diff --git a/README.md b/README.md
index 8c1a36d..f0749de 100644
--- a/README.md
+++ b/README.md
@@ -518,12 +518,12 @@ LMDeploy abstracts the complex inference process of multi-modal Vision-Language
 #### A 'Hello, world' Example
 
 ```python
-from lmdeploy import pipeline, TurbomindEngineConfig
+from lmdeploy import pipeline, TurbomindEngineConfig, ChatTemplateConfig
 from lmdeploy.vl import load_image
 
 model = 'OpenGVLab/InternVL3-8B'
 image = load_image('https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg')
-pipe = pipeline(model, backend_config=TurbomindEngineConfig(session_len=16384, tp=1))
+pipe = pipeline(model, backend_config=TurbomindEngineConfig(session_len=16384, tp=1), chat_template_config=ChatTemplateConfig(model_name='internvl2_5'))
 response = pipe(('describe this image', image))
 print(response.text)
 ```
@@ -535,12 +535,12 @@ If `ImportError` occurs while executing this case, please install the required d
 When dealing with multiple images, you can put them all in one list. Keep in mind that multiple images will lead to a higher number of input tokens, and as a result, the size of the context window typically needs to be increased.
 
 ```python
-from lmdeploy import pipeline, TurbomindEngineConfig
+from lmdeploy import pipeline, TurbomindEngineConfig, ChatTemplateConfig
 from lmdeploy.vl import load_image
 from lmdeploy.vl.constants import IMAGE_TOKEN
 
 model = 'OpenGVLab/InternVL3-8B'
-pipe = pipeline(model, backend_config=TurbomindEngineConfig(session_len=16384, tp=1))
+pipe = pipeline(model, backend_config=TurbomindEngineConfig(session_len=16384, tp=1), chat_template_config=ChatTemplateConfig(model_name='internvl2_5'))
 
 image_urls=[
     'https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/demo/resources/human-pose.jpg',
@@ -558,11 +558,11 @@ print(response.text)
 Conducting inference with batch prompts is quite straightforward; just place them within a list structure:
 
 ```python
-from lmdeploy import pipeline, TurbomindEngineConfig
+from lmdeploy import pipeline, TurbomindEngineConfig, ChatTemplateConfig
 from lmdeploy.vl import load_image
 
 model = 'OpenGVLab/InternVL3-8B'
-pipe = pipeline(model, backend_config=TurbomindEngineConfig(session_len=16384, tp=1))
+pipe = pipeline(model, backend_config=TurbomindEngineConfig(session_len=16384, tp=1), chat_template_config=ChatTemplateConfig(model_name='internvl2_5'))
 
 image_urls=[
     "https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/demo/resources/human-pose.jpg",
@@ -578,11 +578,11 @@ print(response)
 There are two ways to do the multi-turn conversations with the pipeline. One is to construct messages according to the format of OpenAI and use above introduced method, the other is to use the `pipeline.chat` interface.
 
 ```python
-from lmdeploy import pipeline, TurbomindEngineConfig, GenerationConfig
+from lmdeploy import pipeline, TurbomindEngineConfig, GenerationConfig, ChatTemplateConfig
 from lmdeploy.vl import load_image
 
 model = 'OpenGVLab/InternVL3-8B'
-pipe = pipeline(model, backend_config=TurbomindEngineConfig(session_len=16384, tp=1))
+pipe = pipeline(model, backend_config=TurbomindEngineConfig(session_len=16384, tp=1), chat_template_config=ChatTemplateConfig(model_name='internvl2_5'))
 
 image = load_image('https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/demo/resources/human-pose.jpg')
 gen_config = GenerationConfig(top_k=40, top_p=0.8, temperature=0.8)
@@ -597,7 +597,7 @@ print(sess.response.text)
 LMDeploy's `api_server` enables models to be easily packed into services with a single command. The provided RESTful APIs are compatible with OpenAI's interfaces. Below are an example of service startup:
 
 ```shell
-lmdeploy serve api_server OpenGVLab/InternVL3-8B --server-port 23333 --tp 1
+lmdeploy serve api_server OpenGVLab/InternVL3-8B --chat-template internvl2_5 --server-port 23333 --tp 1
 ```
 
 To use the OpenAI-style interface, you need to install OpenAI: