[Docs] optimize and supplement docs system (#86)
Signed-off-by: Gaohan123 <gaohan19@huawei.com>
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@@ -1,8 +0,0 @@
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# Examples
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vLLM-omni's examples are split into two categories:
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- If you are using vLLM-omni from within Python code, see the *Offline Inference* section.
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- If you are using vLLM-omni from an HTTP application or client, see the *Online Serving* section.
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For detailed example documentation, check the [examples directory](https://github.com/vllm-project/vllm-omni/tree/main/examples) in the repository.
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@@ -1,6 +1,6 @@
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# Offline Example of vLLM-omni for Qwen2.5-omni
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Source <https://github.com/vllm-project/vllm-omni/tree/main/examples\offline_inference\qwen2_5_omni>.
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Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/qwen2_5_omni>.
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## 🛠️ Installation
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@@ -36,29 +36,29 @@ Then run the command below.
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bash run_single_prompt.sh
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```
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### FAQ
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If you encounter error about backend of librosa, try to install ffmpeg with command below.
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```
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sudo apt update
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sudo apt install ffmpeg
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```
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## Example materials
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??? abstract "end2end.py"
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``````py
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--8<-- "examples\offline_inference\qwen2_5_omni\end2end.py"
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--8<-- "examples/offline_inference/qwen2_5_omni/end2end.py"
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``````
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??? abstract "extract_prompts.py"
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``````py
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--8<-- "examples\offline_inference\qwen2_5_omni\extract_prompts.py"
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``````
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??? abstract "processing_omni.py"
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``````py
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--8<-- "examples\offline_inference\qwen2_5_omni\processing_omni.py"
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--8<-- "examples/offline_inference/qwen2_5_omni/extract_prompts.py"
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``````
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??? abstract "run_multiple_prompts.sh"
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``````sh
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--8<-- "examples\offline_inference\qwen2_5_omni\run_multiple_prompts.sh"
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--8<-- "examples/offline_inference/qwen2_5_omni/run_multiple_prompts.sh"
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``````
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??? abstract "run_single_prompt.sh"
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``````sh
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--8<-- "examples\offline_inference\qwen2_5_omni\run_single_prompt.sh"
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``````
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??? abstract "utils.py"
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``````py
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--8<-- "examples\offline_inference\qwen2_5_omni\utils.py"
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--8<-- "examples/offline_inference/qwen2_5_omni/run_single_prompt.sh"
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``````
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@@ -1,97 +0,0 @@
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# Offline Example of vLLM-omni for Qwen2.5-omni
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Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/qwen_2_5_omni>.
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## 🛠️ Installation
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### Environment setup
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Use Docker to keep consistent basic environment (Optional, Recommended)
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```bash
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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
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```
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Set up basic uv environment
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```bash
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pip install uv
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uv venv --python 3.12 --seed
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source .venv/bin/activate
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```
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### Installation of vLLM (for users)
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Now we build it based on vLLM v0.11.0. Please install it with command below.
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```bash
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uv pip install vllm==0.11.0 --torch-backend=auto
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```
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### Installation of vLLM (for developers)
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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.
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```bash
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git clone https://github.com/vllm-project/vllm.git
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cd vllm
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git checkout v0.11.0
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```
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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.
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```bash
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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
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```
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Install vllm with command below (If you have no existing PyTorch).
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```bash
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uv pip install --editable .
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```
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Install vllm with command below (If you already have PyTorch).
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```bash
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python use_existing_torch.py
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uv pip install -r requirements/build.txt
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uv pip install --no-build-isolation --editable .
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```
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### Verification for successful installation of vLLM
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Just run the command below. If no error, it demonstrates that the installation is successful.
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```bash
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python -c "import vllm._C"
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```
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### Installation of vLLM-omni
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Install additional requirements for vllm-omni
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```bash
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git clone https://github.com/vllm-project/vllm-omni.git
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cd vllm_omni
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uv pip install -e .
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```
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## Run examples (Qwen2.5-omni)
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Get into the example folder
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```bash
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cd examples/offline_inference/qwen_2_5_omni
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```
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Then run
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```bash
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bash run.sh
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```
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The output audio is saved in ./output_audio
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## Example materials
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??? abstract "end2end.py"
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``````py
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--8<-- "examples/offline_inference/qwen_2_5_omni/end2end.py"
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``````
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??? abstract "processing_omni.py"
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``````py
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--8<-- "examples/offline_inference/qwen_2_5_omni/processing_omni.py"
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``````
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??? abstract "run.sh"
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``````sh
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--8<-- "examples/offline_inference/qwen_2_5_omni/run.sh"
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``````
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??? abstract "utils.py"
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``````py
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--8<-- "examples/offline_inference/qwen_2_5_omni/utils.py"
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``````
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@@ -1,6 +1,6 @@
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# Online serving Example of vLLM-omni for Qwen2.5-omni
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Source <https://github.com/vllm-project/vllm-omni/blob/main/examples\online_serving\README.md>.
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Source <https://github.com/vllm-project/vllm-omni/blob/main/examples/online_serving/README.md>.
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## 🛠️ Installation
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@@ -26,10 +26,55 @@ cd examples/online_serving
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Send request via python
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```bash
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python openai_chat_completion_client_for_multimodal_generation.py
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python openai_chat_completion_client_for_multimodal_generation.py --query-type mixed_modalities
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```
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Send request via curl
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```bash
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bash run_curl_multimodal_generation.sh
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bash run_curl_multimodal_generation.sh mixed_modalities
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```
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### FAQ
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If you encounter error about backend of librosa, try to install ffmpeg with command below.
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```
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sudo apt update
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sudo apt install ffmpeg
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```
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## Run Local Web UI Demo
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This Web UI demo allows users to interact with the model through a web browser.
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### Running Gradio Demo
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Once vllm and vllm-omni are installed, you can launch the web service built on AsyncOmniLLM by
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```bash
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python gradio_demo.py --model Qwen/Qwen2.5-Omni-7B --port 7861
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```
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Then open `http://localhost:7861/` on your local browser to interact with the web UI.
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### Options
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The gradio demo also supports running with an existing API server and can be customized with the following arguments.
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```bash
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python gradio_demo.py \
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--model Qwen/Qwen2.5-Omni-7B \
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--use-api-server \
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--api-base http://localhost:8091/v1 \
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--ip 127.0.0.1 \
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--port 7861
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```
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- `--model`: Model name
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- `--use-api-server`: If set, connect to an existing vLLM HTTP API server instead of running AsyncOmniLLM locally.
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- `--api-base`: Base URL for vllm serve (only used when `use-api-server` is set, default: http://localhost:8091/v1)
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- `--ip`: Host/IP for Gradio server (default: 127.0.0.1)
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- `--port`: Port for Gradio server (default: 7861)
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- `--stage-configs-path`: Path to custom stage configs YAML file (optional)
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- `--share`: Share the Gradio demo publicly (creates a public link)
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@@ -0,0 +1,7 @@
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# Gradio Demo
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Source <https://github.com/vllm-project/vllm-omni/blob/main/examples/online_serving/gradio_demo.py>.
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``````py
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--8<-- "examples/online_serving/gradio_demo.py"
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``````
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# Online Serving
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## Available Examples
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Online serving is under active development. Coming soon.
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+2
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# OpenAI Chat Completion Client For Multimodal Generation
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Source <https://github.com/vllm-project/vllm-omni/blob/main/examples\online_serving\openai_chat_completion_client_for_multimodal_generation.py>.
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Source <https://github.com/vllm-project/vllm-omni/blob/main/examples/online_serving/openai_chat_completion_client_for_multimodal_generation.py>.
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``````py
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--8<-- "examples\online_serving\openai_chat_completion_client_for_multimodal_generation.py"
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--8<-- "examples/online_serving/openai_chat_completion_client_for_multimodal_generation.py"
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``````
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