diff --git a/docs/source/basic_tutorials/consuming_tgi.md b/docs/source/basic_tutorials/consuming_tgi.md
index 619e0a31..7fb74719 100644
--- a/docs/source/basic_tutorials/consuming_tgi.md
+++ b/docs/source/basic_tutorials/consuming_tgi.md
@@ -6,7 +6,7 @@ There are many ways you can consume Text Generation Inference server in your app
 
 After the launch, you can query the model using either the `/generate` or `/generate_stream` routes:
 
-```shell
+```bash
 curl 127.0.0.1:8080/generate \
     -X POST \
     -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":20}}' \
@@ -20,14 +20,13 @@ curl 127.0.0.1:8080/generate \
 
 You can simply install `huggingface-hub` package with pip.
 
-```python
+```bash
 pip install huggingface-hub
 ```
 
 Once you start the TGI server, instantiate `InferenceClient()` with the URL to the endpoint serving the model. You can then call `text_generation()` to hit the endpoint through Python. 
 
 ```python
-
 from huggingface_hub import InferenceClient
 
 client = InferenceClient(model=URL_TO_ENDPOINT_SERVING_TGI)
diff --git a/docs/source/basic_tutorials/gated_model_access.md b/docs/source/basic_tutorials/gated_model_access.md
index f5858dc4..5d8e98f4 100644
--- a/docs/source/basic_tutorials/gated_model_access.md
+++ b/docs/source/basic_tutorials/gated_model_access.md
@@ -2,4 +2,23 @@
 
 If the model you wish to serve is behind gated access or the model repository on Hugging Face Hub is private, and you have access to the model, you can provide your Hugging Face Hub access token. You can generate and copy a read token from [Hugging Face Hub tokens page](https://huggingface.co/settings/tokens)
 
-If you're using the CLI, set the `HUGGING_FACE_HUB_TOKEN` environment variable.
+If you're using the CLI, set the `HUGGING_FACE_HUB_TOKEN` environment variable. For example:
+
+```
+export HUGGING_FACE_HUB_TOKEN=<YOUR READ TOKEN>
+```
+
+If you would like to do it through Docker, you can provide your token by specifying `HUGGING_FACE_HUB_TOKEN` as shown below.
+
+```bash
+model=meta-llama/Llama-2-7b-chat-hf
+volume=$PWD/data
+token=<your READ token>
+
+docker run --gpus all \
+    --shm-size 1g \
+    -e HUGGING_FACE_HUB_TOKEN=$token \
+    -p 8080:80 \
+    -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.0.0 \
+    --model-id $model
+```
\ No newline at end of file
diff --git a/docs/source/installation.md b/docs/source/installation.md
index 1301b930..d870dcea 100644
--- a/docs/source/installation.md
+++ b/docs/source/installation.md
@@ -77,3 +77,9 @@ make run-falcon-7b-instruct
 ```
 
 This will serve Falcon 7B Instruct model from the port 8080, which we can query.
+
+To see all options to serve your models, check in the [codebase](https://github.com/huggingface/text-generation-inference/blob/main/launcher/src/main.rs) or the CLI:
+
+```bash
+text-generation-launcher --help
+```