diff --git a/examples/vllm/readme.md b/examples/vllm/readme.md
index f85c645..3670a1c 100644
--- a/examples/vllm/readme.md
+++ b/examples/vllm/readme.md
@@ -4,29 +4,18 @@
 
 1. Create serving Service: `clearml-serving create --name "serving example"` (write down the service ID)
 
-2. Make sure to add any required additional packages (for your custom model) to the [docker-compose.yml](https://github.com/allegroai/clearml-serving/blob/826f503cf4a9b069b89eb053696d218d1ce26f47/docker/docker-compose.yml#L97) (or as environment variable to the `clearml-serving-inference` container), by defining for example: `CLEARML_EXTRA_PYTHON_PACKAGES="vllm==0.7.3,prometheus_client==0.21.1"`
+2. Add vLLM engine parameters in `VLLM_ENGINE_ARGS` variable as it was done in [this file](/docker/docker-compose-gpu.yml#L108). Make sure to add any required additional packages (for your custom model) to the [requirements.txt](/clearml_serving/serving/requirements.txt) or [docker-compose.yml](https://github.com/allegroai/clearml-serving/blob/826f503cf4a9b069b89eb053696d218d1ce26f47/docker/docker-compose.yml#L97) (or as environment variable to the `clearml-serving-inference` container), by defining for example: `CLEARML_EXTRA_PYTHON_PACKAGES="vllm==0.7.3 prometheus_client==0.21.1"`
+
 3. Create model endpoint: 
     ```
-    clearml-serving --id <service_id> model add --model-id <model_id> --engine vllm --endpoint "test_vllm" --preprocess "examples/vllm/preprocess.py" --name "test vllm" --project "serving examples"
-    ```
-
-    Or auto update 
-
-    ```
-    clearml-serving --id <service_id> model auto-update --engine vllm --endpoint "test_vllm" --preprocess "examples/vllm/preprocess.py" --name "test vllm" --project "serving examples" --max-versions 2
-    ```
-
-    Or add Canary endpoint
-
-    ```
-    clearml-serving --id <service_id> model canary --endpoint "test_vllm" --weights 0.1 0.9 --input-endpoint-prefix test_vllm
+    clearml-serving --id <service_id> model add --model-id <model_id> --engine vllm --endpoint "test_vllm" --preprocess "examples/vllm/preprocess.py"
     ```
 
 4. If you already have the `clearml-serving` docker-compose running, it might take it a minute or two to sync with the new endpoint.
 
     Or you can run the clearml-serving container independently:
     ```
-    docker run -v ~/clearml.conf:/root/clearml.conf -p 8080:8080 -e CLEARML_SERVING_TASK_ID=<service_id> clearml-serving:latest
+    docker run -v ~/clearml.conf:/root/clearml.conf -p 8080:8080 -e CLEARML_SERVING_TASK_ID=<service_id> clearml-serving-inference:latest
     ```
 
 5. Test new endpoint (do notice the first call will trigger the model pulling, so it might take longer, from here on, it's all in memory):