Another great option for inference is vLLM, which supports a wide range of models and performance optimizations.

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Example: Deploy Qwen 2.5 3B on an A10G

This configuration serves Qwen 2.5 3B with vLLM on an A10G GPU. Running this model is fast and cheap, making it a good example for documentation, but the process of deploying it is very similar to larger models like Llama 3.3 70B.

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Setup

Before you deploy a model, you’ll need three quick setup steps.
1

Create an API key for your Baseten account

Create an API key and save it as an environment variable:
export BASETEN_API_KEY="abcd.123456"
2

Add an access token for Hugging Face

Some models require that you accept terms and conditions on HuggingFace before deployment. To prevent issues:
  1. Accept the license for any gated models you wish to access, like Llama 3.3.
  2. Create a read-only user access token from your Hugging Face account.
  3. Add the hf_access_token secret to your Baseten workspace.
3

Install Truss in your local development environment

Install the latest version of Truss, our open-source model packaging framework, as well as OpenAI’s model inference SDK, with:
pip install --upgrade truss openai

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Configuration

Start with an empty configuration file. 
mkdir qwen-2-5-3b-engine
touch qwen-2-5-3b-engine/config.yaml
Below is an example for Qwen 2.5 3B. You can copy-paste it into the empty config.yaml we created above.
config.yaml
model_metadata:
  engine_args:
    model: Qwen/Qwen2.5-3B-Instruct
  example_model_input: # Loads sample request into Baseten playground
    messages:
        - role: system
          content: "You are a helpful assistant."
        - role: user
          content: "What does Tongyi Qianwen mean?"
    stream: true
    max_tokens: 512
    temperature: 0.6
base_image:
  image: vllm/vllm-openai:v0.7.3
docker_server:
  start_command: sh -c "HF_TOKEN=$(cat /secrets/hf_access_token) vllm serve Qwen/Qwen2.5-3B-Instruct --enable-prefix-caching --enable-chunked-prefill"
  readiness_endpoint: /health
  liveness_endpoint: /health
  predict_endpoint: /v1/completions
  server_port: 8000
runtime:
  predict_concurrency: 256
resources:
  accelerator: A10G
  use_gpu: true
environment_variables:
  hf_access_token: null

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Deployment

Pushing the model to Baseten kicks off a multi-stage deployment process. 
truss push qwen-2-5-3b-engine --publish
Upon deployment, check your terminal logs or Baseten account to find the URL for the model server.

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Inference

This model is OpenAI compatible and can be called using the OpenAI client.
call_model.py
import os
from openai import OpenAI

# https://model-XXXXXXX.api.baseten.co/environments/production/sync/v1
model_url = ""

client = OpenAI(
    base_url=model_url,
    api_key=os.environ.get("BASETEN_API_KEY"),
)

stream = client.chat.completions.create(
    model="Qwen/Qwen2.5-3B-Instruct",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What does Tongyi Qianwen mean?"}
    ],
    stream=True,
)

for chunk in stream:
    if chunk.choices[0].delta.content is not None:
        print(chunk.choices[0].delta.content, end="")
That’s it! You have successfully deployed and called a model using vLLM.