# Qwen2.5-Omni-7B ## Introduction Qwen2.5-Omni is an end-to-end multimodal model designed to perceive diverse modalities, including text, images, audio, and video, while simultaneously generating text and natural speech responses in a streaming manner. The `Qwen2.5-Omni` model was supported since `vllm-ascend:v0.11.0rc0`. This document will show the main verification steps of the model, including supported features, feature configuration, environment preparation, single-NPU and multi-NPU deployment, accuracy and performance evaluation. ## Supported Features Refer to [supported features](../../user_guide/support_matrix/supported_models.md) to get the model's supported feature matrix. Refer to [feature guide](../../user_guide/feature_guide/index.md) to get the feature's configuration. ## Environment Preparation ### Model Weight - `Qwen2.5-Omni-3B`(BF16): [Download model weight](https://huggingface.co/Qwen/Qwen2.5-Omni-3B) - `Qwen2.5-Omni-7B`(BF16): [Download model weight](https://huggingface.co/Qwen/Qwen2.5-Omni-7B) Following examples use the 7B version by default. ### Installation You can use our official docker image to run `Qwen2.5-Omni` directly. Select an image based on your machine type and start the docker image on your node, refer to [using docker](../../installation.md#set-up-using-docker). ```{code-block} bash :substitutions: # Update --device according to your device (Atlas A2: /dev/davinci[0-7] Atlas A3:/dev/davinci[0-15]). # Update the vllm-ascend image according to your environment. # Note you should download the weight to /root/.cache in advance. # Update the vllm-ascend image export IMAGE=m.daocloud.io/quay.io/ascend/vllm-ascend:|vllm_ascend_version| export NAME=vllm-ascend # Run the container using the defined variables # Note: If you are running bridge network with docker, please expose available ports for multiple nodes communication in advance docker run --rm \ --name $NAME \ --net=host \ --shm-size=1g \ --device /dev/davinci0 \ --device /dev/davinci1 \ --device /dev/davinci2 \ --device /dev/davinci3 \ --device /dev/davinci4 \ --device /dev/davinci5 \ --device /dev/davinci6 \ --device /dev/davinci7 \ --device /dev/davinci_manager \ --device /dev/devmm_svm \ --device /dev/hisi_hdc \ -v /usr/local/dcmi:/usr/local/dcmi \ -v /usr/local/Ascend/driver/tools/hccn_tool:/usr/local/Ascend/driver/tools/hccn_tool \ -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \ -v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \ -v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \ -v /etc/ascend_install.info:/etc/ascend_install.info \ -v /mnt/sfs_turbo/.cache:/root/.cache \ -it $IMAGE bash ``` ## Deployment ### Single-node Deployment #### Single NPU (Qwen2.5-Omni-7B) :::{note} The env `LOCAL_MEDIA_PATH` which allowing API requests to read local images or videos from directories specified by the server file system. Please note this is a security risk. Should only be enabled in trusted environments. ```bash export VLLM_USE_MODELSCOPE=true export MODEL_PATH="Qwen/Qwen2.5-Omni-7B" export LOCAL_MEDIA_PATH=$HOME/.cache/vllm/assets/vllm_public_assets/ vllm serve "${MODEL_PATH}" \ --host 0.0.0.0 \ --port 8000 \ --served-model-name Qwen-Omni \ --allowed-local-media-path ${LOCAL_MEDIA_PATH} \ --trust-remote-code \ --compilation-config '{"full_cuda_graph": 1}' \ --no-enable-prefix-caching ``` :::{note} Now vllm-ascend docker image should contain vllm[audio] build part, if you encounter *audio not supported issue* by any chance, please re-build vllm with [audio] flag. ```bash VLLM_TARGET_DEVICE=empty pip install -v ".[audio]" ``` ::: `--allowed-local-media-path` is optional, only set it if you need infer model with local media file. `--gpu-memory-utilization` should not be set manually only if you know what this parameter aims to. #### Multiple NPU (Qwen2.5-Omni-7B) ```bash export VLLM_USE_MODELSCOPE=true export MODEL_PATH=Qwen/Qwen2.5-Omni-7B export LOCAL_MEDIA_PATH=/local_path/to_media/ export DP_SIZE=8 vllm serve ${MODEL_PATH}\ --host 0.0.0.0 \ --port 8000 \ --served-model-name Qwen-Omni \ --allowed-local-media-path ${LOCAL_MEDIA_PATH} \ --trust-remote-code \ --compilation-config {"full_cuda_graph": 1} \ --data-parallel-size ${DP_SIZE} \ --no-enable-prefix-caching ``` `--tensor_parallel_size` no need to set for this 7B model, but if you really need tensor parallel, tp size can be one of `1/2/4`. ### Prefill-Decode Disaggregation Not supported yet. ## Functional Verification If your service start successfully, you can see the info shown below: ```bash INFO: Started server process [2736] INFO: Waiting for application startup. INFO: Application startup complete. ``` Once your server is started, you can query the model with input prompts: ```bash curl http://127.0.0.1:8000/v1/chat/completions -H "Content-Type: application/json" -H "Authorization: Bearer EMPTY" -d '{ "model": "Qwen-Omni", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "What is the text in the illustration?" }, { "type": "image_url", "image_url": { "url": "https://modelscope.oss-cn-beijing.aliyuncs.com/resource/qwen.png" } } ] } ], "max_completion_tokens": 100, "temperature": 0.7 }' ``` If you query the server successfully, you can see the info shown below (client): ```bash {"id":"chatcmpl-a70a719c12f7445c8204390a8d0d8c97","object":"chat.completion","created":1764056861,"model":"Qwen-Omni","choices":[{"index":0,"message":{"role":"assistant","content":"The text in the illustration is \"TONGYI Qwen\".","refusal":null,"annotations":null,"audio":null,"function_call":null,"tool_calls":[],"reasoning_content":null},"logprobs":null,"finish_reason":"stop","stop_reason":null,"token_ids":null}],"service_tier":null,"system_fingerprint":null,"usage":{"prompt_tokens":73,"total_tokens":88,"completion_tokens":15,"prompt_tokens_details":null},"prompt_logprobs":null,"prompt_token_ids":null,"kv_transfer_params":null} ``` ## Accuracy Evaluation Qwen2.5-Omni on vllm-ascend has been tested on AISBench. ### Using AISBench 1. Refer to [Using AISBench](../../developer_guide/evaluation/using_ais_bench.md) for details. 2. After execution, you can get the result, here is the result of `Qwen2.5-Omni-7B` with `vllm-ascend:0.11.0rc0` for reference only. | dataset | platform | metric | mode | vllm-api-stream-chat | |----- | ----- | ----- | ----- | -----| | textVQA | A2 | accuracy | gen_base64 | 83.47 | | textVQA | A3 | accuracy | gen_base64 | 84.04 | ## Performance Evaluation ### Using AISBench Refer to [Using AISBench for performance evaluation](../../developer_guide/evaluation/using_ais_bench.md#execute-performance-evaluation) for details. ### Using vLLM Benchmark Run performance evaluation of `Qwen2.5-Omni-7B` as an example. Refer to [vllm benchmark](https://docs.vllm.ai/en/latest/contributing/benchmarks.html) for more details. There are three `vllm bench` subcommands: - `latency`: Benchmark the latency of a single batch of requests. - `serve`: Benchmark the online serving throughput. - `throughput`: Benchmark offline inference throughput. Take the `serve` as an example. Run the code as follows. ```shell vllm bench serve --model Qwen/Qwen2.5-Omni-7B --dataset-name random --random-input 1024 --num-prompts 200 --request-rate 1 --save-result --result-dir ./ ``` After about several minutes, you can get the performance evaluation result.