diff --git a/deepforest/main.py b/deepforest/main.py
index b0d0847f..f4a38ba6 100644
--- a/deepforest/main.py
+++ b/deepforest/main.py
@@ -21,8 +21,7 @@
 from huggingface_hub import PyTorchModelHubMixin
 
 
-class deepforest(
-        pl.LightningModule,):  # PyTorchModelHubMixin):
+class deepforest(pl.LightningModule, PyTorchModelHubMixin):
     """Class for training and predicting tree crowns in RGB images."""
 
     def __init__(self,
@@ -120,42 +119,30 @@ def __init__(self,
 
         self.save_hyperparameters()
 
-    def from_pretrained(self,
-                        repo_id: str,
-                        filename: str = "model.safetensors",
-                        config_file: str = "config.json"):
-        """Load a pre-trained deepforest model from the Hugging Face Hub.
+    def load_model(self, model_name="weecology/deepforest-tree", revision='main'):
+        """Load DeepForest models from Hugging Face using from_pretrained().
 
         Args:
-            repo_id (str): The Hugging Face repository where the model is stored.
-            filename (str): The model file to download.
-            config_file (str): The config file to download. Defaults to "config.json".
-        """
-        # Download model weights
-        model_file = hf_hub_download(repo_id=repo_id, filename=filename)
-
-        # Download and load the config file
-        downloaded_config_file = hf_hub_download(repo_id=repo_id, filename=config_file)
-        self.config = utilities.read_config(downloaded_config_file)
-
-        # Initialize the model class with the downloaded config
-        self.create_model()  # Initialize the model architecture based on the config
-
-        # Load model weights from the checkpoint
-        self.model.load_state_dict(
-            torch.load(model_file))  # Use the class method directly
+            model_name (str): A repository ID for huggingface in the form of organization/repository
+            version (str): The model version ('main', 'v1.0.0', etc.).
 
-    def load_model(self, repo_id: str, **kwargs):
-        """Wrapper method to load both the model and config file.
-
-        Args:
-            repo_id (str): The Hugging Face repository ID where the model is stored.
-            kwargs: Additional arguments to pass to from_pretrained method.
+        Returns:
+            self (object):A trained PyTorch model with its config and weights.
         """
-        # Call from_pretrained to load the model and config
-        self.from_pretrained(repo_id, **kwargs)
-
-        print(f"Model and configuration loaded from {repo_id}")
+        # Load the model using from_pretrained
+        loaded_model = self.from_pretrained(model_name, revision=revision)
+        self.config = loaded_model.config
+        self.label_dict = loaded_model.label_dict
+        self.model = loaded_model.model
+        self.numeric_to_label_dict = loaded_model.numeric_to_label_dict
+
+        # Set bird-specific settings if loading the bird model
+        if model_name == "weecology/deepforest-bird":
+            self.config['retinanet']["score_thresh"] = 0.3
+            self.label_dict = {"Bird": 0}
+            self.numeric_to_label_dict = {v: k for k, v in self.label_dict.items()}
+
+        return self
 
     def use_release(self, check_release=True):
         """Use the latest DeepForest model release from github and load model.