diff --git a/toolkit/models/sref.py b/toolkit/models/sref.py
new file mode 100644
index 00000000..7290e894
--- /dev/null
+++ b/toolkit/models/sref.py
@@ -0,0 +1,61 @@
+import torch
+import torch.nn as nn
+
+
+class SrefImageEncoder(torch.nn.Module):
+    def __init__(
+        self,
+        input_features: int = 1152,
+        input_tokens: int = 512,
+        output_tokens: int = 512,
+        output_features: int = 4096,
+        intermediate_size: int = 4096,
+        num_digits: int = 10,
+        device=None,
+        dtype=None,
+    ) -> None:
+        super().__init__()
+        self.input_features = input_features
+        self.device = device
+        self.dtype = dtype
+        self.input_tokens = input_tokens
+        self.output_tokens = output_tokens
+        self.output_features = output_features
+        self.intermediate_size = intermediate_size
+        self.num_digits = num_digits
+
+        self.proj_in = nn.Linear(
+            input_features, intermediate_size, dtype=dtype)
+        # (bs, num_digits, intermediate_size)
+        self.conv_pool = nn.Conv1d(input_tokens, num_digits, 1, dtype=dtype)
+        self.linear_pool = nn.Linear(
+            intermediate_size, 1, dtype=dtype)  # (bs, num_digits, 1)
+        # do sigmoid for digits 0.0-1.0 = (0 to 10) Always floor when rounding digits so you get 0-9
+        self.flatten = nn.Flatten()  # (bs, num_digits * intermediate_size)
+
+        # a numeric sref would come in here with num_digits
+        self.sref_in = nn.Linear(num_digits, intermediate_size, dtype=dtype)
+        self.fc1 = nn.Linear(intermediate_size, intermediate_size, dtype=dtype)
+        self.fc2 = nn.Linear(intermediate_size, intermediate_size, dtype=dtype)
+
+        self.proj_out = nn.Linear(
+            intermediate_size, output_features * output_tokens, dtype=dtype)
+
+    def forward(self, siglip_embeds) -> torch.Tensor:
+        x = self.proj_in(siglip_embeds)
+        x = torch.nn.functional.silu(x)
+        x = self.conv_pool(x)
+        x = self.linear_pool(x)
+        x = torch.sigmoid(x)
+
+        sref = self.flatten(x)
+
+        x = self.sref_in(sref)
+        x = torch.nn.functional.silu(x)
+        x = self.fc1(x)
+        x = torch.nn.functional.silu(x)
+        x = self.fc2(x)
+        x = torch.nn.functional.silu(x)
+        x = self.proj_out(x)
+
+        return x
diff --git a/toolkit/optimizers/automagic.py b/toolkit/optimizers/automagic.py
index ac7355f1..0aa5d51f 100644
--- a/toolkit/optimizers/automagic.py
+++ b/toolkit/optimizers/automagic.py
@@ -322,6 +322,8 @@ class Automagic(torch.optim.Optimizer):
     
     def load_state_dict(self, state_dict, strict=True):
         # load the lr_mask from the state_dict
+        # dont load state dict for now. Has a bug. Need to fix it.
+        return
         idx = 0
         for group in self.param_groups:
             for p in group['params']: