[Bugfix] Remove mandatory flash-attn dependency and optimzie docs (#113)
Signed-off-by: Gaohan123 <gaohan19@huawei.com>
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@@ -1,55 +1,40 @@
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# Offline Inference Example of vLLM-Omni for Qwen2.5-Omni
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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/tree/main/examples/offline_inference/qwen2_5_omni>.
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## 🛠️ Installation
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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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Please refer to [README.md](https://github.com/vllm-project/vllm/tree/main/README.md)
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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) 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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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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```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 10
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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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```
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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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??? 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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### Single Prompt
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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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### FAQ
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@@ -58,3 +43,22 @@ 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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|
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## Example materials
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??? abstract "end2end.py"
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``````py
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--8<-- "/mnt/vllm_open_release/vllm-omni-cursor/vllm-omni/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<-- "/mnt/vllm_open_release/vllm-omni-cursor/vllm-omni/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<-- "/mnt/vllm_open_release/vllm-omni-cursor/vllm-omni/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<-- "/mnt/vllm_open_release/vllm-omni-cursor/vllm-omni/examples/offline_inference/qwen2_5_omni/run_single_prompt.sh"
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``````
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@@ -0,0 +1,64 @@
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# Offline Example of vLLM-Omni for Qwen3-omni
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Source <https://github.com/vllm-project/vllm/tree/main/examples/offline_inference/qwen3_omni>.
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|
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## 🛠️ Installation
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|
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Please refer to [README.md](https://github.com/vllm-project/vllm/tree/main/README.md)
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## Run examples (Qwen3-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). For processing dataset please refer to [Qwen2.5-omni README.md](https://github.com/vllm-project/vllm/tree/main/examples/offline_inference/qwen2_5_omni/README.md)
|
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Get into the example folder
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```bash
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cd examples/offline_inference/qwen3_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_multiple_prompts.sh
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```
|
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### Single Prompt
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Get into the example folder
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```bash
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cd examples/offline_inference/qwen3_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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If you have not enough memory, you can set thinker with tensor parallel. Just run the command below.
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```bash
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bash run_single_prompt_tp.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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|
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## Example materials
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??? abstract "end2end.py"
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``````py
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--8<-- "/mnt/vllm_open_release/vllm-omni-cursor/vllm-omni/examples/offline_inference/qwen3_omni/end2end.py"
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``````
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??? abstract "qwen3_omni_moe_tp.yaml"
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``````yaml
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--8<-- "/mnt/vllm_open_release/vllm-omni-cursor/vllm-omni/examples/offline_inference/qwen3_omni/qwen3_omni_moe_tp.yaml"
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``````
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??? abstract "run_multiple_prompts.sh"
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``````sh
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--8<-- "/mnt/vllm_open_release/vllm-omni-cursor/vllm-omni/examples/offline_inference/qwen3_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<-- "/mnt/vllm_open_release/vllm-omni-cursor/vllm-omni/examples/offline_inference/qwen3_omni/run_single_prompt.sh"
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``````
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??? abstract "run_single_prompt_tp.sh"
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``````sh
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--8<-- "/mnt/vllm_open_release/vllm-omni-cursor/vllm-omni/examples/offline_inference/qwen3_omni/run_single_prompt_tp.sh"
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``````
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@@ -1,25 +1,15 @@
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# Offline Inference Example of vLLM-Omni for Qwen-Image
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# Qwen-Image Offline Inference
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Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/qwen_image>.
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Source <https://github.com/vllm-project/vllm/tree/main/examples/offline_inference/qwen_image>.
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|
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|
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## 🛠️ Installation
|
||||
|
||||
Please refer to [installation](../../../getting_started/installation/README.md).
|
||||
|
||||
## Run Offline inference with Qwen-Image
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First, navigate to the example folder
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```bash
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cd examples/offline_inference/qwen_image
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```
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This folder provides two simple entrypoints for experimenting with `Qwen/Qwen-Image` using vLLM-Omni:
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- `text_to_image.py`: command-line script for single image generation.
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- `gradio_demo.py`: lightweight Gradio UI for interactive prompt/seed/CFG exploration.
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- `web_demo.py`: lightweight Gradio UI for interactive prompt/seed/CFG exploration.
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### Command-line Usage
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## Local CLI Usage
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```bash
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python text_to_image.py \
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@@ -43,14 +33,9 @@ Key arguments:
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- `--height/--width`: output resolution (defaults 1024x1024).
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- `--output`: path to save the generated PNG.
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??? abstract "text_to_image.py"
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``````py
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--8<-- "examples/offline_inference/qwen_image/text_to_image.py"
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``````
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> ℹ️ Qwen-Image currently publishes best-effort presets at `1328x1328`, `1664x928`, `928x1664`, `1472x1140`, `1140x1472`, `1584x1056`, and `1056x1584`. Adjust `--height/--width` accordingly for the most reliable outcomes.
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|
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### Web UI Demo
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## Web UI Demo
|
||||
|
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Launch the gradio demo:
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@@ -60,7 +45,13 @@ python gradio_demo.py --port 7862
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Then open `http://localhost:7862/` on your local browser to interact with the web UI.
|
||||
|
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## Example materials
|
||||
|
||||
??? abstract "gradio_demo.py"
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||||
``````py
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--8<-- "examples/offline_inference/qwen_image/gradio_demo.py"
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--8<-- "/mnt/vllm_open_release/vllm-omni-cursor/vllm-omni/examples/offline_inference/qwen_image/gradio_demo.py"
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``````
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??? abstract "text_to_image.py"
|
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``````py
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--8<-- "/mnt/vllm_open_release/vllm-omni-cursor/vllm-omni/examples/offline_inference/qwen_image/text_to_image.py"
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``````
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@@ -1,43 +1,38 @@
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# Online Serving Example of vLLM-Omni for Qwen2.5-Omni
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# Online serving Example of vLLM-Omni for Qwen2.5-omni
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|
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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/tree/main/examples/online_serving/qwen2_5_omni>.
|
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|
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|
||||
## 🛠️ Installation
|
||||
|
||||
Please refer to [installation](../../../getting_started/installation/README.md).
|
||||
Please refer to [README.md](https://github.com/vllm-project/vllm/tree/main/examples/README.md)
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## Deploy Qwen/Qwen2.5-Omni-7B
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## Run examples (Qwen2.5-omni)
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First launch the OpenAI-compatible inference server
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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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## Query the model
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Navigate to the example folder
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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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Query the model server with OpenAI Python API client:
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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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??? 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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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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??? 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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@@ -49,16 +44,17 @@ sudo apt install ffmpeg
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## Run Local Web UI Demo
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|
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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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This Web UI demo allows users to interact with the model through a web browser.
|
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|
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### Running Gradio Demo
|
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|
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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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Once vllm and vllm-omni are installed, you can launch the web service built on AsyncOmniLLM by
|
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|
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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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|
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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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@@ -78,3 +74,18 @@ python gradio_demo.py \
|
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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.
|
||||
- `--share`: Set to expose a temporary public link via Gradio.
|
||||
|
||||
## Example materials
|
||||
|
||||
??? abstract "gradio_demo.py"
|
||||
``````py
|
||||
--8<-- "/mnt/vllm_open_release/vllm-omni-cursor/vllm-omni/examples/online_serving/qwen2_5_omni/gradio_demo.py"
|
||||
``````
|
||||
??? abstract "openai_chat_completion_client_for_multimodal_generation.py"
|
||||
``````py
|
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--8<-- "/mnt/vllm_open_release/vllm-omni-cursor/vllm-omni/examples/online_serving/qwen2_5_omni/openai_chat_completion_client_for_multimodal_generation.py"
|
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``````
|
||||
??? abstract "run_curl_multimodal_generation.sh"
|
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``````sh
|
||||
--8<-- "/mnt/vllm_open_release/vllm-omni-cursor/vllm-omni/examples/online_serving/qwen2_5_omni/run_curl_multimodal_generation.sh"
|
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``````
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@@ -0,0 +1,95 @@
|
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# Online serving Example of vLLM-Omni for Qwen3-omni
|
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|
||||
Source <https://github.com/vllm-project/vllm/tree/main/examples/online_serving/qwen3_omni>.
|
||||
|
||||
|
||||
## 🛠️ Installation
|
||||
|
||||
Please refer to [README.md](https://github.com/vllm-project/vllm/tree/main/examples/README.md)
|
||||
|
||||
## Run examples (Qwen3-Omni)
|
||||
|
||||
Launch the server
|
||||
```bash
|
||||
vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091
|
||||
```
|
||||
|
||||
If you have custom stage configs file, launch the server with command below
|
||||
```bash
|
||||
vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091 --stage-configs-path /path/to/stage_configs_file
|
||||
```
|
||||
|
||||
Get into the example folder
|
||||
```bash
|
||||
cd examples/online_serving
|
||||
```
|
||||
|
||||
Send request via python
|
||||
```bash
|
||||
python openai_chat_completion_client_for_multimodal_generation.py --query-type mixed_modalities
|
||||
```
|
||||
|
||||
Send request via curl
|
||||
```bash
|
||||
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/Qwen3-Omni-30B-A3B-Instruct --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/Qwen3-Omni-30B-A3B-Instruct \
|
||||
--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)
|
||||
|
||||
## Example materials
|
||||
|
||||
??? abstract "gradio_demo.py"
|
||||
``````py
|
||||
--8<-- "/mnt/vllm_open_release/vllm-omni-cursor/vllm-omni/examples/online_serving/qwen3_omni/gradio_demo.py"
|
||||
``````
|
||||
??? abstract "openai_chat_completion_client_for_multimodal_generation.py"
|
||||
``````py
|
||||
--8<-- "/mnt/vllm_open_release/vllm-omni-cursor/vllm-omni/examples/online_serving/qwen3_omni/openai_chat_completion_client_for_multimodal_generation.py"
|
||||
``````
|
||||
??? abstract "run_curl_multimodal_generation.sh"
|
||||
``````sh
|
||||
--8<-- "/mnt/vllm_open_release/vllm-omni-cursor/vllm-omni/examples/online_serving/qwen3_omni/run_curl_multimodal_generation.sh"
|
||||
``````
|
||||
Reference in new issue
Block a user