diff --git a/unsloth/import_fixes.py b/unsloth/import_fixes.py
index 2164a95db9..6a866dc5b2 100644
--- a/unsloth/import_fixes.py
+++ b/unsloth/import_fixes.py
@@ -117,13 +117,16 @@ def fix_xformers_performance_issue():
 def patch_vllm_imports():
     if importlib.util.find_spec("vllm") is None:
         return
+
     def fix_vllm_aimv2_issue():
         # ValueError: 'aimv2' is already used by a Transformers config, pick another name.
         vllm_version = importlib_version("vllm")
         if Version(vllm_version) < Version("0.10.1"):
             vllm_version = importlib.util.find_spec("vllm").origin
             vllm_version = os.path.split(vllm_version)[0]
-            ovis_config = Path(vllm_version) / "transformers_utils" / "configs" / "ovis.py"
+            ovis_config = (
+                Path(vllm_version) / "transformers_utils" / "configs" / "ovis.py"
+            )
             try:
                 if ovis_config.exists():
                     with open(ovis_config, "r+", encoding = "utf-8") as f:
@@ -160,16 +163,18 @@ def patch_vllm_imports():
         # https://github.com/vllm-project/vllm/pull/22772/files
         # trl still wants to use GuidedDecodingParams. This is a temporary patch till trl updates
         import vllm
+
         try:
             from vllm.sampling_params import GuidedDecodingParams
         except ImportError:
-            vllm.sampling_params.GuidedDecodingParams = vllm.sampling_params.StructuredOutputsParams
+            vllm.sampling_params.GuidedDecodingParams = (
+                vllm.sampling_params.StructuredOutputsParams
+            )
 
     fix_vllm_aimv2_issue()
     fix_vllm_guided_decoding_params()
 
 
-
 def ignore_logger_messages():
     # Ignore Environment variable `HF_TOKEN` is set
     try:
diff --git a/unsloth/models/qwen3_moe.py b/unsloth/models/qwen3_moe.py
index 94633ca2d4..4202608536 100644
--- a/unsloth/models/qwen3_moe.py
+++ b/unsloth/models/qwen3_moe.py
@@ -59,7 +59,9 @@ def Qwen3MoeSparseMoeBlock_fast_forward(self, X, temp_gate = None, temp_up = Non
         self.gate_proj, X, out = temp_gate
     )  # pretty much the only change from transformers implementation.
 
-    routing_weights = torch_nn_functional_softmax(router_logits, dim = -1, dtype = torch.float32)
+    routing_weights = torch_nn_functional_softmax(
+        router_logits, dim = -1, dtype = torch.float32
+    )
     routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim = -1)
     routing_weights /= routing_weights.sum(dim = -1, keepdim = True)
     # we cast back to the input dtype
diff --git a/unsloth/models/rl.py b/unsloth/models/rl.py
index b1773a318d..18103ca7eb 100644
--- a/unsloth/models/rl.py
+++ b/unsloth/models/rl.py
@@ -348,10 +348,14 @@ def _patch_trl_rl_trainers(trainer_file = "grpo_trainer"):
         and trainer_file.split("_")[0] in x.lower()
     ]
     if len(name) != 1:
-        print(f"Unsloth: Could not find Trainer class in trl.trainer.{trainer_file}. Found: {name}")
+        print(
+            f"Unsloth: Could not find Trainer class in trl.trainer.{trainer_file}. Found: {name}"
+        )
         return
     if len(config) != 1:
-        print(f"Unsloth: Could not find Config class in trl.trainer.{trainer_file}. Found: {config}")
+        print(
+            f"Unsloth: Could not find Config class in trl.trainer.{trainer_file}. Found: {config}"
+        )
         return
 
     # Get SFTTrainer, SFTConfig
@@ -360,12 +364,16 @@ def _patch_trl_rl_trainers(trainer_file = "grpo_trainer"):
     try:
         RLTrainer = eval(f"trl.trainer.{trainer_file}.{RLTrainer_name}")
     except:
-        print(f"Unsloth: Could not load {RLTrainer_name} from trl.trainer.{trainer_file}")
+        print(
+            f"Unsloth: Could not load {RLTrainer_name} from trl.trainer.{trainer_file}"
+        )
         return
     try:
         RLConfig = eval(f"trl.trainer.{trainer_file}.{RLConfig_name}")
     except:
-        print(f"Unsloth: Could not load {RLConfig_name} from trl.trainer.{trainer_file}")
+        print(
+            f"Unsloth: Could not load {RLConfig_name} from trl.trainer.{trainer_file}"
+        )
         return
 
     # Check name