diff --git a/pinecone_text/sparse/splade_encoder.py b/pinecone_text/sparse/splade_encoder.py
index 240a9df..f5225ee 100644
--- a/pinecone_text/sparse/splade_encoder.py
+++ b/pinecone_text/sparse/splade_encoder.py
@@ -1,5 +1,7 @@
 from typing import List, Union, Optional
-
+from os import PathLike
+import os
+import json
 try:
     import torch
 except (OSError, ImportError, ModuleNotFoundError) as e:
@@ -26,11 +28,12 @@ class SpladeEncoder(BaseSparseEncoder):
     Currently only supports inference with  naver/splade-cocondenser-ensembledistil
     """
 
-    def __init__(self, max_seq_length: int = 256, device: Optional[str] = None):
+    def __init__(self, max_seq_length: int = 256, device: Optional[str] = None, model_dir:Optional[PathLike[str]] = None):
         """
         Args:
             max_seq_length: Maximum sequence length for the model. Must be between 1 and 512.
             device: Device to use for inference. Defaults to GPU if available, otherwise CPU.
+            model_dir: Directory to download and load model from. Saves time and resources.
 
         Example:
 
@@ -61,12 +64,38 @@ def __init__(self, max_seq_length: int = 256, device: Optional[str] = None):
 
         device = device or ("cuda" if torch.cuda.is_available() else "cpu")
         self.device = device
-
-        model = "naver/splade-cocondenser-ensembledistil"
-        self.tokenizer = AutoTokenizer.from_pretrained(model)
-        self.model = AutoModelForMaskedLM.from_pretrained(model).to(self.device)
+        expected_model_name = "naver/splade-cocondenser-ensembledistil"
+        if model_dir:
+            if not self._is_correct_model(model_dir, expected_model_name):
+                self.tokenizer,self.model=self._download_model(model_dir, expected_model_name)
+            else:
+                self.tokenizer = AutoTokenizer.from_pretrained(model_dir)
+                self.model = AutoModelForMaskedLM.from_pretrained(model_dir).to(self.device)
+        else:
+            self.tokenizer = AutoTokenizer.from_pretrained(expected_model_name)
+            self.model = AutoModelForMaskedLM.from_pretrained(expected_model_name).to(self.device)
         self.max_seq_length = max_seq_length
-
+    def _is_correct_model(self, model_dir, expected_model_name):
+        # Check for the presence of specific files that indicate the correct model
+        config_path = os.path.join(model_dir, 'config.json')
+        if not os.path.exists(config_path):
+            return False
+
+        with open(config_path, 'r') as config_file:
+            config = json.load(config_file)
+            return config.get("_name_or_path") == expected_model_name
+
+    def _download_model(self, model_dir, model_name):
+        # Ensure the directory exists
+        os.makedirs(model_dir, exist_ok=True)
+
+        # Download the tokenizer and model
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        tokenizer.save_pretrained(model_dir)
+
+        model = AutoModelForMaskedLM.from_pretrained(model_name)
+        model.save_pretrained(model_dir)
+        return tokenizer,model
     def encode_documents(
         self, texts: Union[str, List[str]]
     ) -> Union[SparseVector, List[SparseVector]]: