diff --git a/src/litdata/streaming/serializers.py b/src/litdata/streaming/serializers.py
index 57d3e85e..35ef7a30 100644
--- a/src/litdata/streaming/serializers.py
+++ b/src/litdata/streaming/serializers.py
@@ -27,9 +27,6 @@
 
 from litdata.constants import _NUMPY_DTYPES_MAPPING, _TORCH_DTYPES_MAPPING
 
-if TYPE_CHECKING:
-    from PIL.JpegImagePlugin import JpegImageFile
-
 _PIL_AVAILABLE = RequirementCache("PIL")
 _TORCH_VISION_AVAILABLE = RequirementCache("torchvision")
 _AV_AVAILABLE = RequirementCache("av")
@@ -70,6 +67,8 @@ def serialize(self, item: Any) -> Tuple[bytes, Optional[str]]:
 
     @classmethod
     def deserialize(cls, data: bytes) -> Any:
+        if not _PIL_AVAILABLE:
+            raise ModuleNotFoundError("PIL is required. Run `pip install pillow`")
         from PIL import Image
 
         idx = 3 * 4
@@ -94,6 +93,9 @@ class JPEGSerializer(Serializer):
     """The JPEGSerializer serialize and deserialize JPEG image to and from bytes."""
 
     def serialize(self, item: Any) -> Tuple[bytes, Optional[str]]:
+        if not _PIL_AVAILABLE:
+            raise ModuleNotFoundError("PIL is required. Run `pip install pillow`")
+
         from PIL import Image
         from PIL.GifImagePlugin import GifImageFile
         from PIL.JpegImagePlugin import JpegImageFile
@@ -121,7 +123,7 @@ def serialize(self, item: Any) -> Tuple[bytes, Optional[str]]:
             buff.seek(0)
             return buff.read(), None
 
-        raise TypeError(f"The provided item should be of type {JpegImageFile}. Found {item}.")
+        raise TypeError(f"The provided item should be of type `JpegImageFile`. Found {item}.")
 
     def deserialize(self, data: bytes) -> Union["JpegImageFile", torch.Tensor]:
         if _TORCH_VISION_AVAILABLE:
@@ -183,7 +185,7 @@ def deserialize(self, data: bytes) -> torch.Tensor:
         shape = []
         for shape_idx in range(shape_size):
             shape.append(np.frombuffer(data[8 + 4 * shape_idx : 8 + 4 * (shape_idx + 1)], np.uint32).item())
-        idx_start = 8 + 4 * (shape_idx + 1)
+        idx_start = 8 + 4 * shape_size
         idx_end = len(data)
         if idx_end > idx_start:
             tensor = torch.frombuffer(data[idx_start:idx_end], dtype=dtype)
@@ -249,7 +251,7 @@ def deserialize(self, data: bytes) -> np.ndarray:
             shape.append(np.frombuffer(data[8 + 4 * shape_idx : 8 + 4 * (shape_idx + 1)], np.uint32).item())
 
         # deserialize the numpy array bytes
-        tensor = np.frombuffer(data[8 + 4 * (shape_idx + 1) : len(data)], dtype=dtype)
+        tensor = np.frombuffer(data[8 + 4 * shape_size : len(data)], dtype=dtype)
         if tensor.shape == shape:
             return tensor
         return np.reshape(tensor, shape)