diff --git a/modules/logits.py b/modules/logits.py
index 4233c8a5..73cabb41 100644
--- a/modules/logits.py
+++ b/modules/logits.py
@@ -13,8 +13,8 @@ global_scores = None
 
 
 def get_next_logits(*args, **kwargs):
-    if shared.args.idle_timeout > 0 and shared.model is None and shared.previous_model_name not in [None, 'None']:
-        shared.model, shared.tokenizer = load_model(shared.previous_model_name)
+    if shared.args.idle_timeout > 0 and shared.model is None and shared.model_name not in [None, 'None']:
+        shared.model, shared.tokenizer = load_model(shared.model_name)
 
     needs_lock = not args[2]  # use_samplers
     if needs_lock:
diff --git a/modules/models.py b/modules/models.py
index ff990ae6..ecef9060 100644
--- a/modules/models.py
+++ b/modules/models.py
@@ -370,7 +370,6 @@ def clear_torch_cache():
 
 def unload_model(keep_model_name=False):
     shared.model = shared.tokenizer = None
-    shared.previous_model_name = shared.model_name
     shared.lora_names = []
     shared.model_dirty_from_training = False
     clear_torch_cache()
diff --git a/modules/shared.py b/modules/shared.py
index 2aebb7c6..c27657ff 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -13,7 +13,6 @@ from modules.logging_colors import logger
 model = None
 tokenizer = None
 model_name = 'None'
-previous_model_name = 'None'
 is_seq2seq = False
 model_dirty_from_training = False
 lora_names = []
diff --git a/modules/text_generation.py b/modules/text_generation.py
index d971a30e..75e5ef36 100644
--- a/modules/text_generation.py
+++ b/modules/text_generation.py
@@ -32,8 +32,8 @@ from modules.models import clear_torch_cache, load_model
 
 
 def generate_reply(*args, **kwargs):
-    if shared.args.idle_timeout > 0 and shared.model is None and shared.previous_model_name not in [None, 'None']:
-        shared.model, shared.tokenizer = load_model(shared.previous_model_name)
+    if shared.args.idle_timeout > 0 and shared.model is None and shared.model_name not in [None, 'None']:
+        shared.model, shared.tokenizer = load_model(shared.model_name)
 
     shared.generation_lock.acquire()
     try: