diff --git a/docs/guide/integrations.rst b/docs/guide/integrations.rst
index 97d197f..9007ade 100644
--- a/docs/guide/integrations.rst
+++ b/docs/guide/integrations.rst
@@ -43,7 +43,97 @@ The full documentation is available here: https://docs.wandb.ai/guides/integrati
   run.finish()
 
 
-Hugging Face
-============
+Hugging Face 🤗
+===============
+The Hugging Face Hub 🤗 is a central place where anyone can share and explore models. It allows you to host your saved models 💾.
 
-To be added.
+You can see the list of stable-baselines3 saved models here: https://huggingface.co/models?other=stable-baselines3
+
+Official pre-trained models are saved in the SB3 organization on the hub: https://huggingface.co/sb3
+
+We wrote a tutorial on how to use 🤗 Hub and Stable-Baselines3 here: https://colab.research.google.com/drive/1GI0WpThwRHbl-Fu2RHfczq6dci5GBDVE#scrollTo=q4cz-w9MdO7T
+
+Installation
+-------------
+
+.. code-block:: bash
+
+ pip install huggingface_hub
+ pip install huggingface_sb3
+
+
+Download a model from the Hub
+-----------------------------
+You need to copy the repo-id that contains your saved model.
+For instance ``sb3/demo-hf-CartPole-v1``:
+
+.. code-block:: python
+
+  import gym
+
+  from huggingface_sb3 import load_from_hub
+  from stable_baselines3 import PPO
+  from stable_baselines3.common.evaluation import evaluate_policy
+
+  # Retrieve the model from the hub
+  ## repo_id = id of the model repository from the Hugging Face Hub (repo_id = {organization}/{repo_name})
+  ## filename = name of the model zip file from the repository
+  checkpoint = load_from_hub(
+      repo_id="sb3/demo-hf-CartPole-v1",
+      filename="ppo-CartPole-v1",
+  )
+  model = PPO.load(checkpoint)
+
+  # Evaluate the agent and watch it
+  eval_env = gym.make("CartPole-v1")
+  mean_reward, std_reward = evaluate_policy(
+      model, eval_env, render=True, n_eval_episodes=5, deterministic=True, warn=False
+  )
+  print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
+
+
+
+Upload a model to the Hub
+-------------------------
+
+First, you need to be logged in to Hugging Face to upload a model:
+
+- If you're using Colab/Jupyter Notebooks:
+
+.. code-block:: python
+
+ from huggingface_hub import notebook_login
+ notebook_login()
+
+
+- Otheriwse:
+
+.. code-block:: bash
+
+ huggingface-cli login
+
+Then, in this example, we train a PPO agent to play CartPole-v1 and push it to a new repo ``sb3/demo-hf-CartPole-v1``
+
+.. code-block:: python
+
+  from huggingface_sb3 import push_to_hub
+  from stable_baselines3 import PPO
+
+  # Define a PPO model with MLP policy network
+  model = PPO("MlpPolicy", "CartPole-v1", verbose=1)
+
+  # Train it for 10000 timesteps
+  model.learn(total_timesteps=10_000)
+
+  # Save the model
+  model.save("ppo-CartPole-v1")
+
+  # Push this saved model to the hf repo
+  # If this repo does not exists it will be created
+  ## repo_id = id of the model repository from the Hugging Face Hub (repo_id = {organization}/{repo_name})
+  ## filename: the name of the file == "name" inside model.save("ppo-CartPole-v1")
+  push_to_hub(
+      repo_id="sb3/demo-hf-CartPole-v1",
+      filename="ppo-CartPole-v1",
+      commit_message="Added Cartpole-v1 model trained with PPO",
+  )
diff --git a/docs/misc/changelog.rst b/docs/misc/changelog.rst
index 9576a6c..15b35e0 100644
--- a/docs/misc/changelog.rst
+++ b/docs/misc/changelog.rst
@@ -4,6 +4,35 @@ Changelog
 ==========
 
 
+Release 1.4.1a0 (WIP)
+---------------------------
+
+
+Breaking Changes:
+^^^^^^^^^^^^^^^^^
+
+
+New Features:
+^^^^^^^^^^^^^
+
+SB3-Contrib
+^^^^^^^^^^^
+
+Bug Fixes:
+^^^^^^^^^^
+- Fixed a bug in ``VecMonitor``. The monitor did not consider the ``info_keywords`` during stepping (@ScheiklP)
+
+Deprecations:
+^^^^^^^^^^^^^
+
+Others:
+^^^^^^^
+
+Documentation:
+^^^^^^^^^^^^^^
+- Added doc on Hugging Face integration (@simoninithomas)
+
+
 Release 1.4.0 (2022-01-18)
 ---------------------------
 
@@ -48,7 +77,6 @@ Bug Fixes:
 - Fixed a bug where the observation would be incorrectly detected as non-vectorized instead of throwing an error
 - The env checker now properly checks and warns about potential issues for continuous action spaces when the boundaries are too small or when the dtype is not float32
 - Fixed a bug in ``VecFrameStack`` with channel first image envs, where the terminal observation would be wrongly created.
-- Fixed a bug in ``VecMonitor``. The monitor did not consider the ``info_keywords`` during stepping (@ScheiklP) 
 
 Deprecations:
 ^^^^^^^^^^^^^
@@ -881,3 +909,4 @@ And all the contributors:
 @benblack769 @bstee615 @c-rizz @skandermoalla @MihaiAnca13 @davidblom603 @ayeright @cyprienc
 @wkirgsn @AechPro @CUN-bjy @batu @IljaAvadiev @timokau @kachayev @cleversonahum
 @eleurent @ac-93 @cove9988 @theDebugger811 @hsuehch @Demetrio92 @thomasgubler @IperGiove @ScheiklP
+@simoninithomas
diff --git a/stable_baselines3/version.txt b/stable_baselines3/version.txt
index 88c5fb8..97ec5cc 100644
--- a/stable_baselines3/version.txt
+++ b/stable_baselines3/version.txt
@@ -1 +1 @@
-1.4.0
+1.4.1a0