diff --git a/buzz/model_loader.py b/buzz/model_loader.py
index 08d15730..6a9aed69 100644
--- a/buzz/model_loader.py
+++ b/buzz/model_loader.py
@@ -100,6 +100,10 @@ class ModelType(enum.Enum):
 
 HUGGING_FACE_MODEL_ALLOW_PATTERNS = [
     "model.safetensors",  # largest by size first
+    "pytorch_model.bin",
+    "model-00001-of-00002.safetensors",
+    "model-00002-of-00002.safetensors",
+    "model.safetensors.index.json",
     "added_tokens.json",
     "config.json",
     "generation_config.json",
@@ -218,7 +222,8 @@ class TranscriptionModel:
                     self.hugging_face_model_id,
                     allow_patterns=HUGGING_FACE_MODEL_ALLOW_PATTERNS,
                     local_files_only=True,
-                    cache_dir=model_root_dir
+                    cache_dir=model_root_dir,
+                    etag_timeout=60
                 )
             except (ValueError, FileNotFoundError):
                 return None
@@ -324,7 +329,8 @@ def download_from_huggingface(
         model_root = huggingface_hub.snapshot_download(
             repo_id,
             allow_patterns=allow_patterns[num_large_files:],  # all, but largest
-            cache_dir=model_root_dir
+            cache_dir=model_root_dir,
+            etag_timeout=60
         )
     except Exception as exc:
         logging.exception(exc)
@@ -351,7 +357,8 @@ def download_from_huggingface(
         huggingface_hub.snapshot_download(
             repo_id,
             allow_patterns=allow_patterns[:num_large_files],  # largest
-            cache_dir=model_root_dir
+            cache_dir=model_root_dir,
+            etag_timeout=60
         )
     except Exception as exc:
         logging.exception(exc)
@@ -399,7 +406,8 @@ def download_faster_whisper_model(
             repo_id,
             allow_patterns=allow_patterns,
             local_files_only=True,
-            cache_dir=model_root_dir
+            cache_dir=model_root_dir,
+            etag_timeout=60
         )
 
     return download_from_huggingface(
@@ -469,6 +477,7 @@ class ModelDownloader(QRunnable):
                 self.model.hugging_face_model_id,
                 allow_patterns=HUGGING_FACE_MODEL_ALLOW_PATTERNS,
                 progress=self.signals.progress,
+                num_large_files=4
             )
             self.signals.finished.emit(model_path)
             return
diff --git a/buzz/transformers_whisper.py b/buzz/transformers_whisper.py
index 270c8440..80d1fe12 100644
--- a/buzz/transformers_whisper.py
+++ b/buzz/transformers_whisper.py
@@ -1,7 +1,7 @@
-from typing import Optional, Union
-
+import os
 import numpy as np
 import torch
+from typing import Optional, Union
 from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
 
 
@@ -17,12 +17,14 @@ class TransformersWhisper:
         language: str,
         task: str,
     ):
-
         device = "cuda" if torch.cuda.is_available() else "cpu"
         torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
 
+        safetensors_path = os.path.join(self.model_id, "model.safetensors")
+        use_safetensors = os.path.exists(safetensors_path)
+
         model = AutoModelForSpeechSeq2Seq.from_pretrained(
-            self.model_id, torch_dtype=torch_dtype, use_safetensors=True
+            self.model_id, torch_dtype=torch_dtype, use_safetensors=use_safetensors
         )
 
         model.generation_config.language = language
diff --git a/buzz/widgets/model_download_progress_dialog.py b/buzz/widgets/model_download_progress_dialog.py
index ca5b9a8b..e2a41dce 100644
--- a/buzz/widgets/model_download_progress_dialog.py
+++ b/buzz/widgets/model_download_progress_dialog.py
@@ -35,11 +35,11 @@ class ModelDownloadProgressDialog(QProgressDialog):
     def update_label_text(self, fraction_completed: float):
         downloading_text = _("Downloading model")
         remaining_text = _("remaining")
-        label_text = f"{downloading_text} ({fraction_completed:.0%}"
+        label_text = f"{downloading_text} ("
         if fraction_completed > 0:
             time_spent = (datetime.now() - self.start_time).total_seconds()
             time_left = (time_spent / fraction_completed) - time_spent
-            label_text += f", {humanize.naturaldelta(time_left)} {remaining_text}"
+            label_text += f"{humanize.naturaldelta(time_left)} {remaining_text}"
         label_text += ")"
 
         self.setLabelText(label_text)