diff --git a/docs/source/basic_tutorials/non_core_models.md b/docs/source/basic_tutorials/non_core_models.md
index 324dc930..b60c5c3d 100644
--- a/docs/source/basic_tutorials/non_core_models.md
+++ b/docs/source/basic_tutorials/non_core_models.md
@@ -2,7 +2,7 @@
 
 TGI supports various LLM architectures (see full list [here](../supported_models)). If you wish to serve a model that is not one of the supported models, TGI will fallback to the `transformers` implementation of that model. This means you will be unable to use some of the features introduced by TGI, such as tensor-parallel sharding or flash attention. However, you can still get many benefits of TGI, such as continuous batching or streaming outputs.
 
-You can serve these models using Docker like below 👇 
+You can serve these models using the same Docker command-line invocation as with fully supported models 👇 
 
 ```bash
 docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:latest --model-id gpt2