diff --git a/InstructorEmbedding/instructor.py b/InstructorEmbedding/instructor.py
index 72b3df2..7cfdaba 100644
--- a/InstructorEmbedding/instructor.py
+++ b/InstructorEmbedding/instructor.py
@@ -3,12 +3,13 @@
 import json
 import os
 from collections import OrderedDict
-from typing import Union
+from typing import Literal, Union
 
 import numpy as np
 import torch
 from sentence_transformers import SentenceTransformer
 from sentence_transformers.models import Transformer
+from sentence_transformers.util import load_file_path, load_dir_path
 from torch import Tensor, nn
 from tqdm.autonotebook import trange
 from transformers import AutoConfig, AutoTokenizer
@@ -278,6 +279,7 @@ def __init__(
         max_seq_length=None,
         model_args=None,
         cache_dir=None,
+        backend: Literal['torch', 'onnx', 'openvino'] = 'torch',
         tokenizer_args=None,
         do_lower_case: bool = False,
         tokenizer_name_or_path: Union[str, None] = None,
@@ -307,7 +309,7 @@ def __init__(
             )
 
         if load_model:
-            self._load_model(self.model_name_or_path, config, cache_dir, **model_args)
+            self._load_model(self.model_name_or_path, config, cache_dir, **model_args, backend=backend)
         self.tokenizer = AutoTokenizer.from_pretrained(
             tokenizer_name_or_path
             if tokenizer_name_or_path is not None
@@ -517,7 +519,9 @@ def smart_batching_collate(self, batch):
 
         return batched_input_features, labels
 
-    def _load_sbert_model(self, model_path, token=None, cache_folder=None, revision=None, trust_remote_code=False):
+    def _load_sbert_model(self, model_path, token=None, cache_folder=None, revision=None,
+                          trust_remote_code=None, local_files_only=None, model_kwargs=None,
+                          tokenizer_kwargs=None, config_kwargs=None):
         """
         Loads a full sentence-transformers model
         """
@@ -527,15 +531,15 @@ def _load_sbert_model(self, model_path, token=None, cache_folder=None, revision=
         if os.path.isdir(model_path):
             model_path = str(model_path)
         else:
-            # If model_path is a Hugging Face repository ID, download the model
-            download_kwargs = {
-                "repo_id": model_path,
-                "revision": revision,
-                "library_name": "InstructorEmbedding",
-                "token": token,
-                "cache_dir": cache_folder,
-                "tqdm_class": disabled_tqdm,
-            }
+            model_path = load_dir_path(
+                model_path,
+                directory="*",
+                token=token,
+                cache_folder=cache_folder,
+                revision=revision,
+                local_files_only=local_files_only,
+            )
+            model_path = model_path[:model_path.index("*")]
 
         # Check if the config_sentence_transformers.json file exists (exists since v2 of the framework)
         config_sentence_transformers_json_path = os.path.join(
@@ -562,17 +566,19 @@ def _load_sbert_model(self, model_path, token=None, cache_folder=None, revision=
             modules_config = json.load(config_file)
 
         modules = OrderedDict()
+        module_kwargs = OrderedDict()
         for module_config in modules_config:
             if module_config["idx"] == 0:
                 module_class = INSTRUCTORTransformer
             elif module_config["idx"] == 1:
                 module_class = INSTRUCTORPooling
             else:
-                module_class = import_from_string(module_config["type"])
-            module = module_class.load(os.path.join(model_path, module_config["path"]))
-            modules[module_config["name"]] = module
+                module_class = import_from_string(module_config['type'])
+            module = module_class.load(os.path.join(model_path, module_config['path']))
+            modules[module_config['name']] = module
+            module_kwargs[module_config['name']] = module
 
-        return modules
+        return modules, module_kwargs
 
     def encode(
         self,
diff --git a/setup.py b/setup.py
index 6f17fb0..ac4dd1b 100644
--- a/setup.py
+++ b/setup.py
@@ -6,7 +6,7 @@
 setup(
     name='InstructorEmbedding',
     packages=['InstructorEmbedding'],
-    version='1.0.2',
+    version='1.0.3',
     license='Apache License 2.0',
     description='Text embedding tool',
     long_description=readme,