chore: update siglip_vit.py

orignal -> original

janus/models/siglip_vit.py

@@ -92,7 +92,7 @@ def norm_cdf(x):
 def trunc_normal_(tensor, mean=0.0, std=1.0, a=-2.0, b=2.0):
     # type: (torch.Tensor, float, float, float, float) -> torch.Tensor
     r"""The original timm.models.layers.weight_init.trunc_normal_ can not handle bfloat16 yet, here we first
-    convert the tensor to float32, apply the trunc_normal_() in float32, and then convert it back to its orignal dtype.
+    convert the tensor to float32, apply the trunc_normal_() in float32, and then convert it back to its original dtype.
     Fills the input Tensor with values drawn from a truncated normal distribution. The values are effectively drawn
     from the normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
     with values outside :math:`[a, b]` redrawn until they are within