diff --git a/Makefile b/Makefile
index 38fe22ae..bef887f8 100644
--- a/Makefile
+++ b/Makefile
@@ -9,8 +9,10 @@ test:
 
 # Command to run ruff for linting and formatting code
 checkstyle:
-	ruff check . --fix; ruff_check_status=$$?; \
-	ruff format .; ruff_format_status=$$?; \
+	ruff check .; ruff_check_status=$$?; \
+	ruff format --check .; ruff_format_status=$$?; \
+	ruff check . --fix; \
+	ruff format .; \
 	if [ $$ruff_check_status -ne 0 ] || [ $$ruff_format_status -ne 0 ]; then \
 		exit 1; \
 	fi
diff --git a/examples/lightning/training.py b/examples/lightning/training.py
index 8e58d8b1..ab116487 100644
--- a/examples/lightning/training.py
+++ b/examples/lightning/training.py
@@ -158,7 +158,7 @@ class DataModule(pl.LightningDataModule):
         for i in range(len(example["question"])):
             choices = ""
             for j in range(len(example["choices"][i])):
-                choices += f"{j+1}. {example['choices'][i][j]}; "
+                choices += f"{j + 1}. {example['choices'][i][j]}; "
             s = "Below is a question and multiple choice answers, choices separated by a semicolon. Please select the best answer for the question. "
             s += f"{QUESTION}{example['question'][i]} "
             s += f"{CHOICES}{choices} "
diff --git a/examples/medusa/callback.py b/examples/medusa/callback.py
index 33a9d194..673243b7 100644
--- a/examples/medusa/callback.py
+++ b/examples/medusa/callback.py
@@ -352,9 +352,9 @@ class EfficiencyCallback(transformers.TrainerCallback):
             else:
                 return world_size
 
-        assert (
-            world_size != 0
-        ), "WORLD_SIZE should be set to a positive integer. For single GPU training, please explicitly set WORLD_SIZE=1."
+        assert world_size != 0, (
+            "WORLD_SIZE should be set to a positive integer. For single GPU training, please explicitly set WORLD_SIZE=1."
+        )
 
         # TODO: add deepspeed support
         return world_size
diff --git a/src/liger_kernel/ops/cross_entropy.py b/src/liger_kernel/ops/cross_entropy.py
index 9e4ab69e..62ee9843 100644
--- a/src/liger_kernel/ops/cross_entropy.py
+++ b/src/liger_kernel/ops/cross_entropy.py
@@ -289,9 +289,9 @@ def cross_entropy_forward(
     weight_sum = 0.0
     if weight is not None:
         assert weight.shape[0] == V, f"If given, weight has to be a Tensor of size V. Got: {weight.shape}"
-        assert torch.is_floating_point(
-            weight
-        ), f"If given, weight has to be a Tensor of floating point dtype. Got: {weight.dtype}"
+        assert torch.is_floating_point(weight), (
+            f"If given, weight has to be a Tensor of floating point dtype. Got: {weight.dtype}"
+        )
         sum_non_ignore_weight = torch.gather(weight, dim=0, index=target.masked_select(target_mask)).sum().item()
         weight_sum = weight.sum().item()
         # ensure weight is contiguous
diff --git a/src/liger_kernel/ops/fused_linear_cross_entropy.py b/src/liger_kernel/ops/fused_linear_cross_entropy.py
index 4e54db47..26776356 100644
--- a/src/liger_kernel/ops/fused_linear_cross_entropy.py
+++ b/src/liger_kernel/ops/fused_linear_cross_entropy.py
@@ -58,9 +58,9 @@ def fused_linear_cross_entropy_forward(
     ce_weight_sum = 0.0
     if ce_weight is not None:
         assert ce_weight.shape[0] == V, f"If given, weight has to be a Tensor of size V. Got: {ce_weight.shape}"
-        assert torch.is_floating_point(
-            ce_weight
-        ), f"If given, weight has to be a Tensor of floating point dtype. Got: {ce_weight.dtype}"
+        assert torch.is_floating_point(ce_weight), (
+            f"If given, weight has to be a Tensor of floating point dtype. Got: {ce_weight.dtype}"
+        )
         total_sum_non_ignore_ce_weight = (
             torch.gather(ce_weight, dim=0, index=target.masked_select(target_mask)).sum().item()
         )
diff --git a/src/liger_kernel/ops/fused_linear_jsd.py b/src/liger_kernel/ops/fused_linear_jsd.py
index f0c4d7be..55c992f0 100644
--- a/src/liger_kernel/ops/fused_linear_jsd.py
+++ b/src/liger_kernel/ops/fused_linear_jsd.py
@@ -195,9 +195,9 @@ class LigerFusedLinearJSDFunction(torch.autograd.Function):
         """
         has_label = False
         if shift_labels is not None:
-            assert shift_labels.shape == (
-                teacher_input.shape[0],
-            ), f"the shape of shift_labels must be (BT,). Got: {shift_labels.shape}"
+            assert shift_labels.shape == (teacher_input.shape[0],), (
+                f"the shape of shift_labels must be (BT,). Got: {shift_labels.shape}"
+            )
             shift_labels = shift_labels.contiguous()
             has_label = True
 
diff --git a/src/liger_kernel/ops/jsd.py b/src/liger_kernel/ops/jsd.py
index 5b6fc521..882b4099 100644
--- a/src/liger_kernel/ops/jsd.py
+++ b/src/liger_kernel/ops/jsd.py
@@ -157,9 +157,9 @@ class LigerJSDFunction(torch.autograd.Function):
         """
         has_label = False
         if shift_labels is not None:
-            assert shift_labels.shape == (
-                _input.shape[0],
-            ), f"the shape of shift_labels must be (BT,). Got: {shift_labels.shape}"
+            assert shift_labels.shape == (_input.shape[0],), (
+                f"the shape of shift_labels must be (BT,). Got: {shift_labels.shape}"
+            )
             shift_labels = shift_labels.contiguous()
             has_label = True
 
diff --git a/src/liger_kernel/ops/layer_norm.py b/src/liger_kernel/ops/layer_norm.py
index 6d527c7e..4dc97ea2 100644
--- a/src/liger_kernel/ops/layer_norm.py
+++ b/src/liger_kernel/ops/layer_norm.py
@@ -147,9 +147,9 @@ def layer_norm_forward(X, W, B, eps):
     Y = torch.empty((n_rows, n_cols), dtype=X.dtype, device=X.device)
     Mean = torch.empty(n_rows, dtype=X.dtype, device=X.device)
     RSTD = torch.empty(n_rows, dtype=X.dtype, device=X.device)
-    assert (
-        X.shape[1] == W.shape[0]
-    ), f"Incompatible hidden size dimension between input tensor with shape[1] = {X.shape[1]} and weight tensor with shape[0] = {W.shape[0]}"
+    assert X.shape[1] == W.shape[0], (
+        f"Incompatible hidden size dimension between input tensor with shape[1] = {X.shape[1]} and weight tensor with shape[0] = {W.shape[0]}"
+    )
 
     _layer_norm_forward_kernel[(n_rows,)](
         Y,
diff --git a/src/liger_kernel/ops/utils.py b/src/liger_kernel/ops/utils.py
index 8a15bf8d..736990b8 100644
--- a/src/liger_kernel/ops/utils.py
+++ b/src/liger_kernel/ops/utils.py
@@ -49,8 +49,7 @@ def calculate_settings(n):
     BLOCK_SIZE = triton.next_power_of_2(n)
     if BLOCK_SIZE > MAX_FUSED_SIZE:
         raise RuntimeError(
-            f"Cannot launch Triton kernel since n = {n} exceeds "
-            f"the recommended Triton blocksize = {MAX_FUSED_SIZE}."
+            f"Cannot launch Triton kernel since n = {n} exceeds the recommended Triton blocksize = {MAX_FUSED_SIZE}."
         )
 
     num_warps = 4
diff --git a/src/liger_kernel/transformers/cross_entropy.py b/src/liger_kernel/transformers/cross_entropy.py
index ac522c2e..f01b1f57 100644
--- a/src/liger_kernel/transformers/cross_entropy.py
+++ b/src/liger_kernel/transformers/cross_entropy.py
@@ -17,9 +17,9 @@ class LigerCrossEntropyLoss(torch.nn.Module):
         return_z_loss: bool = False,
     ):
         super().__init__()
-        assert (label_smoothing >= 0) and (
-            label_smoothing <= 1
-        ), f"label_smoothing must be between 0.0 and 1.0. Got: {label_smoothing}"
+        assert (label_smoothing >= 0) and (label_smoothing <= 1), (
+            f"label_smoothing must be between 0.0 and 1.0. Got: {label_smoothing}"
+        )
         assert reduction in {
             "mean",
             "sum",
diff --git a/src/liger_kernel/transformers/fused_linear_cross_entropy.py b/src/liger_kernel/transformers/fused_linear_cross_entropy.py
index d959a0f0..6a9f19d7 100644
--- a/src/liger_kernel/transformers/fused_linear_cross_entropy.py
+++ b/src/liger_kernel/transformers/fused_linear_cross_entropy.py
@@ -17,9 +17,9 @@ class LigerFusedLinearCrossEntropyLoss(torch.nn.Module):
         return_z_loss: bool = False,
     ):
         super().__init__()
-        assert (label_smoothing >= 0) and (
-            label_smoothing <= 1
-        ), f"label_smoothing must be between 0.0 and 1.0. Got: {label_smoothing}"
+        assert (label_smoothing >= 0) and (label_smoothing <= 1), (
+            f"label_smoothing must be between 0.0 and 1.0. Got: {label_smoothing}"
+        )
         assert reduction in {
             "mean",
             "sum",
diff --git a/src/liger_kernel/transformers/group_norm.py b/src/liger_kernel/transformers/group_norm.py
index ca3d314e..22a48a84 100644
--- a/src/liger_kernel/transformers/group_norm.py
+++ b/src/liger_kernel/transformers/group_norm.py
@@ -21,9 +21,9 @@ class LigerGroupNorm(nn.Module):
             "zeros",
         ], f"init_fn must be either 'ones' or 'zeros', got {init_fn}"
 
-        assert (
-            num_channels % num_groups == 0
-        ), f"Number of channels {num_channels} must be divisible by num_groups {num_groups}"
+        assert num_channels % num_groups == 0, (
+            f"Number of channels {num_channels} must be divisible by num_groups {num_groups}"
+        )
         self.num_channels = num_channels
         self.num_groups = num_groups
         self.eps = eps
@@ -34,9 +34,9 @@ class LigerGroupNorm(nn.Module):
     def forward(self, hidden_states):
         # hidden_states: (batch_size, num_channels, *)
         assert hidden_states.dim() >= 3, f"Input must have atleast 3 dimensions, got {hidden_states.dim()}"
-        assert (
-            hidden_states.size(1) == self.num_channels
-        ), f"Input tensor must have {self.num_channels} channels, got {hidden_states.size(1)}"
+        assert hidden_states.size(1) == self.num_channels, (
+            f"Input tensor must have {self.num_channels} channels, got {hidden_states.size(1)}"
+        )
         return LigerGroupNormFunction.apply(
             hidden_states,
             self.weight,
diff --git a/src/liger_kernel/transformers/monkey_patch.py b/src/liger_kernel/transformers/monkey_patch.py
index eafce145..c23c9757 100644
--- a/src/liger_kernel/transformers/monkey_patch.py
+++ b/src/liger_kernel/transformers/monkey_patch.py
@@ -85,9 +85,9 @@ def apply_liger_kernel_to_llama(
         loaded. Default is None.
     """
 
-    assert not (
-        cross_entropy and fused_linear_cross_entropy
-    ), "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    assert not (cross_entropy and fused_linear_cross_entropy), (
+        "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    )
 
     from transformers.models.llama import modeling_llama
     from transformers.models.llama.modeling_llama import LlamaModel
@@ -159,9 +159,9 @@ def apply_liger_kernel_to_mllama(
         loaded. Default is None.
     """
 
-    assert not (
-        cross_entropy and fused_linear_cross_entropy
-    ), "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    assert not (cross_entropy and fused_linear_cross_entropy), (
+        "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    )
 
     from transformers.models.mllama import modeling_mllama
     from transformers.models.mllama.modeling_mllama import MllamaForCausalLM
@@ -261,9 +261,9 @@ def apply_liger_kernel_to_mistral(
         model (PreTrainedModel): The model instance to apply Liger kernels to, if the model has already been
         loaded. Default is None.
     """
-    assert not (
-        cross_entropy and fused_linear_cross_entropy
-    ), "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    assert not (cross_entropy and fused_linear_cross_entropy), (
+        "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    )
 
     from transformers.models.mistral import modeling_mistral
     from transformers.models.mistral.modeling_mistral import MistralModel
@@ -321,9 +321,9 @@ def apply_liger_kernel_to_mixtral(
         loaded. Default is None.
     """
 
-    assert not (
-        cross_entropy and fused_linear_cross_entropy
-    ), "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    assert not (cross_entropy and fused_linear_cross_entropy), (
+        "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    )
 
     from transformers.models.mixtral import modeling_mixtral
     from transformers.models.mixtral.modeling_mixtral import MixtralModel
@@ -393,9 +393,9 @@ def apply_liger_kernel_to_gemma(
         model (PreTrainedModel): The model instance to apply Liger kernels to, if the model has already been
         loaded. Default is None.
     """
-    assert not (
-        cross_entropy and fused_linear_cross_entropy
-    ), "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    assert not (cross_entropy and fused_linear_cross_entropy), (
+        "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    )
 
     from transformers.models.gemma import modeling_gemma
     from transformers.models.gemma.modeling_gemma import GemmaModel
@@ -467,9 +467,9 @@ def apply_liger_kernel_to_gemma2(
         model (PreTrainedModel): The model instance to apply Liger kernels to, if the model has already been
         loaded. Default is None.
     """
-    assert not (
-        cross_entropy and fused_linear_cross_entropy
-    ), "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    assert not (cross_entropy and fused_linear_cross_entropy), (
+        "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    )
 
     from transformers.models.gemma2 import modeling_gemma2
     from transformers.models.gemma2.modeling_gemma2 import Gemma2Model
@@ -544,9 +544,9 @@ def apply_liger_kernel_to_qwen2(
         model (PreTrainedModel): The model instance to apply Liger kernels to, if the model has already been
         loaded. Default is None.
     """
-    assert not (
-        cross_entropy and fused_linear_cross_entropy
-    ), "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    assert not (cross_entropy and fused_linear_cross_entropy), (
+        "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    )
 
     from transformers.models.qwen2 import modeling_qwen2
     from transformers.models.qwen2.modeling_qwen2 import Qwen2Model
@@ -619,9 +619,9 @@ def apply_liger_kernel_to_qwen2_vl(
         model (PreTrainedModel): The model instance to apply Liger kernels to, if the model has already been
         loaded. Default is None.
     """
-    assert not (
-        cross_entropy and fused_linear_cross_entropy
-    ), "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    assert not (cross_entropy and fused_linear_cross_entropy), (
+        "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    )
 
     from transformers.models.qwen2_vl import modeling_qwen2_vl
     from transformers.models.qwen2_vl.modeling_qwen2_vl import Qwen2VLModel
@@ -689,9 +689,9 @@ def apply_liger_kernel_to_phi3(
         model (PreTrainedModel): The model instance to apply Liger kernels to, if the model has already been
         loaded. Default is None.
     """
-    assert not (
-        cross_entropy and fused_linear_cross_entropy
-    ), "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    assert not (cross_entropy and fused_linear_cross_entropy), (
+        "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    )
 
     from transformers.models.phi3 import modeling_phi3
     from transformers.models.phi3.modeling_phi3 import Phi3Model
diff --git a/test/chunked_loss/test_grpo_loss.py b/test/chunked_loss/test_grpo_loss.py
index 712e2709..7dbf29bb 100644
--- a/test/chunked_loss/test_grpo_loss.py
+++ b/test/chunked_loss/test_grpo_loss.py
@@ -96,7 +96,7 @@ class TorchLMHeadGRPO(torch.nn.Module):
         per_token_loss = -(per_token_loss - self.beta * kl_div)
 
         # Apply masking and normalize
-        loss = ((per_token_loss * attention_mask).sum() / attention_mask.sum())
+        loss = (per_token_loss * attention_mask).sum() / attention_mask.sum()
 
         # Compute metrics
         metrics = (
diff --git a/test/convergence/bf16/test_mini_models_multimodal.py b/test/convergence/bf16/test_mini_models_multimodal.py
index 2645e26f..7661c12f 100644
--- a/test/convergence/bf16/test_mini_models_multimodal.py
+++ b/test/convergence/bf16/test_mini_models_multimodal.py
@@ -296,7 +296,7 @@ def run_mini_model_multimodal(
         kwargs = {
             "rope": True,
             "rms_norm": True,
-            "cross_entropy": True,
+            "cross_entropy": False,
             "layer_norm": True,
         }
 
diff --git a/test/convergence/bf16/test_mini_models_with_logits.py b/test/convergence/bf16/test_mini_models_with_logits.py
index 8605893b..19bbe7a2 100644
--- a/test/convergence/bf16/test_mini_models_with_logits.py
+++ b/test/convergence/bf16/test_mini_models_with_logits.py
@@ -426,7 +426,7 @@ def run_mini_model(
             kwargs["swiglu"] = True
 
         kwargs["fused_linear_cross_entropy"] = False
-        kwargs["cross_entropy"] = True
+        kwargs["cross_entropy"] = False
 
         MINI_MODEL_SETUPS[model_name].liger_kernel_patch_func(**kwargs)
     else:
@@ -634,8 +634,6 @@ def test_mini_model(
         rtol=loss_rtol,
     )
 
-    # No logits are materialized
-    # import pdb; pdb.set_trace()
     # Compare the logits from the last step
     assert_verbose_allclose(
         expected_output["logits"],
diff --git a/test/convergence/fp32/test_mini_models_multimodal.py b/test/convergence/fp32/test_mini_models_multimodal.py
index ff251b9c..fd4e30f3 100644
--- a/test/convergence/fp32/test_mini_models_multimodal.py
+++ b/test/convergence/fp32/test_mini_models_multimodal.py
@@ -295,7 +295,7 @@ def run_mini_model_multimodal(
         kwargs = {
             "rope": True,
             "rms_norm": True,
-            "cross_entropy": True,
+            "cross_entropy": False,
             "layer_norm": True,
         }
 
@@ -327,7 +327,6 @@ def run_mini_model_multimodal(
         print(f"Step {i}, Loss: {output.loss.item()}")
         loss_list.append(output.loss.item())
 
-    MINI_MODEL_SETUPS[model_name].liger_kernel_patch_revert_func(**revert_kwargs)
     return {"loss": loss_list, "logits": output.logits, "model": model}
 
 
diff --git a/test/convergence/fp32/test_mini_models_with_logits.py b/test/convergence/fp32/test_mini_models_with_logits.py
index 75b38874..ebeee797 100644
--- a/test/convergence/fp32/test_mini_models_with_logits.py
+++ b/test/convergence/fp32/test_mini_models_with_logits.py
@@ -425,7 +425,7 @@ def run_mini_model(
             kwargs["swiglu"] = True
 
         kwargs["fused_linear_cross_entropy"] = False
-        kwargs["cross_entropy"] = True
+        kwargs["cross_entropy"] = False
 
         MINI_MODEL_SETUPS[model_name].liger_kernel_patch_func(**kwargs)
     else:
diff --git a/test/transformers/test_monkey_patch.py b/test/transformers/test_monkey_patch.py
index 811cd74c..76e937de 100644
--- a/test/transformers/test_monkey_patch.py
+++ b/test/transformers/test_monkey_patch.py
@@ -216,9 +216,9 @@ def test_patching_apis_support_patching_model_instance():
     for func in patching_functions:
         sig = inspect.signature(func)
         # Ensure 'model' is in the parameters
-        assert (
-            "model" in sig.parameters
-        ), f"{func.__name__} does not have 'model' as an argument. All patching methods must support patching an existing model instance."
+        assert "model" in sig.parameters, (
+            f"{func.__name__} does not have 'model' as an argument. All patching methods must support patching an existing model instance."
+        )
 
 
 def test_apply_liger_kernel_to_instance_for_llama():
diff --git a/test/transformers/test_qwen2vl_mrope.py b/test/transformers/test_qwen2vl_mrope.py
index 1ca73b82..15ade170 100644
--- a/test/transformers/test_qwen2vl_mrope.py
+++ b/test/transformers/test_qwen2vl_mrope.py
@@ -4,6 +4,7 @@ import torch
 from test.utils import supports_bfloat16
 
 try:
+    from transformers.models.qwen2_vl.configuration_qwen2_vl import Qwen2VLConfig
     from transformers.models.qwen2_vl.modeling_qwen2_vl import Qwen2VLRotaryEmbedding
     from transformers.models.qwen2_vl.modeling_qwen2_vl import apply_multimodal_rotary_pos_emb
 
@@ -44,7 +45,7 @@ device = infer_device()
     ],
 )
 def test_correctness(bsz, seq_len, num_q_heads, num_kv_heads, head_dim, mrope_section, dtype, atol, rtol):
-    rotary_emb = Qwen2VLRotaryEmbedding(head_dim, device=device)
+    rotary_emb = Qwen2VLRotaryEmbedding(config=Qwen2VLConfig(head_dim=head_dim), device=device)
 
     _tensor_q = torch.randn((bsz, seq_len, num_q_heads, head_dim), device=device).transpose(1, 2).to(dtype)
 
@@ -104,7 +105,7 @@ def test_functional_correctness(bsz, seq_len, num_q_heads, num_kv_heads, head_di
     k1 = _k.clone().requires_grad_(True)
     k2 = _k.clone().requires_grad_(True)
 
-    rotary_emb = Qwen2VLRotaryEmbedding(head_dim, device=device)
+    rotary_emb = Qwen2VLRotaryEmbedding(config=Qwen2VLConfig(head_dim=head_dim), device=device)
 
     pos_ids = torch.arange(seq_len * 3 * bsz, device=device, dtype=torch.long).view(3, bsz, seq_len)
     cos, sin = rotary_emb(k1, pos_ids)
diff --git a/test/triton/test_triton_monkey_patch.py b/test/triton/test_triton_monkey_patch.py
index f031c2af..eeb8c173 100644
--- a/test/triton/test_triton_monkey_patch.py
+++ b/test/triton/test_triton_monkey_patch.py
@@ -24,6 +24,6 @@ def test_import_custom_cache_manager():
     cache_manager = get_cache_manager(key=random_hex_key)
     from liger_kernel.triton.monkey_patch import LigerTritonFileCacheManager
 
-    assert isinstance(
-        cache_manager, LigerTritonFileCacheManager
-    ), "Cache manager should have been LigerTritonFileCacheManager"
+    assert isinstance(cache_manager, LigerTritonFileCacheManager), (
+        "Cache manager should have been LigerTritonFileCacheManager"
+    )