anodyine/vllm-omni
1
0 Fork 0
Code Issues 4 Pull Requests 2 Actions Packages Projects Releases Wiki Activity

[Docs] optimize and supplement docs system (#86)

Browse Source 
Signed-off-by: Gaohan123 <gaohan19@huawei.com>
This commit is contained in:
Gao Han authored and GitHub committed 2025-11-28 11:03:52 +08:00
1 parent 38f58064eb
commit db81ee9f69
75 files changed
+2452 -512
Download Patch File Download Diff File Expand all files Collapse all files

No files matched your search

docs/user_guide/examples/index.md
-8
View File
@@ -1,8 +0,0 @@
# Examples
vLLM-omni's examples are split into two categories:
- If you are using vLLM-omni from within Python code, see the *Offline Inference* section.
- If you are using vLLM-omni from an HTTP application or client, see the *Online Serving* section.
For detailed example documentation, check the [examples directory](https://github.com/vllm-project/vllm-omni/tree/main/examples) in the repository.
docs/user_guide/examples/offline_inference/qwen2_5_omni.md
+13 -13
View File
@@ -1,6 +1,6 @@
# Offline Example of vLLM-omni for Qwen2.5-omni
Source <https://github.com/vllm-project/vllm-omni/tree/main/examples\offline_inference\qwen2_5_omni>.
Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/qwen2_5_omni>.
## 🛠️ Installation
@@ -36,29 +36,29 @@ Then run the command below.
bash run_single_prompt.sh
```
### FAQ
If you encounter error about backend of librosa, try to install ffmpeg with command below.
```
sudo apt update
sudo apt install ffmpeg
```
## Example materials
??? abstract "end2end.py"
``````py
--8<-- "examples\offline_inference\qwen2_5_omni\end2end.py"
--8<-- "examples/offline_inference/qwen2_5_omni/end2end.py"
``````
??? abstract "extract_prompts.py"
``````py
--8<-- "examples\offline_inference\qwen2_5_omni\extract_prompts.py"
``````
??? abstract "processing_omni.py"
``````py
--8<-- "examples\offline_inference\qwen2_5_omni\processing_omni.py"
--8<-- "examples/offline_inference/qwen2_5_omni/extract_prompts.py"
``````
??? abstract "run_multiple_prompts.sh"
``````sh
--8<-- "examples\offline_inference\qwen2_5_omni\run_multiple_prompts.sh"
--8<-- "examples/offline_inference/qwen2_5_omni/run_multiple_prompts.sh"
``````
??? abstract "run_single_prompt.sh"
``````sh
--8<-- "examples\offline_inference\qwen2_5_omni\run_single_prompt.sh"
``````
??? abstract "utils.py"
``````py
--8<-- "examples\offline_inference\qwen2_5_omni\utils.py"
--8<-- "examples/offline_inference/qwen2_5_omni/run_single_prompt.sh"
``````
docs/user_guide/examples/offline_inference/qwen_2_5_omni.md
-97
View File
@@ -1,97 +0,0 @@
# Offline Example of vLLM-omni for Qwen2.5-omni
Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/qwen_2_5_omni>.
## 🛠️ Installation
### Environment setup
Use Docker to keep consistent basic environment (Optional, Recommended)
```bash
docker run --gpus all --ipc=host --network=host -v $source_dir:$container_dir --name $container_name -it nvcr.io/nvidia/pytorch:25.01-py3 bash
```
Set up basic uv environment
```bash
pip install uv
uv venv --python 3.12 --seed
source .venv/bin/activate
```
### Installation of vLLM (for users)
Now we build it based on vLLM v0.11.0. Please install it with command below.
```bash
uv pip install vllm==0.11.0 --torch-backend=auto
```
### Installation of vLLM (for developers)
If you want to check or debug with source code of vLLM, install stable release version of vllm with 0.11.0 from source with pre-built wheel file.
```bash
git clone https://github.com/vllm-project/vllm.git
cd vllm
git checkout v0.11.0
```
Set up environment variables to get pre-built wheels. If there are internet problems, just download the whl file manually. And set VLLM_PRECOMPILED_WHEEL_LOCATION as your local absolute path of whl file.
```bash
export VLLM_PRECOMPILED_WHEEL_LOCATION=https://github.com/vllm-project/vllm/releases/download/v0.11.0/vllm-0.11.0-cp38-abi3-manylinux1_x86_64.whl
```
Install vllm with command below (If you have no existing PyTorch).
```bash
uv pip install --editable .
```
Install vllm with command below (If you already have PyTorch).
```bash
python use_existing_torch.py
uv pip install -r requirements/build.txt
uv pip install --no-build-isolation --editable .
```
### Verification for successful installation of vLLM
Just run the command below. If no error, it demonstrates that the installation is successful.
```bash
python -c "import vllm._C"
```
### Installation of vLLM-omni
Install additional requirements for vllm-omni
```bash
git clone https://github.com/vllm-project/vllm-omni.git
cd vllm_omni
uv pip install -e .
```
## Run examples (Qwen2.5-omni)
Get into the example folder
```bash
cd examples/offline_inference/qwen_2_5_omni
```
Then run
```bash
bash run.sh
```
The output audio is saved in ./output_audio
## Example materials
??? abstract "end2end.py"
``````py
--8<-- "examples/offline_inference/qwen_2_5_omni/end2end.py"
``````
??? abstract "processing_omni.py"
``````py
--8<-- "examples/offline_inference/qwen_2_5_omni/processing_omni.py"
``````
??? abstract "run.sh"
``````sh
--8<-- "examples/offline_inference/qwen_2_5_omni/run.sh"
``````
??? abstract "utils.py"
``````py
--8<-- "examples/offline_inference/qwen_2_5_omni/utils.py"
``````
docs/user_guide/examples/online_serving/README.md
+48 -3
View File
@@ -1,6 +1,6 @@
# Online serving Example of vLLM-omni for Qwen2.5-omni
Source <https://github.com/vllm-project/vllm-omni/blob/main/examples\online_serving\README.md>.
Source <https://github.com/vllm-project/vllm-omni/blob/main/examples/online_serving/README.md>.
## 🛠️ Installation
@@ -26,10 +26,55 @@ cd examples/online_serving
Send request via python
```bash
python openai_chat_completion_client_for_multimodal_generation.py
python openai_chat_completion_client_for_multimodal_generation.py --query-type mixed_modalities
```
Send request via curl
```bash
bash run_curl_multimodal_generation.sh
bash run_curl_multimodal_generation.sh mixed_modalities
```
### FAQ
If you encounter error about backend of librosa, try to install ffmpeg with command below.
```
sudo apt update
sudo apt install ffmpeg
```
## Run Local Web UI Demo
This Web UI demo allows users to interact with the model through a web browser.
### Running Gradio Demo
Once vllm and vllm-omni are installed, you can launch the web service built on AsyncOmniLLM by
```bash
python gradio_demo.py --model Qwen/Qwen2.5-Omni-7B --port 7861
```
Then open `http://localhost:7861/` on your local browser to interact with the web UI.
### Options
The gradio demo also supports running with an existing API server and can be customized with the following arguments.
```bash
python gradio_demo.py \
--model Qwen/Qwen2.5-Omni-7B \
--use-api-server \
--api-base http://localhost:8091/v1 \
--ip 127.0.0.1 \
--port 7861
```
- `--model`: Model name
- `--use-api-server`: If set, connect to an existing vLLM HTTP API server instead of running AsyncOmniLLM locally.
- `--api-base`: Base URL for vllm serve (only used when `use-api-server` is set, default: http://localhost:8091/v1)
- `--ip`: Host/IP for Gradio server (default: 127.0.0.1)
- `--port`: Port for Gradio server (default: 7861)
- `--stage-configs-path`: Path to custom stage configs YAML file (optional)
- `--share`: Share the Gradio demo publicly (creates a public link)
docs/user_guide/examples/online_serving/gradio_demo.md
+7
View File
@@ -0,0 +1,7 @@
# Gradio Demo
Source <https://github.com/vllm-project/vllm-omni/blob/main/examples/online_serving/gradio_demo.py>.
``````py
--8<-- "examples/online_serving/gradio_demo.py"
``````
docs/user_guide/examples/online_serving/index.md
-5
View File
@@ -1,5 +0,0 @@
# Online Serving
## Available Examples
Online serving is under active development. Coming soon.
docs/user_guide/examples/online_serving/openai_chat_completion_client_for_multimodal_generation.md
+2 -2
View File
@@ -1,7 +1,7 @@
# OpenAI Chat Completion Client For Multimodal Generation
Source <https://github.com/vllm-project/vllm-omni/blob/main/examples\online_serving\openai_chat_completion_client_for_multimodal_generation.py>.
Source <https://github.com/vllm-project/vllm-omni/blob/main/examples/online_serving/openai_chat_completion_client_for_multimodal_generation.py>.
``````py
--8<-- "examples\online_serving\openai_chat_completion_client_for_multimodal_generation.py"
--8<-- "examples/online_serving/openai_chat_completion_client_for_multimodal_generation.py"
``````
docs/user_guide/getting_started/quickstart.md
-55
View File
@@ -1,55 +0,0 @@
# Quickstart
This guide will help you get started with vLLM-omni.
## Installation
Use Docker to keep consistent basic environment (Optional, Recommended)
```bash
docker run --gpus all --ipc=host --network=host -v $source_dir:$container_dir --rm --name $container_name -it nvcr.io/nvidia/pytorch:25.01-py3 bash
```
Set up basic uv environment
```bash
pip install uv
uv venv --python 3.12 --seed
source .venv/bin/activate
```
Install vLLM
```bash
uv pip install vllm==0.11.0 --torch-backend=auto
```
Install vLLM-omni
```bash
git clone https://github.com/vllm-project/vllm-omni.git
cd vllm_omni
uv pip install -e .
```
## Run examples (Qwen2.5-omni)
Get into the example folder
```bash
cd examples/offline_inference/qwen_2_5_omni
```
Then run.
```bash
bash run.sh
```
The output audio is saved in ./output_audio
## Next Steps
- Read the [architecture documentation](../../contributing/design_documents/vllm_omni_design.md)
- Check out the [API reference](../../api/overview.md)
- Explore the [examples](../examples/index.md)