Awesome
<div align="center"> <h1 align="center">Self-host LLMs with vLLM and BentoML</h1> </div>This is a BentoML example project, showing you how to serve and deploy open-source Large Language Models using vLLM, a high-throughput and memory-efficient inference engine.
See here for a full list of BentoML example projects.
đź’ˇ 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 a Nvidia GPU with at least 16G VRAM.
Install dependencies
git clone https://github.com/bentoml/BentoVLLM.git
cd BentoVLLM/mistral-7b-instruct
# Recommend Python 3.11
pip install -r requirements.txt && pip install -f -U "pydantic>=2.0"
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.
<details> <summary>CURL</summary>curl -X 'POST' \
'http://localhost:3000/generate' \
-H 'accept: text/event-stream' \
-H 'Content-Type: application/json' \
-d '{
"prompt": "Explain superconductors like I'\''m five years old",
"tokens": null
}'
</details>
<details>
<summary>Python client</summary>
import bentoml
with bentoml.SyncHTTPClient("http://localhost:3000") as client:
response_generator = client.generate(
prompt="Explain superconductors like I'm five years old",
tokens=None
)
for response in response_generator:
print(response)
</details>
<details>
<summary>OpenAI-compatible endpoints</summary>
This Service uses the @openai_endpoints
decorator to set up OpenAI-compatible endpoints (chat/completions
and completions
). This means your client can interact with the backend Service (in this case, the VLLM class) as if they were communicating directly with OpenAI's API. This utility does not affect your BentoML Service code, and you can use it for other LLMs as well.
from openai import OpenAI
client = OpenAI(base_url='http://localhost:3000/v1', api_key='na')
# Use the following func to get the available models
client.models.list()
chat_completion = client.chat.completions.create(
model="mistralai/Mistral-7B-Instruct-v0.2",
messages=[
{
"role": "user",
"content": "Explain superconductors like I'm five years old"
}
],
stream=True,
)
for chunk in chat_completion:
# Extract and print the content of the model's reply
print(chunk.choices[0].delta.content or "", end="")
Note: If your Service is deployed with protected endpoints on BentoCloud, you need to set the environment variable OPENAI_API_KEY
to your BentoCloud API key first.
export OPENAI_API_KEY={YOUR_BENTOCLOUD_API_TOKEN}
You can then use the following line to replace the client in the above code snippet. Refer to Obtain the endpoint URL to retrieve the endpoint URL.
client = OpenAI(base_url='your_bentocloud_deployment_endpoint_url/v1')
</details>
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.
Different LLM Models
Besides the mistral-7b-instruct model, we have examples for other models in subdirectories of this repository. Below is a list of these models and links to the example subdirectories.
- gemma-7b-it
- Llama-2-7b-chat-hf
- Llama-3-8b-instruct
- Llama-3-70b-instruct with AWQ quantization
- Mistral-7B-Instruct-v0.2
- Mixtral-8x7B-Instruct-v0.1 with gptq quantization
- Outlines integration
- SOLAR-10.7B-v1.0
LLM tools integration examples
- Every model directory contains codes to add OpenAI compatible endpoints to the BentoML service.
- outlines-integration/ contains the code to integrate with outlines for structured generation.