diff --git a/extensions/openai/embeddings.py b/extensions/openai/embeddings.py
index 88ab1c30..a5b52d7b 100644
--- a/extensions/openai/embeddings.py
+++ b/extensions/openai/embeddings.py
@@ -3,7 +3,9 @@ import os
 import numpy as np
 from extensions.openai.errors import ServiceUnavailableError
 from extensions.openai.utils import debug_msg, float_list_to_base64
-from sentence_transformers import SentenceTransformer
+from transformers import AutoModel
+
+from modules import shared
 
 embeddings_params_initialized = False
 
@@ -26,21 +28,23 @@ def initialize_embedding_params():
         embeddings_params_initialized = True
 
 
-def load_embedding_model(model: str) -> SentenceTransformer:
+def load_embedding_model(model: str):
     initialize_embedding_params()
     global embeddings_device, embeddings_model
     try:
         print(f"Try embedding model: {model} on {embeddings_device}")
-        # see: https://www.sbert.net/docs/package_reference/SentenceTransformer.html#sentence_transformers.SentenceTransformer
-        embeddings_model = SentenceTransformer(model, device=embeddings_device)
-        # ... embeddings_model.device doesn't seem to work, always cpu anyways? but specify cpu anyways to free more VRAM
-        print(f"\nLoaded embedding model: {model} on {embeddings_model.device} [always seems to say 'cpu', even if 'cuda'], max sequence length: {embeddings_model.max_seq_length}")
+        trust = shared.args.trust_remote_code
+        if embeddings_device == 'cpu':
+            embeddings_model = AutoModel.from_pretrained(model, trust_remote_code=trust).to("cpu", dtype=float)
+        else: #use the auto mode
+            embeddings_model = AutoModel.from_pretrained(model, trust_remote_code=trust)
+        print(f"\nLoaded embedding model: {model} on {embeddings_model.device}")
     except Exception as e:
         embeddings_model = None
         raise ServiceUnavailableError(f"Error: Failed to load embedding model: {model}", internal_message=repr(e))
 
 
-def get_embeddings_model() -> SentenceTransformer:
+def get_embeddings_model() -> AutoModel:
     initialize_embedding_params()
     global embeddings_model, st_model
     if st_model and not embeddings_model: