dindang/FloorMaterialAnalyzer_AI_Model
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Upload README.md with huggingface_hub

Browse Source
This commit is contained in:
Weiyun Wang 2025-04-12 06:56:45 +00:00 committed by system
parent dab7194eaa
commit d10a145035
No known key found for this signature in database
GPG Key ID: 6A528E38E0733467
1 changed files with 9 additions and 9 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

18
README.md
View File

@ -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: