diff --git a/src/app/(docs)/models/deployment/local-deployment/vllm/page.mdx b/src/app/(docs)/models/deployment/local-deployment/vllm/page.mdx
index 67a18b53..73b6048b 100644
--- a/src/app/(docs)/models/deployment/local-deployment/vllm/page.mdx
+++ b/src/app/(docs)/models/deployment/local-deployment/vllm/page.mdx
@@ -32,7 +32,7 @@ The following sections will guide you through the process of deploying and query
 - Create a Python virtual environment and install the `vllm` package (version `>=0.6.1.post1` to ensure maximum compatibility with all Mistral models).
 - Authenticate on the HuggingFace Hub using your access token `$HF_TOKEN`:
   ```bash
-  huggingface-cli login --token $HF_TOKEN
+  hf auth login --token $HF_TOKEN
   ```
 
 <SectionTab as="h2" variant="secondary" sectionId="offline-mode-inference">Offline Mode Inference</SectionTab>
@@ -120,4 +120,4 @@ If you are looking to deploy vLLM as a containerized inference server, you can l
           ```
       </TabItem>
   </Tabs>
-Once the container is up and running, you will be able to run inference on your model using the same code as in a standalone deployment.
\ No newline at end of file
+Once the container is up and running, you will be able to run inference on your model using the same code as in a standalone deployment.