diff --git a/src/brevitas_examples/llm/main.py b/src/brevitas_examples/llm/main.py
index e6df0759f..25c885459 100644
--- a/src/brevitas_examples/llm/main.py
+++ b/src/brevitas_examples/llm/main.py
@@ -21,8 +21,8 @@
 from brevitas_examples.llm.llm_quant.ln_affine_merge import apply_layernorm_affine_merge
 from brevitas_examples.llm.llm_quant.prepare_for_quantize import replace_mha_with_quantizable_layers
 from brevitas_examples.llm.llm_quant.quantize import quantize_model
-from brevitas_examples.llm.llm_quant.run_utils import get_model_impl
 from brevitas_examples.llm.llm_quant.run_utils import CastFloat16ToFloat32
+from brevitas_examples.llm.llm_quant.run_utils import get_model_impl
 
 parser = argparse.ArgumentParser()
 parser.add_argument(
@@ -132,8 +132,7 @@
     help='Apply activation equalization (SmoothQuant). Layerwise introduces standalone mul nodes,'
     'while fx merges them whenever possible into previous tensors, which is possible on ReLU based models (e.g. OPT).'
 )
-parser.add_argument('--load-awq', type=str, default=None,
-                    help="Load the awq search results.")
+parser.add_argument('--load-awq', type=str, default=None, help="Load the awq search results.")
 parser.add_argument(
     '--export-target',
     default=None,
@@ -217,7 +216,6 @@ def main():
         awq_results = torch.load(args.load_awq, map_location="cpu")
         with CastFloat16ToFloat32():
             apply_awq(model, awq_results)
-    
 
     if (args.export_target or args.eval or args.act_equalization or args.act_calibration or
             args.gptq or args.bias_corr or args.ln_affine_merge or args.weight_equalization):