diff --git a/vllm/model_executor/models/gemma.py b/vllm/model_executor/models/gemma.py
index fddaa29a93091..029178af61da0 100644
--- a/vllm/model_executor/models/gemma.py
+++ b/vllm/model_executor/models/gemma.py
@@ -350,7 +350,6 @@ class GemmaForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
         "gate_up_proj",
         "down_proj",
     ]
-
     # BitandBytes specific attributes
     default_bitsandbytes_target_modules = [
         ".gate_proj.",
@@ -361,7 +360,6 @@ class GemmaForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
         ".v_proj.",
         ".o_proj.",
     ]
-
     bitsandbytes_stacked_params_mapping = {
         # shard_name, weight_name, index
         "q_proj": ("qkv_proj", 0),
diff --git a/vllm/model_executor/models/gemma2.py b/vllm/model_executor/models/gemma2.py
index 6661ae91195c9..9238ed839c9de 100644
--- a/vllm/model_executor/models/gemma2.py
+++ b/vllm/model_executor/models/gemma2.py
@@ -390,7 +390,6 @@ class Gemma2ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
         ".v_proj.",
         ".o_proj.",
     ]
-
     bitsandbytes_stacked_params_mapping = {
         # shard_name, weight_name, index
         "q_proj": ("qkv_proj", 0),
diff --git a/vllm/model_executor/models/llama.py b/vllm/model_executor/models/llama.py
index 586de6d44950c..38a31f420cec9 100644
--- a/vllm/model_executor/models/llama.py
+++ b/vllm/model_executor/models/llama.py
@@ -464,7 +464,6 @@ class LlamaForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
         ".v_proj.",
         ".o_proj.",
     ]
-
     bitsandbytes_stacked_params_mapping = {
         # shard_name, weight_name, index
         "q_proj": ("qkv_proj", 0),
diff --git a/vllm/model_executor/models/minicpmv.py b/vllm/model_executor/models/minicpmv.py
index 4ac11104a058b..f90df6b7df036 100644
--- a/vllm/model_executor/models/minicpmv.py
+++ b/vllm/model_executor/models/minicpmv.py
@@ -854,7 +854,6 @@ class MiniCPMV2_5(MiniCPMVBaseModel, SupportsLoRA):
         # resampler
         ".kv_proj.",
     ]
-
     bitsandbytes_stacked_params_mapping = {
         # shard_name, weight_name, index
         "q_proj": ("qkv_proj", 0),
@@ -1005,7 +1004,6 @@ class MiniCPMV2_6(MiniCPMVBaseModel, SupportsLoRA):
         # resampler
         ".kv_proj.",
     ]
-
     bitsandbytes_stacked_params_mapping = {
         # shard_name, weight_name, index
         "q_proj": ("qkv_proj", 0),
diff --git a/vllm/model_executor/models/mllama.py b/vllm/model_executor/models/mllama.py
index 0eae08732ae2d..d30b9addd09f1 100644
--- a/vllm/model_executor/models/mllama.py
+++ b/vllm/model_executor/models/mllama.py
@@ -1062,7 +1062,6 @@ class MllamaForConditionalGeneration(nn.Module, SupportsMultiModal):
         # so we can't add a dot in front of it.
         "multi_modal_projector."
     ]
-
     bitsandbytes_stacked_params_mapping = {
         # shard_name, weight_name, index
         "q_proj": ("qkv_proj", 0),
diff --git a/vllm/model_executor/models/qwen2.py b/vllm/model_executor/models/qwen2.py
index 23e4af040cbfb..72b286fe6f6d6 100644
--- a/vllm/model_executor/models/qwen2.py
+++ b/vllm/model_executor/models/qwen2.py
@@ -395,7 +395,6 @@ class Qwen2ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
         ".v_proj.",
         ".o_proj.",
     ]
-
     bitsandbytes_stacked_params_mapping = {
         # shard_name, weight_name, index
         "q_proj": ("qkv_proj", 0),