diff --git a/vllm/model_executor/layers/parameters/lazy_compressed.py b/vllm/model_executor/layers/parameters/lazy_compressed.py
index 1ef776170d53c..96e892a03d1fb 100644
--- a/vllm/model_executor/layers/parameters/lazy_compressed.py
+++ b/vllm/model_executor/layers/parameters/lazy_compressed.py
@@ -63,7 +63,7 @@ def compress(self) -> None:
         density = torch.count_nonzero(
             self.uncompressed_data).item() / numpy.prod(self.shape)
 
-        # only compress if we have sufficient sparsity (>=45%), currently 
+        # only compress if we have sufficient sparsity (>=45%), currently
         # this applies globally across all formats including 2:4
         if (1 - density) < 0.45:
             return
diff --git a/vllm/model_executor/layers/sparsity/sparse_w16a16_linear_method.py b/vllm/model_executor/layers/sparsity/sparse_w16a16_linear_method.py
index b283b42c7e0ee..1420ee97be0ce 100644
--- a/vllm/model_executor/layers/sparsity/sparse_w16a16_linear_method.py
+++ b/vllm/model_executor/layers/sparsity/sparse_w16a16_linear_method.py
@@ -71,4 +71,3 @@ def apply_weights(
             assert not w.compress_transposed
             output = F.linear(x, w.compressed_data.decompress(), bias)
         return output
-