diff --git a/language/t5.py b/language/t5.py
index 9a548cd..7632d66 100644
--- a/language/t5.py
+++ b/language/t5.py
@@ -12,49 +12,43 @@
 from huggingface_hub import hf_hub_download
 
 
+import os
+import re
+import torch
+from transformers import AutoTokenizer, T5EncoderModel
+
+import os
+import re
+import torch
+from transformers import AutoTokenizer, T5EncoderModel
+
 class T5Embedder:
     available_models = ['t5-v1_1-xxl', 't5-v1_1-xl', 'flan-t5-xl']
-    bad_punct_regex = re.compile(r'['+'#®•©™&@·º½¾¿¡§~'+'\)'+'\('+'\]'+'\['+'\}'+'\{'+'\|'+'\\'+'\/'+'\*' + r']{1,}')  # noqa
-
-    def __init__(self, device, dir_or_name='t5-v1_1-xxl', *, local_cache=False, cache_dir=None, hf_token=None, use_text_preprocessing=True,
-                 t5_model_kwargs=None, torch_dtype=None, use_offload_folder=None, model_max_length=120):
+    bad_punct_regex = re.compile(r'['+'#®•©™&@·º½¾¿¡§~'+'\)'+'\('+'\]'+'\['+'\}'+'\{'+'\|'+'\\'+'\/'+'\*' + r']{1,}')
+
+    def __init__(
+        self,
+        device,
+        model_path,
+        *,
+        torch_dtype=None,
+        model_max_length=120
+    ):
         self.device = torch.device(device)
         self.torch_dtype = torch_dtype or torch.bfloat16
-        if t5_model_kwargs is None:
-            t5_model_kwargs = {'low_cpu_mem_usage': True, 'torch_dtype': self.torch_dtype}
-            t5_model_kwargs['device_map'] = {'shared': self.device, 'encoder': self.device}
-
-        self.use_text_preprocessing = use_text_preprocessing
-        self.hf_token = hf_token
-        self.cache_dir = cache_dir or os.path.expanduser('~/.cache/IF_')
-        self.dir_or_name = dir_or_name
-        tokenizer_path, path = dir_or_name, dir_or_name
-        if local_cache:
-            cache_dir = os.path.join(self.cache_dir, dir_or_name)
-            tokenizer_path, path = cache_dir, cache_dir
-        elif dir_or_name in self.available_models:
-            cache_dir = os.path.join(self.cache_dir, dir_or_name)
-            for filename in [
-                'config.json', 'special_tokens_map.json', 'spiece.model', 'tokenizer_config.json',
-                'pytorch_model.bin.index.json', 'pytorch_model-00001-of-00002.bin', 'pytorch_model-00002-of-00002.bin'
-            ]:
-                hf_hub_download(repo_id=f'DeepFloyd/{dir_or_name}', filename=filename, cache_dir=cache_dir,
-                                force_filename=filename, token=self.hf_token)
-            tokenizer_path, path = cache_dir, cache_dir
-        else:
-            cache_dir = os.path.join(self.cache_dir, 't5-v1_1-xxl')
-            for filename in [
-                'config.json', 'special_tokens_map.json', 'spiece.model', 'tokenizer_config.json',
-            ]:
-                hf_hub_download(repo_id='DeepFloyd/t5-v1_1-xxl', filename=filename, cache_dir=cache_dir,
-                                force_filename=filename, token=self.hf_token)
-            tokenizer_path = cache_dir
-
-        print(tokenizer_path)
-        self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
-        self.model = T5EncoderModel.from_pretrained(path, **t5_model_kwargs).eval()
         self.model_max_length = model_max_length
 
+        tokenizer_path = os.path.abspath(model_path)
+        model_path = tokenizer_path
+
+        self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path, local_files_only=True)
+        self.model = T5EncoderModel.from_pretrained(model_path, local_files_only=True, 
+                                                    low_cpu_mem_usage=True,
+                                                    torch_dtype=self.torch_dtype,
+                                                    device_map={'shared': self.device, 'encoder': self.device}).eval()
+
+
+
     def get_text_embeddings(self, texts):
         texts = [self.text_preprocessing(text) for text in texts]
 
@@ -198,4 +192,4 @@ def clean_caption(self, caption):
         caption = re.sub(r'[\'\_,\-\:\-\+]$', r'', caption)
         caption = re.sub(r'^\.\S+$', '', caption)
 
-        return caption.strip()
\ No newline at end of file
+        return caption.strip()