels = 64 * 28 * 28
+messages = [
+    dict(role='user', content=[
+        dict(type='text', text='Describe the two images in detail.'),
+        dict(type='image_url', image_url=dict(min_pixels=min_pixels, max_pixels=max_pixels, url='https://raw.githubusercontent.com/QwenLM/Qwen-VL/master/assets/mm_tutorial/Beijing_Small.jpeg')),
+        dict(type='image_url', image_url=dict(min_pixels=min_pixels, max_pixels=max_pixels, url='https://raw.githubusercontent.com/QwenLM/Qwen-VL/master/assets/mm_tutorial/Chongqing_Small.jpeg'))
+    ])
+]
+out = pipe(messages, gen_config=GenerationConfig(top_k=1))
+
+messages.append(dict(role='assistant', content=out.text))
+messages.append(dict(role='user', content='What are the similarities and differences between these two images.'))
+out = pipe(messages, gen_config=GenerationConfig(top_k=1))
+```
+
+</details>
+
+## Online serving
+
+You can launch the server by the `lmdeploy serve api_server` CLI:
+
+```shell
+lmdeploy serve api_server Qwen/Qwen2-VL-2B-Instruct
+```
+
+You can also start the service using the aforementioned built docker image:
+
+```shell
+docker run --runtime nvidia --gpus all \
+    -v ~/.cache/huggingface:/root/.cache/huggingface \
+    --env "HUGGING_FACE_HUB_TOKEN=<secret>" \
+    -p 23333:23333 \
+    --ipc=host \
+    openmmlab/lmdeploy:qwen2vl \
+    lmdeploy serve api_server Qwen/Qwen2-VL-2B-Instruct
+```
+
+The docker compose is another option. Create a `docker-compose.yml` configuration file in the root directory of the lmdeploy project as follows:
+
+```yaml
+version: '3.5'
+
+services:
+  lmdeploy:
+    container_name: lmdeploy
+    image: openmmlab/lmdeploy:qwen2vl
+    ports:
+      - "23333:23333"
+    environment:
+      HUGGING_FACE_HUB_TOKEN: <secret>
+    volumes:
+      - ~/.cache/huggingface:/root/.cache/huggingface
+    stdin_open: true
+    tty: true
+    ipc: host
+    command: lmdeploy serve api_server Qwen/Qwen2-VL-2B-Instruct
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: "all"
+              capabilities: [gpu]
+```
+
+Then, you can execute the startup command as below:
+
+```shell
+docker-compose up -d
+```
+
+If you find the following logs after running `docker logs -f lmdeploy`, it means the service launches successfully.
+
+```text
+HINT:    Please open  http://0.0.0.0:23333   in a browser for detailed api usage!!!
+HINT:    Please open  http://0.0.0.0:23333   in a browser for detailed api usage!!!
+HINT:    Please open  http://0.0.0.0:23333   in a browser for detailed api usage!!!
+INFO:     Started server process [2439]
+INFO:     Waiting for application startup.
+INFO:     Application startup complete.
+INFO:     Uvicorn running on  http://0.0.0.0:23333  (Press CTRL+C to quit)
+```
+
+The arguments of `lmdeploy serve api_server` can be reviewed in detail by `lmdeploy serve api_server -h`.
+
+More information about `api_server` as well as how to access the service can be found from [here](api_server_vl.md)
diff --git a/docs/zh_cn/multi_modal/qwen2_vl.md b/docs/zh_cn/multi_modal/qwen2_vl.md
@@ -1 +1,172 @@
 # Qwen2-VL
+
+LMDeploy 支持 Qwen-VL 系列模型，具体如下：
+
+|    Model     |  Size  | Supported Inference Engine |
+| :----------: | :----: | :------------------------: |
+| Qwen-VL-Chat |   -    |     TurboMind, Pytorch     |
+|   Qwen2-VL   | 2B, 7B |          PyTorch           |
+
+本文将以[Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct)为例，演示使用 LMDeploy 部署 Qwen2-VL 系列模型的方法
+
+## 安装
+
+请参考[安装文档](../get_started/installation.md)安装 LMDeploy，并安装上游 Qwen2-VL 模型库需的依赖。
+
+```shell
+pip install qwen_vl_utils
+```
+
+或者，你可以为 Qwen2-VL 的推理构建 docker image。如果，宿主机器上的 CUDA 版本 `>=12.4`，你可以执行如下命令构建镜像：
+
+```
+git clone https://github.com/InternLM/lmdeploy.git
+cd lmdeploy
+docker build --build-arg CUDA_VERSION=cu12 -t openmmlab/lmdeploy:qwen2vl . -f ./docker/Qwen2VL_Dockerfile
+```
+
+否则的话，可以基于 LMDeploy cu11 的镜像来构建：
+
+```shell
+docker build --build-arg CUDA_VERSION=cu11 -t openmmlab/lmdeploy:qwen2vl . -f ./docker/Qwen2VL_Dockerfile
+```
+
+## 离线推理
+
+以下是使用 pipeline 进行离线推理的示例，更多用法参考[VLM离线推理 pipeline](./vl_pipeline.md)
+
+```python
+from lmdeploy import pipeline
+from lmdeploy.vl import load_image
+
+pipe = pipeline('Qwen/Qwen2-VL-2B-Instruct')
+
+image = load_image('https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg')
+response = pipe((f'describe this image', image))
+print(response)
+```
+
+更多例子如下：
+
+<details>
+  <summary>
+    <b>多图多轮对话</b>
+  </summary>
+
+```python
+from lmdeploy import pipeline, GenerationConfig
+
+pipe = pipeline('Qwen/Qwen2-VL-2B-Instruct', log_level='INFO')
+messages = [
+    dict(role='user', content=[
+        dict(type='text', text='Describe the two images in detail.'),
+        dict(type='image_url', image_url=dict(url='https://raw.githubusercontent.com/QwenLM/Qwen-VL/master/assets/mm_tutorial/Beijing_Small.jpeg')),
+        dict(type='image_url', image_url=dict(url='https://raw.githubusercontent.com/QwenLM/Qwen-VL/master/assets/mm_tutorial/Chongqing_Small.jpeg'))
+    ])
+]
+out = pipe(messages, gen_config=GenerationConfig(top_k=1))
+
+messages.append(dict(role='assistant', content=out.text))
+messages.append(dict(role='user', content='What are the similarities and differences between these two images.'))
+out = pipe(messages, gen_config=GenerationConfig(top_k=1))
+```
+
+</details>
+
+<details>
+  <summary>
+    <b>控制图片分辨率，加速推理</b>
+  </summary>
+
+```python
+from lmdeploy import pipeline, GenerationConfig
+
+pipe = pipeline('Qwen/Qwen2-VL-2B-Instruct', log_level='INFO')
+
+min_pixels = 64 * 28 * 28
+max_pixels = 64 * 28 * 28
+messages = [
+    dict(role='user', content=[
+        dict(type='text', text='Describe the two images in detail.'),
+        dict(type='image_url', image_url=dict(min_pixels=min_pixels, max_pixels=max_pixels, url='https://raw.githubusercontent.com/QwenLM/Qwen-VL/master/assets/mm_tutorial/Beijing_Small.jpeg')),
+        dict(type='image_url', image_url=dict(min_pixels=min_pixels, max_pixels=max_pixels, url='https://raw.githubusercontent.com/QwenLM/Qwen-VL/master/assets/mm_tutorial/Chongqing_Small.jpeg'))
+    ])
+]
+out = pipe(messages, gen_config=GenerationConfig(top_k=1))
+
+messages.append(dict(role='assistant', content=out.text))
+messages.append(dict(role='user', content='What are the similarities and differences between these two images.'))
+out = pipe(messages, gen_config=GenerationConfig(top_k=1))
+```
+
+</details>
+
+## 在线服务
+
+你可以通过 `lmdeploy serve api_server` CLI 工具启动服务：
+
+```shell
+lmdeploy serve api_server Qwen/Qwen2-VL-2B-Instruct
+```
+
+也可以基于前文构建的 docker image 启动服务：
+
+```shell
+docker run --runtime nvidia --gpus all \
+    -v ~/.cache/huggingface:/root/.cache/huggingface \
+    --env "HUGGING_FACE_HUB_TOKEN=<secret>" \
+    -p 23333:23333 \
+    --ipc=host \
+    openmmlab/lmdeploy:qwen2vl \
+    lmdeploy serve api_server Qwen/Qwen2-VL-2B-Instruct
+```
+
+Docker compose 的方式也是一种选择。在 LMDeploy 代码库的根目录下创建`docker-compose.yml`文件，内容参考如下：
+
+```yaml
+version: '3.5'
+
+services:
+  lmdeploy:
+    container_name: lmdeploy
+    image: openmmlab/lmdeploy:qwen2vl
+    ports:
+      - "23333:23333"
+    environment:
+      HUGGING_FACE_HUB_TOKEN: <secret>
+    volumes:
+      - ~/.cache/huggingface:/root/.cache/huggingface
+    stdin_open: true
+    tty: true
+    ipc: host
+    command: lmdeploy serve api_server Qwen/Qwen2-VL-2B-Instruct
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: "all"
+              capabilities: [gpu]
+```
+
+然后，你就可以执行命令启动服务了：
+
+```shell
+docker-compose up -d
+```
+
+通过`docker logs -f lmdeploy`可以查看启动的日志信息，如果发现类似下方的日志信息，就表明服务启动成功了。
+
+```text
+HINT:    Please open  http://0.0.0.0:23333   in a browser for detailed api usage!!!
+HINT:    Please open  http://0.0.0.0:23333   in a browser for detailed api usage!!!
+HINT:    Please open  http://0.0.0.0:23333   in a browser for detailed api usage!!!
+INFO:     Started server process [2439]
+INFO:     Waiting for application startup.
+INFO:     Application startup complete.
+INFO:     Uvicorn running on  http://0.0.0.0:23333  (Press CTRL+C to quit)
+```
+
+有关 `lmdeploy serve api_server` 的详细参数可以通过`lmdeploy serve api_server -h`查阅。
+
+关于 `api_server` 更多的介绍，以及访问 `api_server` 的方法，请阅读[此处](api_server_vl.md)