diff --git a/sam3/model_builder.py b/sam3/model_builder.py
index d4c67de..058bbec 100644
--- a/sam3/model_builder.py
+++ b/sam3/model_builder.py
@@ -5,10 +5,8 @@ from typing import Optional
 
 import torch
 import torch.nn as nn
-
 from huggingface_hub import hf_hub_download
 from iopath.common.file_io import g_pathmgr
-
 from sam3.model.decoder import (
     TransformerDecoder,
     TransformerDecoderLayer,
@@ -32,9 +30,7 @@ from sam3.model.model_misc import (
 )
 from sam3.model.necks import Sam3DualViTDetNeck
 from sam3.model.position_encoding import PositionEmbeddingSine
-
 from sam3.model.sam1_task_predictor import SAM3InteractiveImagePredictor
-
 from sam3.model.sam3_image import Sam3Image, Sam3ImageOnVideoMultiGPU
 from sam3.model.sam3_tracking_predictor import Sam3TrackerPredictor
 from sam3.model.sam3_video_inference import Sam3VideoInferenceWithInstanceInteractivity
@@ -45,9 +41,6 @@ from sam3.model.vitdet import ViT
 from sam3.model.vl_combiner import SAM3VLBackbone
 from sam3.sam.transformer import RoPEAttention
 
-SAM3_MODEL_ID = "facebook/sam3"
-SAM3_CKPT_NAME = "sam3.pt"
-
 
 # Setup TensorFloat-32 for Ampere GPUs if available
 def _setup_tf32() -> None:
@@ -633,9 +626,7 @@ def build_sam3_image_model(
         eval_mode,
     )
     if load_from_HF and checkpoint_path is None:
-        checkpoint_path = hf_hub_download(
-            repo_id=SAM3_MODEL_ID, filename=SAM3_CKPT_NAME
-        )
+        checkpoint_path = download_ckpt_from_hf()
     # Load checkpoint if provided
     if checkpoint_path is not None:
         _load_checkpoint(model, checkpoint_path)
@@ -646,6 +637,15 @@ def build_sam3_image_model(
     return model
 
 
+def download_ckpt_from_hf():
+    SAM3_MODEL_ID = "facebook/sam3"
+    SAM3_CKPT_NAME = "sam3.pt"
+    SAM3_CFG_NAME = "config.json"
+    _ = hf_hub_download(repo_id=SAM3_MODEL_ID, filename=SAM3_CFG_NAME)
+    checkpoint_path = hf_hub_download(repo_id=SAM3_MODEL_ID, filename=SAM3_CKPT_NAME)
+    return checkpoint_path
+
+
 def build_sam3_video_model(
     checkpoint_path: Optional[str] = None,
     load_from_HF=True,
@@ -768,9 +768,7 @@ def build_sam3_video_model(
 
     # Load checkpoint if provided
     if load_from_HF and checkpoint_path is None:
-        checkpoint_path = hf_hub_download(
-            repo_id=SAM3_MODEL_ID, filename=SAM3_CKPT_NAME
-        )
+        checkpoint_path = download_ckpt_from_hf()
     if checkpoint_path is not None:
         with g_pathmgr.open(checkpoint_path, "rb") as f:
             ckpt = torch.load(f, map_location="cpu", weights_only=True)