Chapter 5. Downloading a model from Hugging Face before running Red Hat AI Inference Server

Procedure

1. Open a shell prompt on the RHEL AI server.

2. Stop the Red Hat AI Inference Server service:

   [cloud-user@localhost ~]$ systemctl stop rhaiis

3. Open a command prompt inside the Red Hat AI Inference Server container:

   [cloud-user@localhost ~]$ sudo podman run -it --rm \
     -e HF_TOKEN=<YOUR_HUGGING_FACE_HUB_TOKEN> \
     -v /var/lib/rhaiis/cache:/opt/app-root/src/.cache:Z \
     -v /var/lib/rhaiis/models:/opt/app-root/src/models:Z \
     --entrypoint /bin/bash \
     registry.redhat.io/rhaiis/model-opt-cuda-rhel9:3.2.3

   Note

   You use the sudo command because the download writes to directories owned by the root group.

4. Inside the container, set HF_HUB_OFFLINE to 0. Run the following command:

   (app-root) /opt/app-root$ export HF_HUB_OFFLINE=0

5. Download the model to the default directory. For example:

   (app-root) /opt/app-root$ hf download RedHatAI/granite-3.3-8b-instruct \
     --local-dir /opt/app-root/src/models/red-hat-ai-granite-3.3-8b-instruct \
     --token $HF_TOKEN

   Note

   The rhaiis/vllm-cuda-rhel9 and rhaiis/model-opt-cuda-rhel9 containers both have the same version of the Hugging Face CLI available.

6. Exit the container:

   exit

7. Edit the Red Hat AI Inference Server configuration file to use the downloaded model in offline mode:

   [cloud-user@localhost ~]$ sudo vi /etc/containers/systemd/rhaiis.container.d/install.conf

   Update the configuration to enable offline mode and use the local model path:

   [Container]
   # Set to 1 to run in offline mode and disable model downloading at runtime
   Environment=HF_HUB_OFFLINE=1
   
   # Token is not required when running in offline mode with a local model
   # Environment=HUGGING_FACE_HUB_TOKEN=
   
   # Configure vLLM to use the locally downloaded model
   Exec=--model /opt/app-root/src/models/red-hat-ai-granite-3.3-8b-instruct \
        --tensor-parallel-size 1 \
        --served-model-name RedHatAI/granite-3.3-8b-instruct \
        --max-model-len 4096
   
   PublishPort=8000:8000
   ShmSize=4G
   
   [Install]
   WantedBy=multi-user.target

   Note

   When you set the model location, you must set the location to the folder that is mapped inside the Red Hat AI Inference Server container, /opt/app-root/src/models/.

8. Reload the systemd configuration:

   [cloud-user@localhost ~]$ sudo systemctl daemon-reload

9. Start the Red Hat AI Inference Server service:

   [cloud-user@localhost ~]$ sudo systemctl start rhaiis

Verification

1. Monitor the service logs to verify the vLLM server is using the local model:

   [cloud-user@localhost ~]$ sudo podman logs -f rhaiis

   Example output

   (APIServer pid=1) INFO 11-12 14:05:33 [utils.py:233] non-default args: {'model': '/opt/app-root/src/models/red-hat-ai-granite-3.3-8b-instruct', 'max_model_len': 4096, 'served_model_name': ['RedHatAI/granite-3.3-8b-instruct']}

2. Test the inference server API:

   [cloud-user@localhost ~]$ curl -X POST -H "Content-Type: application/json" -d '{
       "prompt": "What is the capital of France?",
       "max_tokens": 50
   }' http://localhost:8000/v1/completions | jq

   Example output

   {
     "id": "cmpl-f3e12cc62bee438c86af676332f8fe55",
     "object": "text_completion",
     "created": 1762956836,
     "model": "RedHatAI/granite-3.3-8b-instruct",
     "choices": [
       {
         "index": 0,
         "text": "\n\nThe capital of France is Paris.",
         "logprobs": null,
         "finish_reason": "stop",
         "stop_reason": null,
         "token_ids": null,
         "prompt_logprobs": null,
         "prompt_token_ids": null
       }
     ],
     "service_tier": null,
     "system_fingerprint": null,
     "usage": {
       "prompt_tokens": 7,
       "total_tokens": 18,
       "completion_tokens": 11,
       "prompt_tokens_details": null
     },
     "kv_transfer_params": null
   }