pixtral-12b

Self-host Pixtral 12B with vLLM and BentoML

This is a BentoML example project, showing you how to serve and deploy Pixtral 12B using vLLM, a high-throughput and memory-efficient inference engine.

See here for a full list of BentoML example projects.

💡 This example is served as a basis for advanced code customization, such as custom model, inference logic or vLLM options. For simple LLM hosting with OpenAI compatible endpoint without writing any code, see OpenLLM.

Prerequisites

If you want to test the Service locally, we recommend you use an Nvidia GPU with at least 16G VRAM.

Install dependencies

git clone https://github.com/bentoml/BentoVLLM.git
cd BentoVLLM/pixtral-12b

# Recommend Python 3.11
pip install -r requirements.txt

Run the BentoML Service

We have defined a BentoML Service in service.py. Run bentoml serve in your project directory to start the Service.

$ bentoml serve .

2024-01-18T07:51:30+0800 [INFO] [cli] Starting production HTTP BentoServer from "service:VLLM" listening on http://localhost:3000 (Press CTRL+C to quit)
INFO 01-18 07:51:40 model_runner.py:501] Capturing the model for CUDA graphs. This may lead to unexpected consequences if the model is not static. To run the model in eager mode, set 'enforce_eager=True' or use '--enforce-eager' in the CLI.
INFO 01-18 07:51:40 model_runner.py:505] CUDA graphs can take additional 1~3 GiB memory per GPU. If you are running out of memory, consider decreasing `gpu_memory_utilization` or enforcing eager mode.
INFO 01-18 07:51:46 model_runner.py:547] Graph capturing finished in 6 secs.

The server is now active at http://localhost:3000. You can interact with it using the Swagger UI or in other different ways.

CURL

curl -X 'POST' \
  'http://localhost:3000/generate' \
  -H 'accept: text/event-stream' \
  -H 'Content-Type: multipart/form-data' \
  -F 'prompt=Describe this image in detail words.' \
  -F 'system_prompt=
You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.

If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don'\''t know the answer to a question, please don'\''t share false information.
' \
  -F '[email protected]' \
  -F 'max_tokens=1024'

For detailed explanations of the Service code, see vLLM inference.

Deploy to BentoCloud

After the Service is ready, you can deploy the application to BentoCloud for better management and scalability. Sign up if you haven't got a BentoCloud account.

Make sure you have logged in to BentoCloud, then run the following command to deploy it.

bentoml deploy .

Once the application is up and running on BentoCloud, you can access it via the exposed URL.

Note: For custom deployment in your own infrastructure, use BentoML to generate an OCI-compliant image.