diff --git a/sam3/model_builder.py b/sam3/model_builder.py
index a5dea876d..35320dbbc 100644
--- a/sam3/model_builder.py
+++ b/sam3/model_builder.py
@@ -5,7 +5,7 @@
 import os
 from typing import Optional
 
-import pkg_resources
+from pathlib import Path as _Path
 import torch
 import torch.nn as nn
 from huggingface_hub import hf_hub_download
@@ -596,9 +596,7 @@ def build_sam3_image_model(
         A SAM3 image model
     """
     if bpe_path is None:
-        bpe_path = pkg_resources.resource_filename(
-            "sam3", "assets/bpe_simple_vocab_16e6.txt.gz"
-        )
+        bpe_path = str(_Path(__file__).parent / "assets" / "bpe_simple_vocab_16e6.txt.gz")
 
     # Create visual components
     compile_mode = "default" if compile else None
@@ -695,9 +693,7 @@ def build_sam3_video_model(
         Sam3VideoInferenceWithInstanceInteractivity: The instantiated dense tracking model
     """
     if bpe_path is None:
-        bpe_path = pkg_resources.resource_filename(
-            "sam3", "assets/bpe_simple_vocab_16e6.txt.gz"
-        )
+        bpe_path = str(_Path(__file__).parent / "assets" / "bpe_simple_vocab_16e6.txt.gz")
 
     # Build Tracker module
     tracker = build_tracker(apply_temporal_disambiguation=apply_temporal_disambiguation)
@@ -1105,9 +1101,7 @@ def build_sam3_multiplex_video_predictor(
         Sam3MultiplexVideoPredictor: The fully-initialized predictor
     """
     if bpe_path is None:
-        bpe_path = pkg_resources.resource_filename(
-            "sam3", "assets/bpe_simple_vocab_16e6.txt.gz"
-        )
+        bpe_path = str(_Path(__file__).parent / "assets" / "bpe_simple_vocab_16e6.txt.gz")
 
     from sam3.model.sam3_multiplex_base import Sam3MultiplexPredictorWrapper
     from sam3.model.sam3_multiplex_detector import Sam3MultiplexDetector
diff --git a/sam3/perflib/fused.py b/sam3/perflib/fused.py
index 6800cca64..be30d05af 100644
--- a/sam3/perflib/fused.py
+++ b/sam3/perflib/fused.py
@@ -10,6 +10,7 @@
 def addmm_act(activation, linear, mat1):
     if torch.is_grad_enabled():
         raise ValueError("Expected grad to be disabled.")
+    orig_dtype = mat1.dtype
     self = linear.bias.detach()
     mat2 = linear.weight.detach()
     self = self.to(torch.bfloat16)
@@ -18,8 +19,8 @@ def addmm_act(activation, linear, mat1):
     mat1_flat = mat1.view(-1, mat1.shape[-1])
     if activation in [torch.nn.functional.relu, torch.nn.ReLU]:
         y = addmm_act_op(self, mat1_flat, mat2.t(), beta=1, alpha=1, use_gelu=False)
-        return y.view(mat1.shape[:-1] + (y.shape[-1],))
+        return y.view(mat1.shape[:-1] + (y.shape[-1],)).to(orig_dtype)
     if activation in [torch.nn.functional.gelu, torch.nn.GELU]:
         y = addmm_act_op(self, mat1_flat, mat2.t(), beta=1, alpha=1, use_gelu=True)
-        return y.view(mat1.shape[:-1] + (y.shape[-1],))
+        return y.view(mat1.shape[:-1] + (y.shape[-1],)).to(orig_dtype)
     raise ValueError(f"Unexpected activation {activation}")