diff --git a/backend/patcher/lora.py b/backend/patcher/lora.py
index f7a8819e..c3b0b498 100644
--- a/backend/patcher/lora.py
+++ b/backend/patcher/lora.py
@@ -377,9 +377,7 @@ class LoraLoader:
                 continue
 
             if gguf_cls is not None:
-                gguf_parameter.data = gguf_cls.quantize_pytorch(weight, gguf_parameter.shape)
-                gguf_parameter.baked = False
-                gguf_cls.bake(gguf_parameter)
+                gguf_cls.quantize_pytorch(weight, gguf_parameter)
                 continue
 
             utils.set_attr_raw(self.model, key, torch.nn.Parameter(weight, requires_grad=False))
diff --git a/packages_3rdparty/gguf/quants.py b/packages_3rdparty/gguf/quants.py
index 1fac91ff..abe52d54 100644
--- a/packages_3rdparty/gguf/quants.py
+++ b/packages_3rdparty/gguf/quants.py
@@ -129,17 +129,14 @@ class __Quant(ABC):
         cls.grid = grid.reshape((1, 1, *cls.grid_shape))
 
     @classmethod
-    def quantize_pytorch(cls, data: torch.Tensor, original_shape) -> torch.Tensor:
-        # Copyright Forge 2024, AGPL V3 + CC-BY SA
-
-        original_shape = [x for x in original_shape]
-        original_shape[-1] = -1
-        original_shape = tuple(original_shape)
+    def quantize_pytorch(cls, data, parent) -> torch.Tensor:
+        if not parent.baked:
+            raise ValueError('GGUF Tensor is not baked!')
 
         block_size, type_size = GGML_QUANT_SIZES[cls.qtype]
         blocks = data.reshape(-1, block_size)
-        blocks = cls.quantize_blocks_pytorch(blocks, block_size, type_size)
-        return blocks.reshape(original_shape)
+        parent.data = cls.quantize_blocks_pytorch(blocks, block_size, type_size, parent).contiguous()
+        return parent
 
     @classmethod
     def bake(cls, parameter):
@@ -175,7 +172,7 @@ class __Quant(ABC):
 
     @classmethod
     @abstractmethod
-    def quantize_blocks_pytorch(cls, blocks, block_size, type_size) -> torch.Tensor:
+    def quantize_blocks_pytorch(cls, blocks, block_size, type_size, parent) -> torch.Tensor:
         raise NotImplementedError('Low bit LoRA for this data type is not implemented yet. Please select "Automatic (fp16 LoRA)" in "Diffusion in Low Bits" (on the top line of this page) to use this LoRA.')
 
     @classmethod
@@ -323,7 +320,7 @@ class Q4_0(__Quant, qtype=GGMLQuantizationType.Q4_0):
         return d * qs
 
     @classmethod
-    def quantize_blocks_pytorch(cls, blocks, block_size, type_size) -> torch.Tensor:
+    def quantize_blocks_pytorch(cls, blocks, block_size, type_size, parent) -> torch.Tensor:
         # Copyright Forge 2024, AGPL V3 + CC-BY SA
 
         n_blocks = blocks.shape[0]
@@ -336,10 +333,10 @@ class Q4_0(__Quant, qtype=GGMLQuantizationType.Q4_0):
 
         qs = torch.trunc((blocks * id) + 8.5).clip(0, 15).to(torch.uint8)
 
-        qs = qs.reshape((n_blocks, 2, block_size // 2))
-        qs = qs[:, 0, :] | (qs[:, 1, :] << 4)
+        qs = qs.reshape((n_blocks, block_size // 2, 2))
+        qs = qs[..., 0] | (qs[..., 1] << 4)
 
-        d = d.to(torch.float16).view(torch.uint8)
+        d = d.to(parent.computation_dtype).view(torch.uint8)
 
         return torch.cat([d, qs], dim=-1)
 
@@ -467,7 +464,7 @@ class Q5_0(__Quant, qtype=GGMLQuantizationType.Q5_0):
         return (d * qs)
 
     @classmethod
-    def quantize_blocks_pytorch(cls, blocks, block_size, type_size) -> torch.Tensor:
+    def quantize_blocks_pytorch(cls, blocks, block_size, type_size, parent) -> torch.Tensor:
         # Copyright Forge 2024, AGPL V3 + CC-BY SA
 
         n_blocks = blocks.shape[0]
@@ -611,13 +608,13 @@ class Q8_0(__Quant, qtype=GGMLQuantizationType.Q8_0):
         return x * d
 
     @classmethod
-    def quantize_blocks_pytorch(cls, blocks, block_size, type_size) -> torch.Tensor:
+    def quantize_blocks_pytorch(cls, blocks, block_size, type_size, parent) -> torch.Tensor:
         # Copyright Forge 2024, AGPL V3 + CC-BY SA
         d = torch.abs(blocks).max(dim=1, keepdim=True).values / 127
         ids = torch.where(d == 0, torch.zeros_like(d), 1 / d)
         qs = torch.round(blocks * ids)
-        d = d.to(torch.float16).view(torch.uint8)
-        qs = qs.to(torch.int8).view(torch.uint8)
+        d = d.to(parent.computation_dtype).view(torch.int8)
+        qs = qs.to(torch.int8)
         return torch.cat([d, qs], dim=1)