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@ -518,12 +518,12 @@ LMDeploy abstracts the complex inference process of multi-modal Vision-Language
#### A 'Hello, world' Example #### A 'Hello, world' Example
```python ```python
from lmdeploy import pipeline, TurbomindEngineConfig from lmdeploy import pipeline, TurbomindEngineConfig, ChatTemplateConfig
from lmdeploy.vl import load_image from lmdeploy.vl import load_image
model = 'OpenGVLab/InternVL3-8B' model = 'OpenGVLab/InternVL3-8B'
image = load_image('https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg') 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)) response = pipe(('describe this image', image))
print(response.text) 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. 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 ```python
from lmdeploy import pipeline, TurbomindEngineConfig from lmdeploy import pipeline, TurbomindEngineConfig, ChatTemplateConfig
from lmdeploy.vl import load_image from lmdeploy.vl import load_image
from lmdeploy.vl.constants import IMAGE_TOKEN from lmdeploy.vl.constants import IMAGE_TOKEN
model = 'OpenGVLab/InternVL3-8B' 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=[ image_urls=[
'https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/demo/resources/human-pose.jpg', '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: Conducting inference with batch prompts is quite straightforward; just place them within a list structure:
```python ```python
from lmdeploy import pipeline, TurbomindEngineConfig from lmdeploy import pipeline, TurbomindEngineConfig, ChatTemplateConfig
from lmdeploy.vl import load_image from lmdeploy.vl import load_image
model = 'OpenGVLab/InternVL3-8B' 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=[ image_urls=[
"https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/demo/resources/human-pose.jpg", "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. 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 ```python
from lmdeploy import pipeline, TurbomindEngineConfig, GenerationConfig from lmdeploy import pipeline, TurbomindEngineConfig, GenerationConfig, ChatTemplateConfig
from lmdeploy.vl import load_image from lmdeploy.vl import load_image
model = 'OpenGVLab/InternVL3-8B' 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') 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) 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: 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 ```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: To use the OpenAI-style interface, you need to install OpenAI: