diff --git a/assets/docs/design.md b/assets/docs/design.md
index c7d929a..5cae4e6 100644
--- a/assets/docs/design.md
+++ b/assets/docs/design.md
@@ -100,8 +100,8 @@ classDiagram
         }
 
         class MultiSegmentFetcher {
-            +Dict muti_fetchers
-            +List muti_keys
+            +Dict multi_fetchers
+            +List multi_keys
             +key_fetch(begin_idx, end_idx, keys) Dict
             +fetch_data(begin_idx, end_idx) Dict
         }
@@ -148,7 +148,7 @@ classDiagram
 
         class Transformer {
             +ModelConfig config
-            +RotaryEmbedding rotary_embeding
+            +RotaryEmbedding rotary_embedding
             +Embedding embed_tokens
             +ModuleList layers
             +RMSNorm norm
diff --git a/astrai/dataset/dataset.py b/astrai/dataset/dataset.py
index 66a4b65..1a49b62 100644
--- a/astrai/dataset/dataset.py
+++ b/astrai/dataset/dataset.py
@@ -72,15 +72,16 @@ class MultiSegmentFetcher:
     Each key corresponds to a different type of data (e.g., "sequence", "mask").
     """
 
-    def __init__(self, muti_segments: Dict):
-        self.muti_keys = list(muti_segments.keys())
-        self.muti_fetchers = {
-            key: BaseSegmentFetcher(segments) for key, segments in muti_segments.items()
+    def __init__(self, multi_segments: Dict):
+        self.multi_keys = list(multi_segments.keys())
+        self.multi_fetchers = {
+            key: BaseSegmentFetcher(segments)
+            for key, segments in multi_segments.items()
         }
 
     def __len__(self) -> int:
         """Returns the minimum length across all fetchers."""
-        len_list = [len(seg) for seg in self.muti_fetchers.values()]
+        len_list = [len(seg) for seg in self.multi_fetchers.values()]
         return min(len_list)
 
     def key_fetch(
@@ -100,7 +101,7 @@ class MultiSegmentFetcher:
         keys = [keys] if isinstance(keys, str) else keys
 
         for key in keys:
-            fetcher = self.muti_fetchers[key]
+            fetcher = self.multi_fetchers[key]
             fetch_tensor = fetcher.fetch_data(begin_idx, end_idx)
             fetch_dict[key] = fetch_tensor
 
@@ -108,7 +109,7 @@ class MultiSegmentFetcher:
 
     def fetch_data(self, begin_idx: int, end_idx: int) -> Dict:
         """Fetch all keys."""
-        return self.key_fetch(begin_idx, end_idx, self.muti_keys)
+        return self.key_fetch(begin_idx, end_idx, self.multi_keys)
 
 
 class BaseDataset(Dataset, ABC):
diff --git a/astrai/model/module.py b/astrai/model/module.py
index 0d22166..28c64f6 100644
--- a/astrai/model/module.py
+++ b/astrai/model/module.py
@@ -257,7 +257,7 @@ class MLA(nn.Module):
 
         self.q_proj = Linear(dim, n_heads * self.head_dim, bias=False)
         self.kv_a_proj = Linear(dim, kv_lora_rank, bias=False)
-        self.kv_norm = RMSNorm(kv_lora_rank, eps=norm_eps)
+        self.kv_norm = RMSNorm(kv_lora_rank, norm_eps)
 
         # KV (k_nope, k_rope, v)
         self.kv_b_proj = Linear(
diff --git a/scripts/tools/train.py b/scripts/tools/train.py
index 5b02b3f..eb43382 100644
--- a/scripts/tools/train.py
+++ b/scripts/tools/train.py
@@ -234,13 +234,13 @@ def train(
         },
     )
 
-    toltal_steps = len(dataset) * n_epoch // (batch_size * nprocs)
+    total_steps = len(dataset) * n_epoch // (batch_size * nprocs)
     scheduler_fn = partial(
         create_scheduler,
         **{
             "schedule_type": "cosine",
             "warmup_steps": warmup_steps,
-            "lr_decay_steps": toltal_steps - warmup_steps,
+            "lr_decay_steps": total_steps - warmup_steps,
         },
     )