@@ -73,8 +73,8 @@ For more information on how to do it go to [Getting Started](#getting-started)
     
 
 #### Plug and play recipes
-```python
-python -m super_gradients.examples.train_from_recipe_example.train_from_recipe architecture=regnetY800 dataset_interface.data_dir=<YOUR_Imagenet_LOCAL_PATH> ckpt_root_dir=<CHEKPOINT_DIRECTORY>
+```bash
+python -m super_gradients.train_from_recipe architecture=regnetY800 dataset_interface.data_dir=<YOUR_Imagenet_LOCAL_PATH> ckpt_root_dir=<CHEKPOINT_DIRECTORY>
 ```
 More example on how and why to use recipes can be found in [Recipes](#recipes)
 
@@ -158,7 +158,7 @@ The most simple and straightforward way to start training SOTA performance model
 Just make sure that you [setup your dataset](https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/datasets/Dataset_Setup_Instructions.md) according to the data dir specified in the recipe.
 
 ```bash
-python -m super_gradients.examples.train_from_recipe_example.train_from_recipe --config-name=imagenet_regnetY architecture=regnetY800 dataset_interface.data_dir=<YOUR_Imagenet_LOCAL_PATH> ckpt_root_dir=<CHEKPOINT_DIRECTORY>
+python -m super_gradients.train_from_recipe --config-name=imagenet_regnetY architecture=regnetY800 dataset_interface.data_dir=<YOUR_Imagenet_LOCAL_PATH> ckpt_root_dir=<CHEKPOINT_DIRECTORY>
 ```
 ### Quickly Load Pre-Trained Weights for Your Desired Model with SOTA Performance
 Want to try our pre-trained models on your machine? Import SuperGradients, initialize your Trainer, and load your desired architecture and pre-trained weights from our [SOTA model zoo](http://bit.ly/41dkt89)