[Doc] Misc documentation polishing (#98)
Signed-off-by: Roger Wang <hey@rogerw.io> Signed-off-by: Gao Han <gaohan19@huawei.com> Co-authored-by: Gao Han <gaohan19@huawei.com>
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# Offline Example of vLLM-omni for Qwen2.5-omni
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# Offline Inference 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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## 🛠️ Installation
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Please refer to [README.md](https://github.com/vllm-project/vllm-omni/tree/main/README.md)
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Please refer to [installation](../../../getting_started/installation/README.md).
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## Run Offline inference with Qwen2.5-Omni.
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First, navigate to the example folder
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```bash
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cd examples/offline_inference/qwen2_5_omni
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```
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Inside the directory, `end2end.py` is a comprehensive demo suite for initializing a model instance of `Qwen/Qwen2.5-Omni-7B` and use it for various offline inference tasks.
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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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``````
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Below we also provide simple bash scripts to execute this file.
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### Single Prompt
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```bash
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bash run_single_prompt.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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## Run examples (Qwen2.5-omni)
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### Multiple Prompts
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Download dataset from [seed_tts](https://drive.google.com/file/d/1GlSjVfSHkW3-leKKBlfrjuuTGqQ_xaLP/edit). To get the prompt, you can:
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Download dataset from [seed_tts](https://drive.google.com/file/d/1GlSjVfSHkW3-leKKBlfrjuuTGqQ_xaLP/edit) and extract the prompts with `extract_prompts.py`
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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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```bash
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tar -xf <Your Download Path>/seedtts_testset.tar
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cp seedtts_testset/en/meta.lst examples/offline_inference/qwen2_5_omni/meta.lst
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python3 examples/offline_inference/qwen2_5_omni/extract_prompts.py \
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--input examples/offline_inference/qwen2_5_omni/meta.lst \
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--output examples/offline_inference/qwen2_5_omni/top100.txt \
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--topk 100
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```
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Get into the example folder
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```bash
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cd examples/offline_inference/qwen2_5_omni
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--topk 10
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```
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Then run the command below.
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```bash
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bash run_multiple_prompts.sh
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```
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### Single Prompts
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Get into the example folder
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```bash
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cd examples/offline_inference/qwen2_5_omni
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```
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Then run the command below.
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```bash
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bash run_single_prompt.sh
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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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``````
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### FAQ
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@@ -43,22 +58,3 @@ If you encounter error about backend of librosa, try to install ffmpeg with comm
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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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``````
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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 "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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``````
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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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@@ -1,80 +0,0 @@
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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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## 🛠️ Installation
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Please refer to [README.md](https://github.com/vllm-project/vllm-omni/blob/main/README.md)
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## Run examples (Qwen2.5-omni)
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Launch the server
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```bash
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vllm serve Qwen/Qwen2.5-Omni-7B --omni --port 8091
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```
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If you have custom stage configs file, launch the server with command below
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```bash
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vllm serve Qwen/Qwen2.5-Omni-7B --omni --port 8091 --stage-configs-path /path/to/stage_configs_file
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```
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Get into the example folder
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```bash
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cd examples/online_serving
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```
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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 --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 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 +1,80 @@
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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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## 🛠️ Installation
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Please refer to [installation](../../../getting_started/installation/README.md).
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## Deploy Qwen/Qwen2.5-Omni-7B
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First launch the OpenAI-compatible inference server
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```bash
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vllm serve Qwen/Qwen2.5-Omni-7B --omni --port 8091
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```
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If you have custom stage configs file, launch the server with command below
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```bash
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vllm serve Qwen/Qwen2.5-Omni-7B --omni --port 8091 --stage-configs-path /path/to/stage_configs_file
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```
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## Query the model
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Navigate to the example folder
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```bash
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cd examples/online_serving
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```
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Query the model server with OpenAI Python API client:
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```bash
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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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??? abstract "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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``````
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You can also query the model with `curl` command:
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```bash
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bash run_curl_multimodal_generation.sh mixed_modalities
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```
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??? abstract "run_curl_multimodal_generation.sh"
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``````py
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--8<-- "examples/online_serving/run_curl_multimodal_generation.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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## Run Local Web UI Demo
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You can also deploy a Gradio Web UI that allows users to interact with the model through a web browser. Below is an example on how to do so with `Qwen/Qwen2.5-Omni-7B`.
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### Running Gradio Demo
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Install gradio with `uv pip install "gradio>=5.49.1,<6.0.0"`, then you can launch the web service built on AsyncOmni 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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Now you can interact with model via the web UI at `http://localhost:7861/` on your local browser.
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### Options
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You can customize its basic launch parameters:
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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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--ip 127.0.0.1 \
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--port 7861 \
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--stage-configs-path /path/to/stage_configs.yaml
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```
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- `--model`: Local model checkpoint to load (default `Qwen/Qwen2.5-Omni-7B`).
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- `--ip`: Host/IP for the Gradio server (default `127.0.0.1`).
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- `--port`: Port for the Gradio server (default `7861`).
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- `--stage-configs-path`: Optional path to custom stage configs YAML.
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- `--share`: Set to expose a temporary public link via Gradio.
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