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  1. README.md +5 -5
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@@ -522,7 +522,7 @@ from lmdeploy.vl import load_image
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  model = 'OpenGVLab/InternVL3-1B'
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  image = load_image('https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg')
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- pipe = pipeline(model, backend_config=TurbomindEngineConfig(session_len=16384, tp=4))
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  response = pipe(('describe this image', image))
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  print(response.text)
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  ```
@@ -539,7 +539,7 @@ from lmdeploy.vl import load_image
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  from lmdeploy.vl.constants import IMAGE_TOKEN
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  model = 'OpenGVLab/InternVL3-1B'
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- pipe = pipeline(model, backend_config=TurbomindEngineConfig(session_len=16384, tp=4))
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  image_urls=[
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  'https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/demo/resources/human-pose.jpg',
@@ -561,7 +561,7 @@ from lmdeploy import pipeline, TurbomindEngineConfig
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  from lmdeploy.vl import load_image
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  model = 'OpenGVLab/InternVL3-1B'
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- pipe = pipeline(model, backend_config=TurbomindEngineConfig(session_len=16384, tp=4))
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  image_urls=[
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  "https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/demo/resources/human-pose.jpg",
@@ -581,7 +581,7 @@ from lmdeploy import pipeline, TurbomindEngineConfig, GenerationConfig
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  from lmdeploy.vl import load_image
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  model = 'OpenGVLab/InternVL3-1B'
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- pipe = pipeline(model, backend_config=TurbomindEngineConfig(session_len=16384, tp=4))
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  image = load_image('https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/demo/resources/human-pose.jpg')
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  gen_config = GenerationConfig(top_k=40, top_p=0.8, temperature=0.8)
@@ -596,7 +596,7 @@ print(sess.response.text)
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  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:
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  ```shell
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- lmdeploy serve api_server OpenGVLab/InternVL3-1B --server-port 23333 --tp 4
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  ```
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  To use the OpenAI-style interface, you need to install OpenAI:
 
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  model = 'OpenGVLab/InternVL3-1B'
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  image = load_image('https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg')
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+ pipe = pipeline(model, backend_config=TurbomindEngineConfig(session_len=16384, tp=1))
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  response = pipe(('describe this image', image))
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  print(response.text)
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  ```
 
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  from lmdeploy.vl.constants import IMAGE_TOKEN
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  model = 'OpenGVLab/InternVL3-1B'
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+ pipe = pipeline(model, backend_config=TurbomindEngineConfig(session_len=16384, tp=1))
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  image_urls=[
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  'https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/demo/resources/human-pose.jpg',
 
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  from lmdeploy.vl import load_image
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  model = 'OpenGVLab/InternVL3-1B'
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+ pipe = pipeline(model, backend_config=TurbomindEngineConfig(session_len=16384, tp=1))
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  image_urls=[
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  "https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/demo/resources/human-pose.jpg",
 
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  from lmdeploy.vl import load_image
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  model = 'OpenGVLab/InternVL3-1B'
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+ pipe = pipeline(model, backend_config=TurbomindEngineConfig(session_len=16384, tp=1))
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  image = load_image('https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/demo/resources/human-pose.jpg')
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  gen_config = GenerationConfig(top_k=40, top_p=0.8, temperature=0.8)
 
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  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:
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  ```shell
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+ lmdeploy serve api_server OpenGVLab/InternVL3-1B --server-port 23333 --tp 1
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  ```
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  To use the OpenAI-style interface, you need to install OpenAI: