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Check out the documentation for more information.
How to run
git clone https://github.com/vllm-project/vllm-omni.git
cd vllm-omni
DOCKER_BUILDKIT=1 docker build \
-f docker/Dockerfile.cuda \
--build-arg BASE_IMAGE=vllm/vllm-openai:latest \
-t vllm-omni-custom:latest \
.
The above is for Nvidia GPU.
docker rm -f vllm
docker run -d \
--name vllm \
--gpus all \
--ipc=host \
-p 8000:8000 \
-v ~/.cache/huggingface:/root/.cache/huggingface \
-e HF_TOKEN="$HF_TOKEN" \
-e CUDA_VISIBLE_DEVICES=0 \
--entrypoint /bin/bash \
vllm-omni-custom:latest \
-lc 'pip install --no-cache-dir "vllm[audio]" torchdiffeq && \
vllm serve minchyeom/StarVoice \
--omni \
--served-model-name starlette \
--dtype float32 \
--max-model-len 32768 \
--gpu-memory-utilization 0.5 \
--trust-remote-code \
--host 0.0.0.0 \
--port 8000'
Configure --gpu-memory-utilization according to your GPU VRAM budget. I tested this on a single RTX 5090.
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