diff --git a/optimum/habana/checkpoint_utils.py b/optimum/habana/checkpoint_utils.py
index 1a14c52b2c..8cf5070b34 100644
--- a/optimum/habana/checkpoint_utils.py
+++ b/optimum/habana/checkpoint_utils.py
@@ -3,8 +3,8 @@
 from pathlib import Path
 
 import torch
-import transformers
-from huggingface_hub import snapshot_download
+from huggingface_hub import list_repo_files, snapshot_download
+from transformers import modeling_utils
 from transformers.utils import is_offline_mode
 
 
@@ -22,7 +22,12 @@ def get_repo_root(model_name_or_path, local_rank=-1, token=None):
                 print("Offline mode: forcing local_files_only=True")
 
         # Only download PyTorch weights by default
-        allow_patterns = ["*.bin"]
+        if any(".bin" in filename for filename in list_repo_files(model_name_or_path, token=token)):
+            allow_patterns = ["*.bin"]
+        elif any(
+            ".safetensors" in filename for filename in list_repo_files(model_name_or_path, token=token)
+        ):  # Some models like Falcon-180b are in only safetensors format
+            allow_patterns = ["*.safetensors"]
 
         # Download only on first process
         if local_rank in [-1, 0]:
@@ -52,45 +57,25 @@ def get_repo_root(model_name_or_path, local_rank=-1, token=None):
 
 
 def get_checkpoint_files(model_name_or_path, local_rank, token=None):
-    """
-    Gets the list of files for the specified model checkpoint.
-    """
     cached_repo_dir = get_repo_root(model_name_or_path, local_rank=local_rank, token=token)
 
-    # Logic for loading individual weights from https://github.com/huggingface/transformers/blob/abbffc4525566a48a9733639797c812301218b83/src/transformers/trainer.py#L2061
-    individual_weights = [
-        os.path.join(cached_repo_dir, weight_name)
-        for weight_name in (
-            transformers.modeling_utils.SAFE_WEIGHTS_NAME,
-            transformers.modeling_utils.WEIGHTS_NAME,
-        )
-    ]
-    checkpoint_files = []
-    for weight_file in individual_weights:
-        if os.path.isfile(weight_file):
-            checkpoint_files.append(weight_file)
-            break
-    if checkpoint_files:
-        return checkpoint_files
-
-    # Code for loading sharded weights copied from https://github.com/huggingface/transformers/blob/abbffc4525566a48a9733639797c812301218b83/src/transformers/modeling_utils.py#L414
-    index_file = os.path.join(cached_repo_dir, transformers.modeling_utils.WEIGHTS_INDEX_NAME)
-    safe_index_file = os.path.join(cached_repo_dir, transformers.modeling_utils.SAFE_WEIGHTS_INDEX_NAME)
+    # Extensions: .bin | .safetensors | .pt
+    # Creates a list of paths from all downloaded files in cache dir
 
-    index_present = os.path.isfile(index_file)
-    safe_index_present = os.path.isfile(safe_index_file)
-
-    if not index_present and not safe_index_present:
-        filenames = (transformers.modeling_utils.WEIGHTS_INDEX_NAME, transformers.modeling_utils.SAFE_WEIGHTS_INDEX_NAME)
-        raise ValueError(f"Can't find a checkpoint index ({' or '.join(filenames)}) in {cached_repo_dir}.")
-
-    load_index = safe_index_file if safe_index_present else index_file
+    if any(file.suffix == ".bin" for file in Path(cached_repo_dir).rglob("*")):
+        (name, ext) = os.path.splitext(modeling_utils.WEIGHTS_NAME)
+    elif any(file.suffix == ".safetensors" for file in Path(cached_repo_dir).rglob("*")):
+        (name, ext) = os.path.splitext(modeling_utils.SAFE_WEIGHTS_NAME)
+    else:
+        (name, ext) = ("*", ".pt")
 
-    with open(load_index, "r", encoding="utf-8") as f:
-        index = json.load(f)
+    file_list = [
+        str(entry)
+        for entry in Path(cached_repo_dir).rglob("*")
+        if (entry.is_file() and entry.name.startswith(name) and entry.name.endswith(ext))
+    ]
 
-    file_list = set(index["weight_map"].values())
-    return [os.path.join(cached_repo_dir, entry) for entry in file_list]
+    return file_list
 
 
 def write_checkpoints_json(model_name_or_path, local_rank, f, token=None):