vLLM

vLLM is a high-throughput serving engine for language models that optimizes inference performance through advanced memory management and batching techniques. It provides easy integration with popular model architectures while maximizing GPU utilization for production deployments.

The deployments in this document are for deploying AFM-4.5B; however, they work the exact same for all Arcee AI models. To deploy a different model, simply change the model name to the model you'd like to deploy.

Docker Container for vLLM

Prerequisite

1. GPU Instance with > 9 GB VRAM (if running the model in bf16)

2. A Hugging Face account with access to arcee-ai/AFM-4.5B

3. Docker and NVIDIA Container Toolkit installed on your instance

   1. If you need assistance, see Install Docker Engine and Installing the NVIDIA Container Toolkit

Deployment

docker run --gpus all \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HUGGING_FACE_HUB_TOKEN=your_hf_token_here" \
    -p 8000:8000 \
    --ipc=host \
    vllm/vllm-openai:latest \
    --model arcee-ai/AFM-4.5B \
    --host 0.0.0.0 \
    --port 8000 \
    --max-model-len 8192 \
    --served-model-name afm \
    --model_impl transformers \
    --trust-remote-code

Replace your_hf_token_here with your Hugging Face token

Manual Install using vLLM

Prerequisite

1. A NVIDIA GPU Instance with > 9 GB VRAM (if running the model in bf16)

2. A Hugging Face account with access to arcee-ai/AFM-4.5B

These commands are for an instance running Ubuntu. They will need to be modified for other operating systems.

Deployment

1. Ensure your NVIDIA Driver is configured.

nvidia-smi

1. If information about your GPU is returned, skip this step. If not, run the following commands.

sudo apt update
sudo apt install -y ubuntu-drivers-common
sudo ubuntu-drivers install
sudo reboot

# Once you reconnect, check for correct driver configuration
nvidia-smi

1. Install necessary dev tools.

sudo apt install -y build-essential python3.12-dev

1. Setup a python virtual environment. In this guide, we'll use uv .

curl -LsSf https://astral.sh/uv/install.sh | sh
source $HOME/.local/bin/env

uv venv --python 3.12 --seed
source .venv/bin/activate

1. Install necessary dev tools, vLLM, and Hugging Face.

uv pip install vllm --torch-backend=auto
uv pip install -U "transformers<4.55"
uv pip install --upgrade huggingface_hub[cli]
sudo apt-get install git-lfs
git lfs install

1. Login to your Hugging Face Account using a HF Access Token.

hf auth login

1. Host AFM-4.5B.

vllm serve arcee-ai/AFM-4.5B \
  --host 0.0.0.0 \
  --port 8000 \
  --max-model-len 8192 \
  --served-model-name afm \
  --model_impl transformers \
  --trust-remote-code

• For max-model-len you can specify a context length of up to 65536

• For additional configuration options, see vLLM Configurations.

Run Inference on AFM-4.5B using the Chat Completions endpoint.

curl http://Your.IP.Address:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
        "model": "afm",
        "messages": [
          { "role": "user", "content": "What are the benefits of model merging" }
        ],
        "temperature": 0.7,
        "top_k": 50,
        "repeat_penalty": 1.1
      }'

Ensure you replace Your.IP.Address with the IP address of the instance you're hosting the model on