diff --git a/dflash/model.py b/dflash/model.py
index 5a1d33a..619c78f 100644
--- a/dflash/model.py
+++ b/dflash/model.py
@@ -173,7 +173,13 @@ def dflash_generate(
 # DFlash model
 # ---------------------------------------------------------------------------
 
-def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1):
+def apply_rotary_pos_emb(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    cos: torch.Tensor,
+    sin: torch.Tensor,
+    unsqueeze_dim: int = 1,
+) -> tuple[torch.Tensor, torch.Tensor]:
     cos = cos.unsqueeze(unsqueeze_dim)
     sin = sin.unsqueeze(unsqueeze_dim)
     q_len = q.size(-2)
@@ -300,6 +306,20 @@ def forward(
 
 
 class DFlashDraftModel(Qwen3PreTrainedModel):
+    """DFlash draft model for speculative decoding with block diffusion.
+    
+    This model implements a lightweight block diffusion architecture that
+    generates multiple draft tokens in parallel for speculative decoding.
+    It uses a Qwen3-based backbone with cross-attention to target model
+    hidden states.
+    
+    Args:
+        config: Qwen3Config with additional dflash_config parameters including:
+            - target_layer_ids: Layer IDs to extract from target model
+            - mask_token_id: Token ID used for masking in diffusion
+            - block_size: Number of tokens to generate per block
+            - num_target_layers: Number of layers in target model
+    """
     config_class = Qwen3Config
     _no_split_modules = ["Qwen3DFlashDecoderLayer"]
 
@@ -320,6 +340,23 @@ def __init__(self, config) -> None:
         self.mask_token_id = self.config.dflash_config.get("mask_token_id", None)
         self.post_init()
 
+    def _check_weights_for_nan(self) -> None:
+        """Check for NaN in model weights and warn if found.
+        
+        This helps diagnose issues like #115 where layer norm weights
+        become NaN during inference.
+        """
+        for name, param in self.named_parameters():
+            if torch.isnan(param).any():
+                import warnings
+                warnings.warn(
+                    f"NaN detected in {name}. This may cause incorrect "
+                    f"outputs or further NaN propagation. Consider "
+                    f"re-downloading the model checkpoint.",
+                    stacklevel=2,
+                )
+                break  # Only warn once per forward pass
+
     def forward(
         self,
         position_ids: torch.LongTensor,
@@ -330,7 +367,13 @@ def forward(
         use_cache: bool = False,
         **kwargs,
     ) -> CausalLMOutputWithPast:
+        if noise_embedding is None:
+            raise ValueError("noise_embedding is required for DFlashDraftModel.forward()")
+        if target_hidden is None:
+            raise ValueError("target_hidden is required for DFlashDraftModel.forward()")
+        
         hidden_states = noise_embedding
+        self._check_weights_for_nan()
         target_hidden = self.hidden_norm(self.fc(target_hidden))
         position_embeddings = self.rotary_emb(hidden_states, position_ids)
         for layer in self.layers: