diff --git a/unsloth/kernels/moe/grouped_gemm/interface.py b/unsloth/kernels/moe/grouped_gemm/interface.py
index 3cb186984f..99c58b36ec 100644
--- a/unsloth/kernels/moe/grouped_gemm/interface.py
+++ b/unsloth/kernels/moe/grouped_gemm/interface.py
@@ -114,7 +114,7 @@ def grouped_gemm_forward(
     - `permute_x`: fuse the permutation of hidden states from token order (original order) to grouped expert order, typically only needed for the first grouped GEMM in an MoE MLP.
         - When `permute_x` is True, `X` is expected to be of shape (num_tokens, K).
         - When `permute_x` is False, `X` is expected to be of shape (total_tokens, K) where `total_tokens = num_tokens * topk` AND already permuted to grouped expert order, i.e., hidden states are sorted such that tokens assigned to each expert are contiguous.
-    - `permute_y`: fused the permuation of the output from expert grouped order back to original token order, typically only needed for the second grouped GEMM in an MoE MLP.
+    - `permute_y`: fused the permutation of the output from expert grouped order back to original token order, typically only needed for the second grouped GEMM in an MoE MLP.
     - `fuse_mul_pre`: fuse the multiplication of the routed input with topk_weights, only done in the first grouped GEMM in an MoE MLP as for Llama4.  Do not use, since results in performance regression as it interrupts the GEMM mainloop.
     - `fuse_mul_post`: fuse the multiplication of the routed output with topk_weights, used only when `permute_y` is True. NOTE: this should only be used when using this kernel for inference, not for training.
 
@@ -881,7 +881,7 @@ def grouped_gemm(
     - `permute_x`: fuse the permutation of hidden states from token order (original order) to grouped expert order, typically only needed for the first grouped GEMM in an MoE MLP.
         - When `permute_x` is True, `X` is expected to be of shape (num_tokens, K).
         - When `permute_x` is False, `X` is expected to be of shape (total_tokens, K) where `total_tokens = num_tokens * topk` AND already permuted to grouped expert order, i.e., hidden states are sorted such that tokens assigned to each expert are contiguous.
-    - `permute_y`: fused the permuation of the output from expert grouped order back to original token order, typically only needed for the second grouped GEMM in an MoE MLP.
+    - `permute_y`: fused the permutation of the output from expert grouped order back to original token order, typically only needed for the second grouped GEMM in an MoE MLP.
     - `fuse_mul`: fuse the multiplication of the routed output with topk_weights, used only when `permute_y` is True. NOTE: this should only be used when using this kernel for inference, not for training.
 
     X: (M, K) hidden states where M is the num_tokens if `permute_x` is True, otherwise `total_tokens` where `total_tokens = num_tokens * topk`.
diff --git a/unsloth/kernels/moe/grouped_gemm/reference/layers/qwen3_moe.py b/unsloth/kernels/moe/grouped_gemm/reference/layers/qwen3_moe.py
index 2bc9cc624d..37f001aefc 100644
--- a/unsloth/kernels/moe/grouped_gemm/reference/layers/qwen3_moe.py
+++ b/unsloth/kernels/moe/grouped_gemm/reference/layers/qwen3_moe.py
@@ -25,7 +25,7 @@ from grouped_gemm.reference.moe_ops import (
 """
 Reference implementation of HF Qwen3 MoE block using grouped gemm.
 
-The Qwen3MoeGroupedGEMMBlock is a reference torch-native implemention.
+The Qwen3MoeGroupedGEMMBlock is a reference torch-native implementation.
 Qwen3MoeFusedGroupedGEMMBlock is a version using the triton grouped gemm kernel.
 
 NOTE: This is NOT to be used for production as it contains many extra checks and saves all intermediate results for debugging.