diff --git a/modules/GPTQ_loader.py b/modules/GPTQ_loader.py
index 8142c34e..87a4f524 100644
--- a/modules/GPTQ_loader.py
+++ b/modules/GPTQ_loader.py
@@ -35,7 +35,8 @@ except ImportError:
 
 # This function is a replacement for the load_quant function in the
 # GPTQ-for_LLaMa repository. It supports more models and branches.
-def _load_quant(model, checkpoint, wbits, groupsize=-1, faster_kernel=False, exclude_layers=['lm_head'], kernel_switch_threshold=128, eval=True):
+def _load_quant(model, checkpoint, wbits, groupsize=-1, faster_kernel=False, exclude_layers=None, kernel_switch_threshold=128, eval=True):
+    exclude_layers = exclude_layers or ['lm_head']
 
     def noop(*args, **kwargs):
         pass
diff --git a/modules/RWKV.py b/modules/RWKV.py
index 0405230e..957bc004 100644
--- a/modules/RWKV.py
+++ b/modules/RWKV.py
@@ -34,15 +34,15 @@ class RWKVModel:
         result.pipeline = pipeline
         return result
 
-    def generate(self, context="", token_count=20, temperature=1, top_p=1, top_k=50, repetition_penalty=None, alpha_frequency=0.1, alpha_presence=0.1, token_ban=[0], token_stop=[], callback=None):
+    def generate(self, context="", token_count=20, temperature=1, top_p=1, top_k=50, repetition_penalty=None, alpha_frequency=0.1, alpha_presence=0.1, token_ban=None, token_stop=None, callback=None):
         args = PIPELINE_ARGS(
             temperature=temperature,
             top_p=top_p,
             top_k=top_k,
             alpha_frequency=alpha_frequency,  # Frequency Penalty (as in GPT-3)
             alpha_presence=alpha_presence,  # Presence Penalty (as in GPT-3)
-            token_ban=token_ban,  # ban the generation of some tokens
-            token_stop=token_stop
+            token_ban=token_ban or [0],  # ban the generation of some tokens
+            token_stop=token_stop or []
         )
 
         return self.pipeline.generate(context, token_count=token_count, args=args, callback=callback)
diff --git a/modules/callbacks.py b/modules/callbacks.py
index fb87ad56..5996ba4e 100644
--- a/modules/callbacks.py
+++ b/modules/callbacks.py
@@ -55,12 +55,12 @@ class Iteratorize:
     Adapted from: https://stackoverflow.com/a/9969000
     """
 
-    def __init__(self, func, kwargs={}, callback=None):
+    def __init__(self, func, kwargs=None, callback=None):
         self.mfunc = func
         self.c_callback = callback
         self.q = Queue()
         self.sentinel = object()
-        self.kwargs = kwargs
+        self.kwargs = kwargs or {}
         self.stop_now = False
 
         def _callback(val):
diff --git a/modules/text_generation.py b/modules/text_generation.py
index ba3f16b9..8a980425 100644
--- a/modules/text_generation.py
+++ b/modules/text_generation.py
@@ -142,7 +142,7 @@ def stop_everything_event():
     shared.stop_everything = True
 
 
-def generate_reply(question, state, eos_token=None, stopping_strings=[]):
+def generate_reply(question, state, eos_token=None, stopping_strings=None):
     state = apply_extensions('state', state)
     generate_func = apply_extensions('custom_generate_reply')
     if generate_func is None:
@@ -173,7 +173,7 @@ def generate_reply(question, state, eos_token=None, stopping_strings=[]):
         yield formatted_outputs(reply, shared.model_name)
 
 
-def generate_reply_HF(question, original_question, seed, state, eos_token=None, stopping_strings=[]):
+def generate_reply_HF(question, original_question, seed, state, eos_token=None, stopping_strings=None):
     generate_params = {}
     for k in ['max_new_tokens', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping']:
         generate_params[k] = state[k]
@@ -272,7 +272,7 @@ def generate_reply_HF(question, original_question, seed, state, eos_token=None,
         return
 
 
-def generate_reply_custom(question, original_question, seed, state, eos_token=None, stopping_strings=[]):
+def generate_reply_custom(question, original_question, seed, state, eos_token=None, stopping_strings=None):
     seed = set_manual_seed(state['seed'])
     generate_params = {'token_count': state['max_new_tokens']}
     for k in ['temperature', 'top_p', 'top_k', 'repetition_penalty']:
@@ -309,7 +309,7 @@ def generate_reply_custom(question, original_question, seed, state, eos_token=No
         return
 
 
-def generate_reply_flexgen(question, original_question, seed, state, eos_token=None, stopping_strings=[]):
+def generate_reply_flexgen(question, original_question, seed, state, eos_token=None, stopping_strings=None):
     generate_params = {}
     for k in ['max_new_tokens', 'do_sample', 'temperature']:
         generate_params[k] = state[k]