Efficient LLM Serving - quickstart#

Let’s deploy TinyLlama/TinyLlama-1.1B-Chat-v1.0 model and request generation.

  1. Install python dependencies for the conversion script:

pip3 install -r https://raw.githubusercontent.com/openvinotoolkit/model_server/refs/heads/releases/2024/5/demos/common/export_models/requirements.txt

  1. Run optimum-cli to download and quantize the model:

wget https://raw.githubusercontent.com/openvinotoolkit/model_server/refs/heads/releases/2024/5/demos/common/export_models/export_model.py
mkdir models
python export_model.py text_generation --source_model TinyLlama/TinyLlama-1.1B-Chat-v1.0 --weight-format int8 --kv_cache_precision u8 --config_file_path models/config.json --model_repository_path models

  1. Deploy:

docker run -d --rm -p 8000:8000 -v $(pwd)/models:/workspace:ro openvino/model_server --rest_port 8000 --config_path /workspace/config.json

Wait for the model to load. You can check the status with a simple command:

curl http://localhost:8000/v1/config

{
  "TinyLlama/TinyLlama-1.1B-Chat-v1.0": {
    "model_version_status": [
      {
        "version": "1",
        "state": "AVAILABLE",
        "status": {
          "error_code": "OK",
          "error_message": "OK"
        }
      }
    ]
  }
}

  1. Run generation

curl -s http://localhost:8000/v3/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
    "max_tokens":30,
    "stream":false,
    "messages": [
      {
        "role": "system",
        "content": "You are a helpful assistant."
      },
      {
        "role": "user",
        "content": "What is OpenVINO?"
      }
    ]
  }'| jq .

{
  "choices": [
    {
      "finish_reason": "stop",
      "index": 0,
      "logprobs": null,
      "message": {
        "content": "OpenVINO is a software toolkit developed by Intel that enables developers to accelerate the training and deployment of deep learning models on Intel hardware.",
        "role": "assistant"
      }
    }
  ],
  "created": 1718607923,
  "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
  "object": "chat.completion",
  "usage": {
    "prompt_tokens": 23,
    "completion_tokens": 30,
    "total_tokens": 53
  }
}

Note: If you want to get the response chunks streamed back as they are generated change stream parameter in the request to true.

References#