diff --git a/src/xlora/xlora.py b/src/xlora/xlora.py
index c9cc63d..97ffc71 100644
--- a/src/xlora/xlora.py
+++ b/src/xlora/xlora.py
@@ -1,4 +1,5 @@
 import json
+import os
 from typing import Dict, List, Optional, Union
 
 import peft
@@ -230,6 +231,7 @@ def from_pretrained(
     device: str,
     verbose: bool = False,
     from_safetensors: bool = True,
+    hf_hub_subdir: Optional[str] = None,
 ) -> xLoRAModel:
     """
     Loads a pretrained classifier and potentially adapters from the specified folder while initializing the model. This is the counterpart to `save_pretrained`.
@@ -252,12 +254,15 @@ def from_pretrained(
             Device of the model, used to load the classifier.
         from_safetensors (`bool`, *optional*, defaults to True):
             Whether to load the classifier weights from a .pt or .safetensors file.
+        hf_hub_subdir (`str`, *optional*, defaults to None):
+            If `xlora_path` is a HF model repo ID, specify a subdirectory where the weights may be found.
+
     Returns:
         model (`xLoRAModel`):
             The new model.
     """
 
-    with open(xlora_utils._get_file_path(load_directory, "xlora_config.json"), "r") as f:
+    with open(xlora_utils._get_file_path(load_directory, "xlora_config.json", hf_hub_subdir), "r") as f:
         conf = json.load(f)
         conf["device"] = torch.device(device)
 
@@ -267,7 +272,12 @@ def from_pretrained(
 
     if use_trainable_adapters:
         adapters_dict: Dict[str, str] = {
-            name: xlora_utils._get_file_path_dir(load_directory, name, "adapters") for name in adapters
+            name: xlora_utils._get_file_path(
+                load_directory,
+                name,
+                os.path.join(hf_hub_subdir, "adapters") if hf_hub_subdir is not None else "adapters",
+            )
+            for name in adapters
         }
     else:
         assert isinstance(adapters, dict)
@@ -278,11 +288,11 @@ def from_pretrained(
     if from_safetensors:
         state_dict = load_model(
             classifier,
-            xlora_utils._get_file_path(load_directory, "xlora_classifier.safetensors"),
+            xlora_utils._get_file_path(load_directory, "xlora_classifier.safetensors", hf_hub_subdir),
         )
         classifier.to(device)
     else:
-        state_dict = torch.load(xlora_utils._get_file_path(load_directory, "xlora_classifier.pt"))
+        state_dict = torch.load(xlora_utils._get_file_path(load_directory, "xlora_classifier.pt", hf_hub_subdir))
         classifier.load_state_dict(state_dict)  # type: ignore
 
     return model_peft
diff --git a/src/xlora/xlora_utils.py b/src/xlora/xlora_utils.py
index 77700bc..8c2c3b4 100644
--- a/src/xlora/xlora_utils.py
+++ b/src/xlora/xlora_utils.py
@@ -14,7 +14,7 @@
 from .xlora import from_pretrained, xLoRAModel  # type: ignore
 
 
-def _get_file_path(
+def _get_file_path_single(
     load_directory: str,
     name: str,
 ) -> str:
@@ -29,6 +29,12 @@ def _get_file_path_dir(load_directory: str, name: str, dir: str) -> str:
     return huggingface_hub.hf_hub_download(load_directory, filename=name, subfolder=dir)
 
 
+def _get_file_path(load_directory: str, name: str, dir: Optional[str]) -> str:
+    if dir is not None:
+        return _get_file_path_dir(load_directory, name, dir)
+    return _get_file_path_single(load_directory, name)
+
+
 def load_model(
     model_name: str,
     xlora_path: Optional[str],
@@ -39,6 +45,7 @@ def load_model(
     load_xlora: bool = True,
     verbose: bool = False,
     from_safetensors: bool = True,
+    hf_hub_subdir: Optional[str] = None,
 ) -> Tuple[Union[AutoModelForCausalLM, xLoRAModel], Union[PreTrainedTokenizer, PreTrainedTokenizerFast]]:
     """
     Convenience function to load a model, converting it to xLoRA if specified.
@@ -47,7 +54,7 @@ def load_model(
         model_name (`str`):
             AutoModelForCausalLM pretrained model name or path
         xlora_path (`str`, *optional*):
-            Directory to load the xLoRAClassifier from.
+            Directory or HF model repo ID to load the xLoRAClassifier from.
         device (`str`):
             Device to load the base model and the xLoRA model to.
         dtype (`torch.dtype`):
@@ -63,6 +70,8 @@ def load_model(
             Enable verbose loading.
         from_safetensors (`bool`, *optional*, defaults to True):
             Whether to load the classifier weights from a .pt or .safetensors file.
+        hf_hub_subdir (`str`, *optional*, defaults to None):
+            If `xlora_path` is a HF model repo ID, specify a subdirectory where the weights may be found.
 
     Returns:
         Tuple whose elements are respectively: