diff --git a/whisper/whisper/load_models.py b/whisper/whisper/load_models.py
index c92d9042..0d21ac1e 100644
--- a/whisper/whisper/load_models.py
+++ b/whisper/whisper/load_models.py
@@ -7,14 +7,22 @@ import mlx.core as mx
 import mlx.nn as nn
 from mlx.utils import tree_unflatten
 
+from huggingface_hub import snapshot_download
+
 from . import whisper
 
 
 def load_model(
-    folder: str,
+    path_or_hf_repo: str,
     dtype: mx.Dtype = mx.float32,
 ) -> whisper.Whisper:
-    model_path = Path(folder)
+    model_path = Path(path_or_hf_repo)
+    if not model_path.exists():
+        model_path = Path(
+            snapshot_download(
+                repo_id=path_or_hf_repo
+            )
+        )
 
     with open(str(model_path / "config.json"), "r") as f:
         config = json.loads(f.read())
diff --git a/whisper/whisper/transcribe.py b/whisper/whisper/transcribe.py
index 704fd36c..05ff1fd1 100644
--- a/whisper/whisper/transcribe.py
+++ b/whisper/whisper/transcribe.py
@@ -62,7 +62,7 @@ class ModelHolder:
 def transcribe(
     audio: Union[str, np.ndarray, mx.array],
     *,
-    model_path: str = "mlx_models/tiny",
+    path_or_hf_repo: str = "mlx_models",
     verbose: Optional[bool] = None,
     temperature: Union[float, Tuple[float, ...]] = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0),
     compression_ratio_threshold: Optional[float] = 2.4,
@@ -85,8 +85,8 @@ def transcribe(
     audio: Union[str, np.ndarray, mx.array]
         The path to the audio file to open, or the audio waveform
 
-    model_path: str
-        The path to the Whisper model that has been converted to MLX format.
+    path_or_hf_repo: str
+        The localpath to the Whisper model or HF Hub repo with the MLX converted weights.
 
     verbose: bool
         Whether to display the text being decoded to the console. If True, displays all the details,
@@ -144,7 +144,7 @@ def transcribe(
     """
 
     dtype = mx.float16 if decode_options.get("fp16", True) else mx.float32
-    model = ModelHolder.get_model(model_path, dtype)
+    model = ModelHolder.get_model(path_or_hf_repo, dtype)
 
     # Pad 30-seconds of silence to the input audio, for slicing
     mel = log_mel_spectrogram(audio, n_mels=model.dims.n_mels, padding=N_SAMPLES)