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feat: initial commit for openvino vllm serving microservice (#269)
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‎usecases/ai/microservices/text-generation/vllm/.gitignore‎

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data/ov_model

‎usecases/ai/microservices/text-generation/vllm/Dockerfile‎

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# Copyright (C) 2024 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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FROM debian:12-slim
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ARG DEBIAN_FRONTEND=noninteractive
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ARG VLLM_VERSION=v0.6.5
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SHELL ["/bin/bash", "-o", "pipefail", "-c"]
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RUN apt-get update \
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&& apt-get upgrade -y \
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&& apt-get install --no-install-recommends -y \
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sudo \
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curl \
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git \
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gpg-agent \
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software-properties-common \
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python3.11 \
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python3.11-venv \
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&& curl -fsSL https://repositories.intel.com/gpu/intel-graphics.key | gpg --dearmor | tee /usr/share/keyrings/intel-graphics.gpg \
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&& echo "deb [arch=amd64,i386 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/gpu/ubuntu jammy client" > /etc/apt/sources.list.d/intel-graphics.list \
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&& apt-get update \
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&& apt-get install -y --no-install-recommends \
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intel-opencl-icd \
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intel-level-zero-gpu \
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libze1 \
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libze-dev \
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clinfo \
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&& addgroup --system intel --gid 1000 \
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&& adduser --system --ingroup intel --uid 1000 --home /home/intel intel \
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&& echo "intel ALL=(ALL:ALL) NOPASSWD:ALL" > /etc/sudoers.d/intel \
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&& rm -rf /var/lib/apt/lists/* \
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&& mkdir -p /usr/src \
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&& chown -R intel:intel /usr/src
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USER intel
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WORKDIR /usr/src/app
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RUN python3 -m venv /usr/src/.venv \
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&& git clone https://github.com/vllm-project/vllm.git
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ENV PATH="/usr/src/.venv/bin:$PATH"
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WORKDIR /usr/src/app/vllm
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RUN git checkout ${VLLM_VERSION} \
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&& python3 -m pip install --no-cache-dir --upgrade pip \
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&& PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/cpu" python3 -m pip install --no-cache-dir -r requirements-build.txt \
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&& PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/cpu" VLLM_TARGET_DEVICE="openvino" python3 -m pip install --no-cache-dir .
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WORKDIR /usr/src/app
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RUN opt_in_out --opt_out
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COPY --chown=intel:intel entrypoint.sh /usr/src/app/entrypoint.sh
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RUN chmod +x /usr/src/app/entrypoint.sh
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HEALTHCHECK --interval=1m --timeout=30s --start-period=5s --retries=10 \
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CMD curl -f http://localhost:8000/health || exit 1
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ENTRYPOINT [ "/usr/src/app/entrypoint.sh" ]

‎usecases/ai/microservices/text-generation/vllm/README.md‎

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# VLLM OpenVINO
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## Requirements
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### Validated Hardware Requirements
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- **CPU:** 13th generation Intel Core processors or newer
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- **GPU:** Intel® Arc™ graphics
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- **RAM:** 32GB (may vary based on model size)
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- **Disk:** 128GB (may vary based on model size)
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## Quick Start
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### 1. Install Operating System
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Install the latest [Ubuntu 22.04 LTS Desktop](https://releases.ubuntu.com/jammy/). Refer to the [Ubuntu Desktop installation tutorial](https://ubuntu.com/tutorials/install-ubuntu-desktop#1-overview) if needed.
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### 2. Install GPU Driver (Optional)
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If you plan to use a GPU for inference, install the appropriate GPU driver:
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- **Intel® Arc™ A-Series Graphics:** [Installation Guide](https://github.com/intel/edge-developer-kit-reference-scripts/tree/main/gpu/arc/dg2)
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- **Intel® Data Center GPU Flex Series:** [Installation Guide](https://github.com/intel/edge-developer-kit-reference-scripts/tree/main/gpu/flex/ats)
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### 3. Set Up Docker
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Follow the instructions [here](https://docs.docker.com/engine/install/) to install Docker and Docker Compose.
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### 4. Build the OpenVINO VLLM Docker Image
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```bash
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docker build -t ov-vllm .
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```
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### 5. Run the OpenVINO VLLM container
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By default, on the container launch, it
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* **CPU**
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```bash
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docker run -it --rm \
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-p 8000:8000 \
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-e DEFAULT_MODEL_ID=Qwen/Qwen2.5-7B-Instruct \
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-e MODEL_PRECISION=int4 \
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-e SERVED_MODEL_NAME=ov-vllm \
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-e MAX_MODEL_LEN=2048 \
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-e VLLM_OPENVINO_DEVICE=CPU \
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-e VLLM_OPENVINO_KVCACHE_SPACE=8 \
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-e VLLM_OPENVINO_CPU_KV_CACHE_PRECISION=u8 \
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-e VLLM_OPENVINO_ENABLE_QUANTIZED_WEIGHTS=ON \
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-v ./data:/usr/src/app/data \
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ov-vllm
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```
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* **GPU**
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```bash
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RENDER_GROUP_ID=$(getent group render | cut -d: -f3)
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docker run -it --rm \
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--group-add $RENDER_GROUP_ID \
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--device /dev/dri:/dev/dri \
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-p 8000:8000 \
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-e DEFAULT_MODEL_ID=Qwen/Qwen2.5-7B-Instruct \
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-e MODEL_PRECISION=int4 \
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-e SERVED_MODEL_NAME=ov-vllm \
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-e MAX_MODEL_LEN=2048 \
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-e GPU_MEMORY_UTILIZATION=0.9 \
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-e VLLM_OPENVINO_DEVICE=GPU \
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-e VLLM_OPENVINO_KVCACHE_SPACE=8 \
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-e VLLM_OPENVINO_CPU_KV_CACHE_PRECISION=u8 \
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-e VLLM_OPENVINO_ENABLE_QUANTIZED_WEIGHTS=ON \
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-v ./data:/usr/src/app/data \
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ov-vllm
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```
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### 6. Test the OpenVINO VLLM with chat completion API
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```bash
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curl "http://localhost:8000/v1/chat/completions" \
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-H "Content-Type: application/json" \
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-d '{
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"model": "ov-vllm",
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"messages": [
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{
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"role": "system",
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"content": "You are a helpful assistant."
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},
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{
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"role": "user",
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"content": "What is AI?"
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}
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],
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"stream": true
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}'
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```
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## FAQs
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### 1. How can I replace or use my own model?
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1. Convert the model into OpenVINO format. Refer to this [link](https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide/genai-model-preparation.html) for more information.
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2. After the model convertion steps, place the model in the following following file structures.
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```bash
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.
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├── data
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│ └── ov_model
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│ ├── added_tokens.json
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│ ├── config.json
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│ ├── generation_config.json
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│ ├── merges.txt
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│ ├── openvino_model.bin
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│ ├── openvino_model.xml
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│ ├── special_tokens_map.json
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│ ├── tokenizer_config.json
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│ ├── tokenizer.json
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│ └── vocab.json
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├── Dockerfile
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├── entrypoint.sh
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└── README.md
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```
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### 2. How can I change the default model after it has been run once?
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1. Delete the existing model located in `./data/ov_model`.
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```bash
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rm -rf ./data/ov_model
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```
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2. Rerun the `docker run` command to load and quantize the new model.
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### 3. How can I avoid redownload the model everytime to convert and quantize the model?
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1. Mount the huggingface cache path into the container
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```bash
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-v $HOME/.cache/huggingface:/home/intel/.cache/huggingface
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```

‎usecases/ai/microservices/text-generation/vllm/data/.gitkeep‎

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‎usecases/ai/microservices/text-generation/vllm/entrypoint.sh‎

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#!/bin/bash
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# Copyright (C) 2024 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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echo -e "Initializing OpenVINO VLLM service ..."
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export DEFAULT_MODEL_ID=${DEFAULT_MODEL_ID:-Qwen/Qwen2.5-7B-Instruct}
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export MODEL_PATH=${MODEL_PATH:-./data/ov_model}
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export MODEL_PRECISION=${MODEL_PRECISION:-int4}
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export SERVED_MODEL_NAME=${SERVED_MODEL_NAME:-ov-vllm}
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export MAX_MODEL_LEN=${MAX_MODEL_LEN:-2048}
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export GPU_MEMORY_UTILIZATION=${GPU_MEMORY_UTILIZATION:-0.9}
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export VLLM_OPENVINO_DEVICE=${VLLM_OPENVINO_DEVICE:-CPU}
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export VLLM_OPENVINO_KVCACHE_SPACE=${VLLM_OPENVINO_KVCACHE_SPACE:-8}
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export VLLM_OPENVINO_CPU_KV_CACHE_PRECISION=${VLLM_OPENVINO_CPU_KV_CACHE_PRECISION:-u8}
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export VLLM_OPENVINO_ENABLE_QUANTIZED_WEIGHTS=${VLLM_OPENVINO_ENABLE_QUANTIZED_WEIGHTS:-ON}
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echo -e "Using the following configuration:"
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echo -e "- VLLM_OPENVINO_DEVICE: ${VLLM_OPENVINO_DEVICE}"
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echo -e "- VLLM_OPENVINO_KVCACHE_SPACE: ${VLLM_OPENVINO_KVCACHE_SPACE}"
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echo -e "- VLLM_OPENVINO_CPU_KV_CACHE_PRECISION: ${VLLM_OPENVINO_CPU_KV_CACHE_PRECISION}"
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echo -e "- VLLM_OPENVINO_ENABLE_QUANTIZED_WEIGHTS: ${VLLM_OPENVINO_ENABLE_QUANTIZED_WEIGHTS}"
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echo -e "- DEFAULT_MODEL_ID: ${DEFAULT_MODEL_ID}"
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echo -e "- MODEL_PATH: ${MODEL_PATH}"
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echo -e "- MODEL_PRECISION: ${MODEL_PRECISION}"
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echo -e "- SERVED_MODEL_NAME: ${SERVED_MODEL_NAME}"
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echo -e "- MAX_MODEL_LEN: ${MAX_MODEL_LEN}"
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echo -e "- GPU_MEMORY_UTILIZATION: ${GPU_MEMORY_UTILIZATION}"
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if [ ! -d "$MODEL_PATH" ]; then
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echo -e "Model path does not exist: $MODEL_PATH. Downloading the default model: $DEFAULT_MODEL_ID ..."
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optimum-cli export openvino \
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--model "$DEFAULT_MODEL_ID" \
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--weight-format "$MODEL_PRECISION" \
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--sym \
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--ratio 1.0 \
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--group-size -1 \
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"$MODEL_PATH"
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fi
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if [ ! -f "$MODEL_PATH/openvino_model.xml" ]; then
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echo -e "Model file does not exist: $MODEL_PATH/openvino_model.xml. Please export the model first and save to $MODEL_PATH"
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exit 1
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fi
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echo -e "Starting OpenVINO VLLM service ..."
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vllm serve "$MODEL_PATH" \
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--served_model_name "$SERVED_MODEL_NAME" \
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--max-model-len "$MAX_MODEL_LEN" \
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--gpu-memory-utilization "$GPU_MEMORY_UTILIZATION"

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