diff --git a/README.md b/README.md
index f5c90259..5ff08743 100644
--- a/README.md
+++ b/README.md
@@ -471,7 +471,7 @@ python ./examples/run_generation.py \
 
 Starting with `v2.2.2`, you can now upload and share your fine-tuned models with the community, using the <abbr title="Command-line interface">CLI</abbr> that's built-in to the library.
 
-**First, create an account on [https://huggingface.co/join](https://huggingface.co/join)**. Then:
+**First, create an account on [https://huggingface.co/join](https://huggingface.co/join)**. Optionally, join an existing organization or create a new one. Then:
 
 ```shell
 transformers-cli login
@@ -490,19 +490,24 @@ transformers-cli upload ./config.json [--filename folder/foobar.json]
 # (you can optionally override its filename, which can be nested inside a folder)
 ```
 
-Your model will then be accessible through its identifier, a concatenation of your username and the folder name above:
-```python
-"username/pretrained_model"
+If you want your model to be namespaced by your organization name rather than your username, add the following flag to any command:
+```shell
+--organization organization_name
 ```
 
-**Please add a README.md model card** to the repo under `model_cards/` with: model description, training params (dataset, preprocessing, hyperparameters), evaluation results, intended uses & limitations, etc.
+Your model will then be accessible through its identifier, a concatenation of your username (or organization name) and the folder name above:
+```python
+"namespace/pretrained_model"
+```
+
+**Please add a README.md model card** to the repo under `model_cards/` with: model description, training params (dataset, preprocessing, hardware used, hyperparameters), evaluation results, intended uses & limitations, etc.
 
 Your model now has a page on huggingface.co/models 🔥
 
 Anyone can load it from code:
 ```python
-tokenizer = AutoTokenizer.from_pretrained("username/pretrained_model")
-model = AutoModel.from_pretrained("username/pretrained_model")
+tokenizer = AutoTokenizer.from_pretrained("namespace/pretrained_model")
+model = AutoModel.from_pretrained("namespace/pretrained_model")
 ```
 
 List all your files on S3:
diff --git a/docs/source/model_sharing.md b/docs/source/model_sharing.md
index 4210ea21..6b29fa70 100644
--- a/docs/source/model_sharing.md
+++ b/docs/source/model_sharing.md
@@ -2,7 +2,7 @@
 
 Starting with `v2.2.2`, you can now upload and share your fine-tuned models with the community, using the <abbr title="Command-line interface">CLI</abbr> that's built-in to the library.
 
-**First, create an account on [https://huggingface.co/join](https://huggingface.co/join)**. Then:
+**First, create an account on [https://huggingface.co/join](https://huggingface.co/join)**. Optionally, join an existing organization or create a new one. Then:
 
 ```shell
 transformers-cli login
@@ -21,19 +21,24 @@ transformers-cli upload ./config.json [--filename folder/foobar.json]
 # (you can optionally override its filename, which can be nested inside a folder)
 ```
 
-Your model will then be accessible through its identifier, a concatenation of your username and the folder name above:
-```python
-"username/pretrained_model"
+If you want your model to be namespaced by your organization name rather than your username, add the following flag to any command:
+```shell
+--organization organization_name
 ```
 
-**Please add a README.md model card** to the repo under `model_cards/` with: model description, training params (dataset, preprocessing, hyperparameters), evaluation results, intended uses & limitations, etc.
+Your model will then be accessible through its identifier, a concatenation of your username (or organization name) and the folder name above:
+```python
+"namespace/pretrained_model"
+```
+
+**Please add a README.md model card** to the repo under `model_cards/` with: model description, training params (dataset, preprocessing, hardware used, hyperparameters), evaluation results, intended uses & limitations, etc.
 
 Your model now has a page on huggingface.co/models 🔥
 
 Anyone can load it from code:
 ```python
-tokenizer = AutoTokenizer.from_pretrained("username/pretrained_model")
-model = AutoModel.from_pretrained("username/pretrained_model")
+tokenizer = AutoTokenizer.from_pretrained("namespace/pretrained_model")
+model = AutoModel.from_pretrained("namespace/pretrained_model")
 ```
 
 List all your files on S3:
@@ -45,4 +50,4 @@ You can also delete unneeded files:
 
 ```shell
 transformers-cli s3 rm …
-```
\ No newline at end of file
+```