diff --git a/examples/flax/image-captioning/run_image_captioning_flax.py b/examples/flax/image-captioning/run_image_captioning_flax.py
index 652bb3be45474..640919f7bff87 100644
--- a/examples/flax/image-captioning/run_image_captioning_flax.py
+++ b/examples/flax/image-captioning/run_image_captioning_flax.py
@@ -36,14 +36,14 @@
 import nltk  # Here to have a nice missing dependency error message early on
 import numpy as np
 import optax
+from PIL import Image
 from datasets import Dataset, load_dataset
 from filelock import FileLock
 from flax import jax_utils, traverse_util
 from flax.jax_utils import unreplicate
 from flax.training import train_state
 from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key
-from huggingface_hub import Repository, create_repo
-from PIL import Image
+from huggingface_hub import HfApi
 from tqdm import tqdm
 
 import transformers
@@ -455,9 +455,8 @@ def main():
         if repo_name is None:
             repo_name = Path(training_args.output_dir).absolute().name
         # Create repo and retrieve repo_id
-        repo_id = create_repo(repo_name, exist_ok=True, token=training_args.hub_token).repo_id
-        # Clone repo locally
-        repo = Repository(training_args.output_dir, clone_from=repo_id, token=training_args.hub_token)
+        api = HfApi()
+        repo_id = api.create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
 
     # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
     # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
@@ -1052,7 +1051,7 @@ def generate_step(params, batch):
     if not os.path.isdir(os.path.join(training_args.output_dir)):
         os.makedirs(os.path.join(training_args.output_dir), exist_ok=True)
 
-    def save_ckpt(ckpt_dir: str, commit_msg: str = ""):
+    def save_ckpt(ckpt_dir: str, commit_msg: str):
         """save checkpoints and push to Hugging Face Hub if specified"""
 
         # save checkpoint after each epoch and push checkpoint to the hub
@@ -1061,7 +1060,13 @@ def save_ckpt(ckpt_dir: str, commit_msg: str = ""):
             model.save_pretrained(os.path.join(training_args.output_dir, ckpt_dir), params=params)
             tokenizer.save_pretrained(os.path.join(training_args.output_dir, ckpt_dir))
             if training_args.push_to_hub:
-                repo.push_to_hub(commit_message=commit_msg, blocking=False)
+                api.upload_folder(
+                    commit_message=commit_msg,
+                    folder_path=training_args.output_dir,
+                    repo_id=repo_id,
+                    repo_type="model",
+                    token=training_args.hub_token,
+                )
 
     def evaluation_loop(
         rng: jax.random.PRNGKey,
diff --git a/examples/flax/language-modeling/run_bart_dlm_flax.py b/examples/flax/language-modeling/run_bart_dlm_flax.py
index f5369299a6d4c..e5cbe5cd0fdba 100644
--- a/examples/flax/language-modeling/run_bart_dlm_flax.py
+++ b/examples/flax/language-modeling/run_bart_dlm_flax.py
@@ -44,7 +44,7 @@
 from flax.jax_utils import pad_shard_unpad
 from flax.training import train_state
 from flax.training.common_utils import get_metrics, onehot, shard
-from huggingface_hub import Repository, create_repo
+from huggingface_hub import HfApi
 from tqdm import tqdm
 
 from transformers import (
@@ -517,9 +517,8 @@ def main():
         if repo_name is None:
             repo_name = Path(training_args.output_dir).absolute().name
         # Create repo and retrieve repo_id
-        repo_id = create_repo(repo_name, exist_ok=True, token=training_args.hub_token).repo_id
-        # Clone repo locally
-        repo = Repository(training_args.output_dir, clone_from=repo_id, token=training_args.hub_token)
+        api = HfApi()
+        repo_id = api.create_repo(repo_name, exist_ok=True, token=training_args.hub_token).repo_id
 
     # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
     # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
@@ -949,7 +948,13 @@ def eval_step(params, batch):
                     model.save_pretrained(training_args.output_dir, params=params)
                     tokenizer.save_pretrained(training_args.output_dir)
                     if training_args.push_to_hub:
-                        repo.push_to_hub(commit_message=f"Saving weights and logs of step {cur_step}", blocking=False)
+                        api.upload_folder(
+                            commit_message=f"Saving weights and logs of step {cur_step}",
+                            folder_path=training_args.output_dir,
+                            repo_id=repo_id,
+                            repo_type="model",
+                            token=training_args.hub_token,
+                        )
 
     # Eval after training
     if training_args.do_eval:
diff --git a/examples/flax/language-modeling/run_clm_flax.py b/examples/flax/language-modeling/run_clm_flax.py
index 5ef17be35322c..48bad1a04c6d1 100755
--- a/examples/flax/language-modeling/run_clm_flax.py
+++ b/examples/flax/language-modeling/run_clm_flax.py
@@ -44,7 +44,7 @@
 from flax.jax_utils import pad_shard_unpad, unreplicate
 from flax.training import train_state
 from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key
-from huggingface_hub import Repository, create_repo
+from huggingface_hub import HfApi
 from tqdm import tqdm
 
 import transformers
@@ -403,9 +403,8 @@ def main():
         if repo_name is None:
             repo_name = Path(training_args.output_dir).absolute().name
         # Create repo and retrieve repo_id
-        repo_id = create_repo(repo_name, exist_ok=True, token=training_args.hub_token).repo_id
-        # Clone repo locally
-        repo = Repository(training_args.output_dir, clone_from=repo_id, token=training_args.hub_token)
+        api = HfApi()
+        repo_id = api.create_repo(repo_name, exist_ok=True, token=training_args.hub_token).repo_id
 
     #  Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
     # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
@@ -847,8 +846,13 @@ def eval_step(params, batch):
                     model.save_pretrained(training_args.output_dir, params=params)
                     tokenizer.save_pretrained(training_args.output_dir)
                     if training_args.push_to_hub:
-                        repo.push_to_hub(commit_message=f"Saving weights and logs of step {cur_step}", blocking=False)
-
+                        api.upload_folder(
+                            commit_message=f"Saving weights and logs of step {cur_step}",
+                            folder_path=training_args.output_dir,
+                            repo_id=repo_id,
+                            repo_type="model",
+                            token=training_args.hub_token,
+                        )
     # Eval after training
     if training_args.do_eval:
         eval_metrics = []
diff --git a/examples/flax/language-modeling/run_mlm_flax.py b/examples/flax/language-modeling/run_mlm_flax.py
index 86415578138ae..ccd0f2bf20d97 100755
--- a/examples/flax/language-modeling/run_mlm_flax.py
+++ b/examples/flax/language-modeling/run_mlm_flax.py
@@ -45,7 +45,7 @@
 from flax.jax_utils import pad_shard_unpad
 from flax.training import train_state
 from flax.training.common_utils import get_metrics, onehot, shard
-from huggingface_hub import Repository, create_repo
+from huggingface_hub import HfApi
 from tqdm import tqdm
 
 from transformers import (
@@ -441,9 +441,8 @@ def main():
         if repo_name is None:
             repo_name = Path(training_args.output_dir).absolute().name
         # Create repo and retrieve repo_id
-        repo_id = create_repo(repo_name, exist_ok=True, token=training_args.hub_token).repo_id
-        # Clone repo locally
-        repo = Repository(training_args.output_dir, clone_from=repo_id, token=training_args.hub_token)
+        api = HfApi()
+        repo_id = api.create_repo(repo_name, exist_ok=True, token=training_args.hub_token).repo_id
 
     # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
     # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
@@ -890,8 +889,13 @@ def eval_step(params, batch):
                     model.save_pretrained(training_args.output_dir, params=params)
                     tokenizer.save_pretrained(training_args.output_dir)
                     if training_args.push_to_hub:
-                        repo.push_to_hub(commit_message=f"Saving weights and logs of step {cur_step}", blocking=False)
-
+                        api.upload_folder(
+                            commit_message=f"Saving weights and logs of step {cur_step}",
+                            folder_path=training_args.output_dir,
+                            repo_id=repo_id,
+                            repo_type="model",
+                            token=training_args.hub_token,
+                        )
     # Eval after training
     if training_args.do_eval:
         num_eval_samples = len(tokenized_datasets["validation"])
diff --git a/examples/flax/language-modeling/run_t5_mlm_flax.py b/examples/flax/language-modeling/run_t5_mlm_flax.py
index fa6a5742236ca..06384deac457f 100755
--- a/examples/flax/language-modeling/run_t5_mlm_flax.py
+++ b/examples/flax/language-modeling/run_t5_mlm_flax.py
@@ -44,7 +44,7 @@
 from flax.jax_utils import pad_shard_unpad
 from flax.training import train_state
 from flax.training.common_utils import get_metrics, onehot, shard
-from huggingface_hub import Repository, create_repo
+from huggingface_hub import HfApi
 from tqdm import tqdm
 
 from transformers import (
@@ -558,9 +558,8 @@ def main():
         if repo_name is None:
             repo_name = Path(training_args.output_dir).absolute().name
         # Create repo and retrieve repo_id
-        repo_id = create_repo(repo_name, exist_ok=True, token=training_args.hub_token).repo_id
-        # Clone repo locally
-        repo = Repository(training_args.output_dir, clone_from=repo_id, token=training_args.hub_token)
+        api = HfApi()
+        repo_id = api.create_repo(repo_name, exist_ok=True, token=training_args.hub_token).repo_id
 
     # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
     # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
@@ -977,8 +976,13 @@ def eval_step(params, batch):
                     model.save_pretrained(training_args.output_dir, params=params)
                     tokenizer.save_pretrained(training_args.output_dir)
                     if training_args.push_to_hub:
-                        repo.push_to_hub(commit_message=f"Saving weights and logs of step {cur_step}", blocking=False)
-
+                        api.upload_folder(
+                            commit_message=f"Saving weights and logs of step {cur_step}",
+                            folder_path=training_args.output_dir,
+                            repo_id=repo_id,
+                            repo_type="model",
+                            token=training_args.hub_token,
+                        )
     # Eval after training
     if training_args.do_eval:
         num_eval_samples = len(tokenized_datasets["validation"])
diff --git a/examples/flax/question-answering/run_qa.py b/examples/flax/question-answering/run_qa.py
index 0a9e98a52dd5b..3b9c352399573 100644
--- a/examples/flax/question-answering/run_qa.py
+++ b/examples/flax/question-answering/run_qa.py
@@ -42,7 +42,7 @@
 from flax.jax_utils import pad_shard_unpad, replicate, unreplicate
 from flax.training import train_state
 from flax.training.common_utils import get_metrics, onehot, shard
-from huggingface_hub import Repository, create_repo
+from huggingface_hub import HfApi
 from tqdm import tqdm
 from utils_qa import postprocess_qa_predictions
 
@@ -493,9 +493,8 @@ def main():
         if repo_name is None:
             repo_name = Path(training_args.output_dir).absolute().name
         # Create repo and retrieve repo_id
-        repo_id = create_repo(repo_name, exist_ok=True, token=training_args.hub_token).repo_id
-        # Clone repo locally
-        repo = Repository(training_args.output_dir, clone_from=repo_id, token=training_args.hub_token)
+        api = HfApi()
+        repo_id = api.create_repo(repo_name, exist_ok=True, token=training_args.hub_token).repo_id
 
     # region Load Data
     # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
@@ -1051,7 +1050,13 @@ def eval_step(state, batch):
                     model.save_pretrained(training_args.output_dir, params=params)
                     tokenizer.save_pretrained(training_args.output_dir)
                     if training_args.push_to_hub:
-                        repo.push_to_hub(commit_message=f"Saving weights and logs of step {cur_step}", blocking=False)
+                        api.upload_folder(
+                            commit_message=f"Saving weights and logs of step {cur_step}",
+                            folder_path=training_args.output_dir,
+                            repo_id=repo_id,
+                            repo_type="model",
+                            token=training_args.hub_token,
+                        )
         epochs.desc = f"Epoch ... {epoch + 1}/{num_epochs}"
     # endregion
 
diff --git a/examples/flax/speech-recognition/run_flax_speech_recognition_seq2seq.py b/examples/flax/speech-recognition/run_flax_speech_recognition_seq2seq.py
index 7df04fe6c4943..f6037c3321575 100644
--- a/examples/flax/speech-recognition/run_flax_speech_recognition_seq2seq.py
+++ b/examples/flax/speech-recognition/run_flax_speech_recognition_seq2seq.py
@@ -39,7 +39,7 @@
 from flax.jax_utils import pad_shard_unpad, unreplicate
 from flax.training import train_state
 from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key
-from huggingface_hub import Repository, create_repo
+from huggingface_hub import HfApi
 from torch.utils.data import DataLoader
 from tqdm import tqdm
 
@@ -427,8 +427,9 @@ def main():
             )
         else:
             repo_name = training_args.hub_model_id
-        create_repo(repo_name, exist_ok=True, token=training_args.hub_token)
-        repo = Repository(training_args.output_dir, clone_from=repo_name, token=training_args.hub_token)
+        # Create repo and retrieve repo_id
+        api = HfApi()
+        repo_id = api.create_repo(repo_name, exist_ok=True, token=training_args.hub_token).repo_id
 
     # 3. Load dataset
     raw_datasets = DatasetDict()
@@ -852,7 +853,13 @@ def generate_step(params, batch):
             model.save_pretrained(training_args.output_dir, params=params)
             tokenizer.save_pretrained(training_args.output_dir)
             if training_args.push_to_hub:
-                repo.push_to_hub(commit_message=f"Saving weights and logs of epoch {epoch}", blocking=False)
+                api.upload_folder(
+                    commit_message=f"Saving weights and logs of epoch {epoch}",
+                    folder_path=training_args.output_dir,
+                    repo_id=repo_id,
+                    repo_type="model",
+                    token=training_args.hub_token,
+                )
 
 
 if __name__ == "__main__":
diff --git a/examples/flax/summarization/run_summarization_flax.py b/examples/flax/summarization/run_summarization_flax.py
index 1a72cc65225c5..391cd8cba77f6 100644
--- a/examples/flax/summarization/run_summarization_flax.py
+++ b/examples/flax/summarization/run_summarization_flax.py
@@ -44,7 +44,7 @@
 from flax.jax_utils import pad_shard_unpad, unreplicate
 from flax.training import train_state
 from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key
-from huggingface_hub import Repository, create_repo
+from huggingface_hub import HfApi
 from tqdm import tqdm
 
 import transformers
@@ -483,9 +483,8 @@ def main():
         if repo_name is None:
             repo_name = Path(training_args.output_dir).absolute().name
         # Create repo and retrieve repo_id
-        repo_id = create_repo(repo_name, exist_ok=True, token=training_args.hub_token).repo_id
-        # Clone repo locally
-        repo = Repository(training_args.output_dir, clone_from=repo_id, token=training_args.hub_token)
+        api = HfApi()
+        repo_id = api.create_repo(repo_name, exist_ok=True, token=training_args.hub_token).repo_id
 
     # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
     # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
@@ -976,7 +975,13 @@ def generate_step(params, batch):
             model.save_pretrained(training_args.output_dir, params=params)
             tokenizer.save_pretrained(training_args.output_dir)
             if training_args.push_to_hub:
-                repo.push_to_hub(commit_message=f"Saving weights and logs of epoch {epoch}", blocking=False)
+                api.upload_folder(
+                    commit_message=f"Saving weights and logs of epoch {epoch}",
+                    folder_path=training_args.output_dir,
+                    repo_id=repo_id,
+                    repo_type="model",
+                    token=training_args.hub_token,
+                )
 
     # ======================== Prediction loop ==============================
     if training_args.do_predict:
diff --git a/examples/flax/text-classification/run_flax_glue.py b/examples/flax/text-classification/run_flax_glue.py
index 0167bfdd26f9b..d60a87251800e 100755
--- a/examples/flax/text-classification/run_flax_glue.py
+++ b/examples/flax/text-classification/run_flax_glue.py
@@ -37,7 +37,7 @@
 from flax.jax_utils import pad_shard_unpad, replicate, unreplicate
 from flax.training import train_state
 from flax.training.common_utils import get_metrics, onehot, shard
-from huggingface_hub import Repository, create_repo
+from huggingface_hub import HfApi
 from tqdm import tqdm
 
 import transformers
@@ -373,9 +373,8 @@ def main():
         if repo_name is None:
             repo_name = Path(training_args.output_dir).absolute().name
         # Create repo and retrieve repo_id
-        repo_id = create_repo(repo_name, exist_ok=True, token=training_args.hub_token).repo_id
-        # Clone repo locally
-        repo = Repository(training_args.output_dir, clone_from=repo_id, token=training_args.hub_token)
+        api = HfApi()
+        repo_id = api.create_repo(repo_name, exist_ok=True, token=training_args.hub_token).repo_id
 
     # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
     # or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
@@ -677,7 +676,13 @@ def eval_step(state, batch):
                     model.save_pretrained(training_args.output_dir, params=params)
                     tokenizer.save_pretrained(training_args.output_dir)
                     if training_args.push_to_hub:
-                        repo.push_to_hub(commit_message=f"Saving weights and logs of step {cur_step}", blocking=False)
+                        api.upload_folder(
+                            commit_message=f"Saving weights and logs of epoch {epoch}",
+                            folder_path=training_args.output_dir,
+                            repo_id=repo_id,
+                            repo_type="model",
+                            token=training_args.hub_token,
+                        )
             epochs.desc = f"Epoch ... {epoch + 1}/{num_epochs}"
 
     # save the eval metrics in json
diff --git a/examples/flax/token-classification/run_flax_ner.py b/examples/flax/token-classification/run_flax_ner.py
index b73ec4810308a..f021402f09d63 100644
--- a/examples/flax/token-classification/run_flax_ner.py
+++ b/examples/flax/token-classification/run_flax_ner.py
@@ -39,7 +39,7 @@
 from flax.jax_utils import pad_shard_unpad, replicate, unreplicate
 from flax.training import train_state
 from flax.training.common_utils import get_metrics, onehot, shard
-from huggingface_hub import Repository, create_repo
+from huggingface_hub import HfApi
 from tqdm import tqdm
 
 import transformers
@@ -429,9 +429,8 @@ def main():
         if repo_name is None:
             repo_name = Path(training_args.output_dir).absolute().name
         # Create repo and retrieve repo_id
-        repo_id = create_repo(repo_name, exist_ok=True, token=training_args.hub_token).repo_id
-        # Clone repo locally
-        repo = Repository(training_args.output_dir, clone_from=repo_id, token=training_args.hub_token)
+        api = HfApi()
+        repo_id = api.create_repo(repo_name, exist_ok=True, token=training_args.hub_token).repo_id
 
     # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
     # or just provide the name of one of the public datasets for token classification task available on the hub at https://huggingface.co/datasets/
@@ -798,7 +797,13 @@ def compute_metrics():
                     model.save_pretrained(training_args.output_dir, params=params)
                     tokenizer.save_pretrained(training_args.output_dir)
                     if training_args.push_to_hub:
-                        repo.push_to_hub(commit_message=f"Saving weights and logs of step {cur_step}", blocking=False)
+                        api.upload_folder(
+                            commit_message=f"Saving weights and logs of step {cur_step}",
+                            folder_path=training_args.output_dir,
+                            repo_id=repo_id,
+                            repo_type="model",
+                            token=training_args.hub_token,
+                        )
         epochs.desc = f"Epoch ... {epoch + 1}/{num_epochs}"
 
     # Eval after training
diff --git a/examples/flax/vision/run_image_classification.py b/examples/flax/vision/run_image_classification.py
index 364ac7dd2d093..d8011867957cd 100644
--- a/examples/flax/vision/run_image_classification.py
+++ b/examples/flax/vision/run_image_classification.py
@@ -42,7 +42,7 @@
 from flax.jax_utils import pad_shard_unpad, unreplicate
 from flax.training import train_state
 from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key
-from huggingface_hub import Repository, create_repo
+from huggingface_hub import HfApi
 from tqdm import tqdm
 
 import transformers
@@ -324,9 +324,8 @@ def main():
         if repo_name is None:
             repo_name = Path(training_args.output_dir).absolute().name
         # Create repo and retrieve repo_id
-        repo_id = create_repo(repo_name, exist_ok=True, token=training_args.hub_token).repo_id
-        # Clone repo locally
-        repo = Repository(training_args.output_dir, clone_from=repo_id, token=training_args.hub_token)
+        api = HfApi()
+        repo_id = api.create_repo(repo_name, exist_ok=True, token=training_args.hub_token).repo_id
 
     # Initialize datasets and pre-processing transforms
     # We use torchvision here for faster pre-processing
@@ -595,7 +594,13 @@ def eval_step(params, batch):
             params = jax.device_get(jax.tree_util.tree_map(lambda x: x[0], state.params))
             model.save_pretrained(training_args.output_dir, params=params)
             if training_args.push_to_hub:
-                repo.push_to_hub(commit_message=f"Saving weights and logs of epoch {epoch}", blocking=False)
+                api.upload_folder(
+                    commit_message=f"Saving weights and logs of epoch {epoch}",
+                    folder_path=training_args.output_dir,
+                    repo_id=repo_id,
+                    repo_type="model",
+                    token=training_args.hub_token,
+                )
 
 
 if __name__ == "__main__":
diff --git a/examples/pytorch/image-classification/run_image_classification_no_trainer.py b/examples/pytorch/image-classification/run_image_classification_no_trainer.py
index 9dd9acace50a7..124cc9215e94c 100644
--- a/examples/pytorch/image-classification/run_image_classification_no_trainer.py
+++ b/examples/pytorch/image-classification/run_image_classification_no_trainer.py
@@ -27,7 +27,7 @@
 from accelerate.logging import get_logger
 from accelerate.utils import set_seed
 from datasets import load_dataset
-from huggingface_hub import Repository, create_repo
+from huggingface_hub import HfApi
 from torch.utils.data import DataLoader
 from torchvision.transforms import (
     CenterCrop,
@@ -264,15 +264,9 @@ def main():
             if repo_name is None:
                 repo_name = Path(args.output_dir).absolute().name
             # Create repo and retrieve repo_id
-            repo_id = create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
-            # Clone repo locally
-            repo = Repository(args.output_dir, clone_from=repo_id, token=args.hub_token)
-
-            with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
-                if "step_*" not in gitignore:
-                    gitignore.write("step_*\n")
-                if "epoch_*" not in gitignore:
-                    gitignore.write("epoch_*\n")
+            api = HfApi()
+            repo_id = api.create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
+
         elif args.output_dir is not None:
             os.makedirs(args.output_dir, exist_ok=True)
     accelerator.wait_for_everyone()
@@ -561,10 +555,12 @@ def collate_fn(examples):
                         )
                         if accelerator.is_main_process:
                             image_processor.save_pretrained(args.output_dir)
-                            repo.push_to_hub(
-                                commit_message=f"Training in progress {completed_steps} steps",
-                                blocking=False,
-                                auto_lfs_prune=True,
+                            api.upload_folder(
+                                commit_message=f"Training in progress epoch {epoch}",
+                                folder_path=args.output_dir,
+                                repo_id=repo_id,
+                                repo_type="model",
+                                token=args.hub_token,
                             )
 
             if completed_steps >= args.max_train_steps:
@@ -603,8 +599,12 @@ def collate_fn(examples):
             )
             if accelerator.is_main_process:
                 image_processor.save_pretrained(args.output_dir)
-                repo.push_to_hub(
-                    commit_message=f"Training in progress epoch {epoch}", blocking=False, auto_lfs_prune=True
+                api.upload_folder(
+                    commit_message=f"Training in progress epoch {epoch}",
+                    folder_path=args.output_dir,
+                    repo_id=repo_id,
+                    repo_type="model",
+                    token=args.hub_token,
                 )
 
         if args.checkpointing_steps == "epoch":
@@ -625,8 +625,13 @@ def collate_fn(examples):
         if accelerator.is_main_process:
             image_processor.save_pretrained(args.output_dir)
             if args.push_to_hub:
-                repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True)
-
+                api.upload_folder(
+                    commit_message="End of training",
+                    folder_path=args.output_dir,
+                    repo_id=repo_id,
+                    repo_type="model",
+                    token=args.hub_token,
+                )
             all_results = {f"eval_{k}": v for k, v in eval_metric.items()}
             with open(os.path.join(args.output_dir, "all_results.json"), "w") as f:
                 json.dump(all_results, f)
diff --git a/examples/pytorch/image-pretraining/run_mim_no_trainer.py b/examples/pytorch/image-pretraining/run_mim_no_trainer.py
index 1bca532f930c8..ecc1b1196f15c 100644
--- a/examples/pytorch/image-pretraining/run_mim_no_trainer.py
+++ b/examples/pytorch/image-pretraining/run_mim_no_trainer.py
@@ -26,7 +26,7 @@
 from accelerate import Accelerator, DistributedType
 from accelerate.utils import set_seed
 from datasets import load_dataset
-from huggingface_hub import Repository, create_repo
+from huggingface_hub import HfApi
 from torch.utils.data import DataLoader
 from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
 from tqdm.auto import tqdm
@@ -437,15 +437,9 @@ def main():
             if repo_name is None:
                 repo_name = Path(args.output_dir).absolute().name
             # Create repo and retrieve repo_id
-            repo_id = create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
-            # Clone repo locally
-            repo = Repository(args.output_dir, clone_from=repo_id, token=args.hub_token)
-
-            with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
-                if "step_*" not in gitignore:
-                    gitignore.write("step_*\n")
-                if "epoch_*" not in gitignore:
-                    gitignore.write("epoch_*\n")
+            api = HfApi()
+            repo_id = api.create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
+
         elif args.output_dir is not None:
             os.makedirs(args.output_dir, exist_ok=True)
     accelerator.wait_for_everyone()
@@ -781,8 +775,12 @@ def preprocess_images(examples):
             )
             if accelerator.is_main_process:
                 image_processor.save_pretrained(args.output_dir)
-                repo.push_to_hub(
-                    commit_message=f"Training in progress epoch {epoch}", blocking=False, auto_lfs_prune=True
+                api.upload_folder(
+                    commit_message=f"Training in progress epoch {epoch}",
+                    folder_path=args.output_dir,
+                    repo_id=repo_id,
+                    repo_type="model",
+                    token=args.hub_token,
                 )
 
         if args.checkpointing_steps == "epoch":
@@ -803,7 +801,13 @@ def preprocess_images(examples):
         if accelerator.is_main_process:
             image_processor.save_pretrained(args.output_dir)
             if args.push_to_hub:
-                repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True)
+                api.upload_folder(
+                    commit_message="End of training",
+                    folder_path=args.output_dir,
+                    repo_id=repo_id,
+                    repo_type="model",
+                    token=args.hub_token,
+                )
 
 
 if __name__ == "__main__":
diff --git a/examples/pytorch/language-modeling/run_clm_no_trainer.py b/examples/pytorch/language-modeling/run_clm_no_trainer.py
index e227997bc5739..2a2b91afa3719 100755
--- a/examples/pytorch/language-modeling/run_clm_no_trainer.py
+++ b/examples/pytorch/language-modeling/run_clm_no_trainer.py
@@ -37,7 +37,7 @@
 from accelerate.logging import get_logger
 from accelerate.utils import set_seed
 from datasets import load_dataset
-from huggingface_hub import Repository, create_repo
+from huggingface_hub import HfApi
 from torch.utils.data import DataLoader
 from tqdm.auto import tqdm
 
@@ -304,15 +304,8 @@ def main():
             if repo_name is None:
                 repo_name = Path(args.output_dir).absolute().name
             # Create repo and retrieve repo_id
-            repo_id = create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
-            # Clone repo locally
-            repo = Repository(args.output_dir, clone_from=repo_id, token=args.hub_token)
-
-            with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
-                if "step_*" not in gitignore:
-                    gitignore.write("step_*\n")
-                if "epoch_*" not in gitignore:
-                    gitignore.write("epoch_*\n")
+            api = HfApi()
+            repo_id = api.create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
         elif args.output_dir is not None:
             os.makedirs(args.output_dir, exist_ok=True)
     accelerator.wait_for_everyone()
@@ -682,8 +675,12 @@ def group_texts(examples):
             )
             if accelerator.is_main_process:
                 tokenizer.save_pretrained(args.output_dir)
-                repo.push_to_hub(
-                    commit_message=f"Training in progress epoch {epoch}", blocking=False, auto_lfs_prune=True
+                api.upload_folder(
+                    commit_message=f"Training in progress epoch {epoch}",
+                    folder_path=args.output_dir,
+                    repo_id=repo_id,
+                    repo_type="model",
+                    token=args.hub_token,
                 )
 
         if args.checkpointing_steps == "epoch":
@@ -704,8 +701,13 @@ def group_texts(examples):
         if accelerator.is_main_process:
             tokenizer.save_pretrained(args.output_dir)
             if args.push_to_hub:
-                repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True)
-
+                api.upload_folder(
+                    commit_message="End of training",
+                    folder_path=args.output_dir,
+                    repo_id=repo_id,
+                    repo_type="model",
+                    token=args.hub_token,
+                )
             with open(os.path.join(args.output_dir, "all_results.json"), "w") as f:
                 json.dump({"perplexity": perplexity}, f)
 
diff --git a/examples/pytorch/language-modeling/run_mlm_no_trainer.py b/examples/pytorch/language-modeling/run_mlm_no_trainer.py
index e002c999c883d..0eb690a98501d 100755
--- a/examples/pytorch/language-modeling/run_mlm_no_trainer.py
+++ b/examples/pytorch/language-modeling/run_mlm_no_trainer.py
@@ -37,7 +37,7 @@
 from accelerate.logging import get_logger
 from accelerate.utils import set_seed
 from datasets import load_dataset
-from huggingface_hub import Repository, create_repo
+from huggingface_hub import HfApi
 from torch.utils.data import DataLoader
 from tqdm.auto import tqdm
 
@@ -311,15 +311,8 @@ def main():
             if repo_name is None:
                 repo_name = Path(args.output_dir).absolute().name
             # Create repo and retrieve repo_id
-            repo_id = create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
-            # Clone repo locally
-            repo = Repository(args.output_dir, clone_from=repo_id, token=args.hub_token)
-
-            with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
-                if "step_*" not in gitignore:
-                    gitignore.write("step_*\n")
-                if "epoch_*" not in gitignore:
-                    gitignore.write("epoch_*\n")
+            api = HfApi()
+            repo_id = api.create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
         elif args.output_dir is not None:
             os.makedirs(args.output_dir, exist_ok=True)
     accelerator.wait_for_everyone()
@@ -720,8 +713,12 @@ def group_texts(examples):
             )
             if accelerator.is_main_process:
                 tokenizer.save_pretrained(args.output_dir)
-                repo.push_to_hub(
-                    commit_message=f"Training in progress epoch {epoch}", blocking=False, auto_lfs_prune=True
+                api.upload_folder(
+                    commit_message=f"Training in progress epoch {epoch}",
+                    folder_path=args.output_dir,
+                    repo_id=repo_id,
+                    repo_type="model",
+                    token=args.hub_token,
                 )
 
         if args.checkpointing_steps == "epoch":
@@ -742,8 +739,13 @@ def group_texts(examples):
         if accelerator.is_main_process:
             tokenizer.save_pretrained(args.output_dir)
             if args.push_to_hub:
-                repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True)
-
+                api.upload_folder(
+                    commit_message="End of training",
+                    folder_path=args.output_dir,
+                    repo_id=repo_id,
+                    repo_type="model",
+                    token=args.hub_token,
+                )
             with open(os.path.join(args.output_dir, "all_results.json"), "w") as f:
                 json.dump({"perplexity": perplexity}, f)
 
diff --git a/examples/pytorch/multiple-choice/run_swag_no_trainer.py b/examples/pytorch/multiple-choice/run_swag_no_trainer.py
index b7c3cb58bb646..da7d99526b5a9 100755
--- a/examples/pytorch/multiple-choice/run_swag_no_trainer.py
+++ b/examples/pytorch/multiple-choice/run_swag_no_trainer.py
@@ -36,7 +36,7 @@
 from accelerate.logging import get_logger
 from accelerate.utils import set_seed
 from datasets import load_dataset
-from huggingface_hub import Repository, create_repo
+from huggingface_hub import HfApi
 from torch.utils.data import DataLoader
 from tqdm.auto import tqdm
 
@@ -328,15 +328,8 @@ def main():
             if repo_name is None:
                 repo_name = Path(args.output_dir).absolute().name
             # Create repo and retrieve repo_id
-            repo_id = create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
-            # Clone repo locally
-            repo = Repository(args.output_dir, clone_from=repo_id, token=args.hub_token)
-
-            with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
-                if "step_*" not in gitignore:
-                    gitignore.write("step_*\n")
-                if "epoch_*" not in gitignore:
-                    gitignore.write("epoch_*\n")
+            api = HfApi()
+            repo_id = api.create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
         elif args.output_dir is not None:
             os.makedirs(args.output_dir, exist_ok=True)
     accelerator.wait_for_everyone()
diff --git a/examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py b/examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py
index 8cf216e5f4d0b..bd425a93bbc9e 100644
--- a/examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py
+++ b/examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py
@@ -34,7 +34,7 @@
 from accelerate.logging import get_logger
 from accelerate.utils import set_seed
 from datasets import load_dataset
-from huggingface_hub import Repository, create_repo
+from huggingface_hub import HfApi
 from torch.utils.data import DataLoader
 from tqdm.auto import tqdm
 from utils_qa import postprocess_qa_predictions_with_beam_search
@@ -333,15 +333,8 @@ def main():
             if repo_name is None:
                 repo_name = Path(args.output_dir).absolute().name
             # Create repo and retrieve repo_id
-            repo_id = create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
-            # Clone repo locally
-            repo = Repository(args.output_dir, clone_from=repo_id, token=args.hub_token)
-
-            with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
-                if "step_*" not in gitignore:
-                    gitignore.write("step_*\n")
-                if "epoch_*" not in gitignore:
-                    gitignore.write("epoch_*\n")
+            api = HfApi()
+            repo_id = api.create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
         elif args.output_dir is not None:
             os.makedirs(args.output_dir, exist_ok=True)
     accelerator.wait_for_everyone()
@@ -873,8 +866,12 @@ def create_and_fill_np_array(start_or_end_logits, dataset, max_len):
             )
             if accelerator.is_main_process:
                 tokenizer.save_pretrained(args.output_dir)
-                repo.push_to_hub(
-                    commit_message=f"Training in progress epoch {epoch}", blocking=False, auto_lfs_prune=True
+                api.upload_folder(
+                    commit_message=f"Training in progress epoch {epoch}",
+                    folder_path=args.output_dir,
+                    repo_id=repo_id,
+                    repo_type="model",
+                    token=args.hub_token,
                 )
 
     # initialize all lists to collect the batches
@@ -1020,7 +1017,13 @@ def create_and_fill_np_array(start_or_end_logits, dataset, max_len):
         if accelerator.is_main_process:
             tokenizer.save_pretrained(args.output_dir)
             if args.push_to_hub:
-                repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True)
+                api.upload_folder(
+                    commit_message="End of training",
+                    folder_path=args.output_dir,
+                    repo_id=repo_id,
+                    repo_type="model",
+                    token=args.hub_token,
+                )
 
             logger.info(json.dumps(eval_metric, indent=4))
             save_prefixed_metrics(eval_metric, args.output_dir)
diff --git a/examples/pytorch/question-answering/run_qa_no_trainer.py b/examples/pytorch/question-answering/run_qa_no_trainer.py
index 07a51cb53b1ef..0c591acdda3c3 100755
--- a/examples/pytorch/question-answering/run_qa_no_trainer.py
+++ b/examples/pytorch/question-answering/run_qa_no_trainer.py
@@ -34,7 +34,7 @@
 from accelerate.logging import get_logger
 from accelerate.utils import set_seed
 from datasets import load_dataset
-from huggingface_hub import Repository, create_repo
+from huggingface_hub import HfApi
 from torch.utils.data import DataLoader
 from tqdm.auto import tqdm
 from utils_qa import postprocess_qa_predictions
@@ -381,15 +381,8 @@ def main():
             if repo_name is None:
                 repo_name = Path(args.output_dir).absolute().name
             # Create repo and retrieve repo_id
-            repo_id = create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
-            # Clone repo locally
-            repo = Repository(args.output_dir, clone_from=repo_id, token=args.hub_token)
-
-            with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
-                if "step_*" not in gitignore:
-                    gitignore.write("step_*\n")
-                if "epoch_*" not in gitignore:
-                    gitignore.write("epoch_*\n")
+            api = HfApi()
+            repo_id = api.create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
         elif args.output_dir is not None:
             os.makedirs(args.output_dir, exist_ok=True)
     accelerator.wait_for_everyone()
@@ -912,8 +905,12 @@ def create_and_fill_np_array(start_or_end_logits, dataset, max_len):
             )
             if accelerator.is_main_process:
                 tokenizer.save_pretrained(args.output_dir)
-                repo.push_to_hub(
-                    commit_message=f"Training in progress epoch {epoch}", blocking=False, auto_lfs_prune=True
+                api.upload_folder(
+                    commit_message=f"Training in progress epoch {epoch}",
+                    folder_path=args.output_dir,
+                    repo_id=repo_id,
+                    repo_type="model",
+                    token=args.hub_token,
                 )
 
     # Evaluation
@@ -1013,8 +1010,13 @@ def create_and_fill_np_array(start_or_end_logits, dataset, max_len):
         if accelerator.is_main_process:
             tokenizer.save_pretrained(args.output_dir)
             if args.push_to_hub:
-                repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True)
-
+                api.upload_folder(
+                    commit_message="End of training",
+                    folder_path=args.output_dir,
+                    repo_id=repo_id,
+                    repo_type="model",
+                    token=args.hub_token,
+                )
             logger.info(json.dumps(eval_metric, indent=4))
             save_prefixed_metrics(eval_metric, args.output_dir)
 
diff --git a/examples/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py b/examples/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
index 44faf4fc4c5e3..358d3bd208d5b 100644
--- a/examples/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
+++ b/examples/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
@@ -29,7 +29,7 @@
 from accelerate.logging import get_logger
 from accelerate.utils import set_seed
 from datasets import load_dataset
-from huggingface_hub import Repository, create_repo, hf_hub_download
+from huggingface_hub import HfApi, hf_hub_download
 from PIL import Image
 from torch.utils.data import DataLoader
 from torchvision import transforms
@@ -365,15 +365,8 @@ def main():
             if repo_name is None:
                 repo_name = Path(args.output_dir).absolute().name
             # Create repo and retrieve repo_id
-            repo_id = create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
-            # Clone repo locally
-            repo = Repository(args.output_dir, clone_from=repo_id, token=args.hub_token)
-
-            with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
-                if "step_*" not in gitignore:
-                    gitignore.write("step_*\n")
-                if "epoch_*" not in gitignore:
-                    gitignore.write("epoch_*\n")
+            api = HfApi()
+            repo_id = api.create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
         elif args.output_dir is not None:
             os.makedirs(args.output_dir, exist_ok=True)
     accelerator.wait_for_everyone()
@@ -632,10 +625,12 @@ def preprocess_val(example_batch):
                         )
                         if accelerator.is_main_process:
                             image_processor.save_pretrained(args.output_dir)
-                            repo.push_to_hub(
-                                commit_message=f"Training in progress {completed_steps} steps",
-                                blocking=False,
-                                auto_lfs_prune=True,
+                            api.upload_folder(
+                                commit_message=f"Training in progress epoch {epoch}",
+                                folder_path=args.output_dir,
+                                repo_id=repo_id,
+                                repo_type="model",
+                                token=args.hub_token,
                             )
 
             if completed_steps >= args.max_train_steps:
@@ -687,8 +682,12 @@ def preprocess_val(example_batch):
             )
             if accelerator.is_main_process:
                 image_processor.save_pretrained(args.output_dir)
-                repo.push_to_hub(
-                    commit_message=f"Training in progress epoch {epoch}", blocking=False, auto_lfs_prune=True
+                api.upload_folder(
+                    commit_message=f"Training in progress epoch {epoch}",
+                    folder_path=args.output_dir,
+                    repo_id=repo_id,
+                    repo_type="model",
+                    token=args.hub_token,
                 )
 
         if args.checkpointing_steps == "epoch":
@@ -709,7 +708,13 @@ def preprocess_val(example_batch):
         if accelerator.is_main_process:
             image_processor.save_pretrained(args.output_dir)
             if args.push_to_hub:
-                repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True)
+                api.upload_folder(
+                    commit_message="End of training",
+                    folder_path=args.output_dir,
+                    repo_id=repo_id,
+                    repo_type="model",
+                    token=args.hub_token,
+                )
 
             all_results = {
                 f"eval_{k}": v.tolist() if isinstance(v, np.ndarray) else v for k, v in eval_metrics.items()
diff --git a/examples/pytorch/speech-pretraining/run_wav2vec2_pretraining_no_trainer.py b/examples/pytorch/speech-pretraining/run_wav2vec2_pretraining_no_trainer.py
index 6bde6d2b7d0f1..5b77348ad7278 100755
--- a/examples/pytorch/speech-pretraining/run_wav2vec2_pretraining_no_trainer.py
+++ b/examples/pytorch/speech-pretraining/run_wav2vec2_pretraining_no_trainer.py
@@ -27,7 +27,7 @@
 from accelerate import Accelerator
 from accelerate.logging import get_logger
 from datasets import DatasetDict, concatenate_datasets, load_dataset
-from huggingface_hub import Repository, create_repo
+from huggingface_hub import HfApi
 from torch.utils.data.dataloader import DataLoader
 from tqdm.auto import tqdm
 
@@ -423,9 +423,8 @@ def main():
             if repo_name is None:
                 repo_name = Path(args.output_dir).absolute().name
             # Create repo and retrieve repo_id
-            repo_id = create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
-            # Clone repo locally
-            repo = Repository(args.output_dir, clone_from=repo_id, token=args.hub_token)
+            api = HfApi()
+            repo_id = api.create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
         elif args.output_dir is not None:
             os.makedirs(args.output_dir, exist_ok=True)
     accelerator.wait_for_everyone()
@@ -719,10 +718,12 @@ def prepare_dataset(batch):
                     )
 
                 if (args.push_to_hub and epoch < args.num_train_epochs - 1) and accelerator.is_main_process:
-                    repo.push_to_hub(
-                        commit_message=f"Training in progress step {completed_steps}",
-                        blocking=False,
-                        auto_lfs_prune=True,
+                    api.upload_folder(
+                        commit_message=f"Training in progress epoch {epoch}",
+                        folder_path=args.output_dir,
+                        repo_id=repo_id,
+                        repo_type="model",
+                        token=args.hub_token,
                     )
 
             # if completed steps > `args.max_train_steps` stop
@@ -772,7 +773,13 @@ def prepare_dataset(batch):
             )
             if accelerator.is_main_process:
                 if args.push_to_hub:
-                    repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True)
+                    api.upload_folder(
+                        commit_message="End of training",
+                        folder_path=args.output_dir,
+                        repo_id=repo_id,
+                        repo_type="model",
+                        token=args.hub_token,
+                    )
 
 
 if __name__ == "__main__":
diff --git a/examples/pytorch/summarization/run_summarization_no_trainer.py b/examples/pytorch/summarization/run_summarization_no_trainer.py
index f2c6c456a92d7..f859f1cfed5c5 100644
--- a/examples/pytorch/summarization/run_summarization_no_trainer.py
+++ b/examples/pytorch/summarization/run_summarization_no_trainer.py
@@ -36,7 +36,7 @@
 from accelerate.utils import set_seed
 from datasets import load_dataset
 from filelock import FileLock
-from huggingface_hub import Repository, create_repo
+from huggingface_hub import HfApi
 from torch.utils.data import DataLoader
 from tqdm.auto import tqdm
 
@@ -375,9 +375,8 @@ def main():
             if repo_name is None:
                 repo_name = Path(args.output_dir).absolute().name
             # Create repo and retrieve repo_id
-            repo_id = create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
-            # Clone repo locally
-            repo = Repository(args.output_dir, clone_from=repo_id, token=args.hub_token)
+            api = HfApi()
+            repo_id = api.create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
 
             with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
                 if "step_*" not in gitignore:
@@ -755,8 +754,12 @@ def postprocess_text(preds, labels):
             )
             if accelerator.is_main_process:
                 tokenizer.save_pretrained(args.output_dir)
-                repo.push_to_hub(
-                    commit_message=f"Training in progress epoch {epoch}", blocking=False, auto_lfs_prune=True
+                api.upload_folder(
+                    commit_message=f"Training in progress epoch {epoch}",
+                    folder_path=args.output_dir,
+                    repo_id=repo_id,
+                    repo_type="model",
+                    token=args.hub_token,
                 )
 
         if args.checkpointing_steps == "epoch":
@@ -774,7 +777,13 @@ def postprocess_text(preds, labels):
         if accelerator.is_main_process:
             tokenizer.save_pretrained(args.output_dir)
             if args.push_to_hub:
-                repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True)
+                api.upload_folder(
+                    commit_message="End of training",
+                    folder_path=args.output_dir,
+                    repo_id=repo_id,
+                    repo_type="model",
+                    token=args.hub_token,
+                )
 
             all_results = {f"eval_{k}": v for k, v in result.items()}
             with open(os.path.join(args.output_dir, "all_results.json"), "w") as f:
diff --git a/examples/pytorch/text-classification/run_glue_no_trainer.py b/examples/pytorch/text-classification/run_glue_no_trainer.py
index 084e6753b27b8..c6a6cd7516e0f 100644
--- a/examples/pytorch/text-classification/run_glue_no_trainer.py
+++ b/examples/pytorch/text-classification/run_glue_no_trainer.py
@@ -28,7 +28,7 @@
 from accelerate.logging import get_logger
 from accelerate.utils import set_seed
 from datasets import load_dataset
-from huggingface_hub import HfApi, create_repo
+from huggingface_hub import HfApi
 from torch.utils.data import DataLoader
 from tqdm.auto import tqdm
 
@@ -46,6 +46,7 @@
 from transformers.utils import check_min_version, send_example_telemetry
 from transformers.utils.versions import require_version
 
+
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
 check_min_version("4.39.0.dev0")
 
@@ -255,7 +256,7 @@ def main():
                 repo_name = Path(args.output_dir).absolute().name
             # Create repo and retrieve repo_id
             api = HfApi()
-            repo_id = create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
+            repo_id = api.create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
         elif args.output_dir is not None:
             os.makedirs(args.output_dir, exist_ok=True)
     accelerator.wait_for_everyone()
@@ -608,7 +609,6 @@ def preprocess_function(examples):
                     folder_path=args.output_dir,
                     repo_id=repo_id,
                     repo_type="model",
-                    ignore_patterns=["step_*", "epoch_*"],
                     token=args.hub_token,
                 )
 
@@ -635,7 +635,6 @@ def preprocess_function(examples):
                     folder_path=args.output_dir,
                     repo_id=repo_id,
                     repo_type="model",
-                    ignore_patterns=["step_*", "epoch_*"],
                     token=args.hub_token,
                 )
 
diff --git a/examples/pytorch/token-classification/run_ner_no_trainer.py b/examples/pytorch/token-classification/run_ner_no_trainer.py
index c6058b0fed3ff..297ffde3bc469 100755
--- a/examples/pytorch/token-classification/run_ner_no_trainer.py
+++ b/examples/pytorch/token-classification/run_ner_no_trainer.py
@@ -34,7 +34,7 @@
 from accelerate.logging import get_logger
 from accelerate.utils import set_seed
 from datasets import ClassLabel, load_dataset
-from huggingface_hub import Repository, create_repo
+from huggingface_hub import HfApi
 from torch.utils.data import DataLoader
 from tqdm.auto import tqdm
 
@@ -310,15 +310,8 @@ def main():
             if repo_name is None:
                 repo_name = Path(args.output_dir).absolute().name
             # Create repo and retrieve repo_id
-            repo_id = create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
-            # Clone repo locally
-            repo = Repository(args.output_dir, clone_from=repo_id, token=args.hub_token)
-
-            with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
-                if "step_*" not in gitignore:
-                    gitignore.write("step_*\n")
-                if "epoch_*" not in gitignore:
-                    gitignore.write("epoch_*\n")
+            api = HfApi()
+            repo_id = api.create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
         elif args.output_dir is not None:
             os.makedirs(args.output_dir, exist_ok=True)
     accelerator.wait_for_everyone()
@@ -776,8 +769,12 @@ def compute_metrics():
             )
             if accelerator.is_main_process:
                 tokenizer.save_pretrained(args.output_dir)
-                repo.push_to_hub(
-                    commit_message=f"Training in progress epoch {epoch}", blocking=False, auto_lfs_prune=True
+                api.upload_folder(
+                    commit_message=f"Training in progress epoch {epoch}",
+                    folder_path=args.output_dir,
+                    repo_id=repo_id,
+                    repo_type="model",
+                    token=args.hub_token,
                 )
 
         if args.checkpointing_steps == "epoch":
@@ -798,7 +795,13 @@ def compute_metrics():
         if accelerator.is_main_process:
             tokenizer.save_pretrained(args.output_dir)
             if args.push_to_hub:
-                repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True)
+                api.upload_folder(
+                    commit_message="End of training",
+                    folder_path=args.output_dir,
+                    repo_id=repo_id,
+                    repo_type="model",
+                    token=args.hub_token,
+                )
 
             all_results = {f"eval_{k}": v for k, v in eval_metric.items()}
             if args.with_tracking:
diff --git a/examples/pytorch/translation/run_translation_no_trainer.py b/examples/pytorch/translation/run_translation_no_trainer.py
index 6e803c0a19c7c..f732fb6428924 100644
--- a/examples/pytorch/translation/run_translation_no_trainer.py
+++ b/examples/pytorch/translation/run_translation_no_trainer.py
@@ -34,7 +34,7 @@
 from accelerate.logging import get_logger
 from accelerate.utils import set_seed
 from datasets import load_dataset
-from huggingface_hub import Repository, create_repo
+from huggingface_hub import HfApi
 from torch.utils.data import DataLoader
 from tqdm.auto import tqdm
 
@@ -355,9 +355,8 @@ def main():
             if repo_name is None:
                 repo_name = Path(args.output_dir).absolute().name
             # Create repo and retrieve repo_id
-            repo_id = create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
-            # Clone repo locally
-            repo = Repository(args.output_dir, clone_from=repo_id, token=args.hub_token)
+            api = HfApi()
+            repo_id = api.create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
 
             with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
                 if "step_*" not in gitignore:
@@ -743,8 +742,12 @@ def postprocess_text(preds, labels):
             )
             if accelerator.is_main_process:
                 tokenizer.save_pretrained(args.output_dir)
-                repo.push_to_hub(
-                    commit_message=f"Training in progress epoch {epoch}", blocking=False, auto_lfs_prune=True
+                api.upload_folder(
+                    commit_message=f"Training in progress epoch {epoch}",
+                    folder_path=args.output_dir,
+                    repo_id=repo_id,
+                    repo_type="model",
+                    token=args.hub_token,
                 )
 
         if args.checkpointing_steps == "epoch":
@@ -765,7 +768,13 @@ def postprocess_text(preds, labels):
         if accelerator.is_main_process:
             tokenizer.save_pretrained(args.output_dir)
             if args.push_to_hub:
-                repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True)
+                api.upload_folder(
+                    commit_message="End of training",
+                    folder_path=args.output_dir,
+                    repo_id=repo_id,
+                    repo_type="model",
+                    token=args.hub_token,
+                )
         with open(os.path.join(args.output_dir, "all_results.json"), "w") as f:
             json.dump({"eval_bleu": eval_metric["score"]}, f)
 
diff --git a/examples/research_projects/codeparrot/scripts/codeparrot_training.py b/examples/research_projects/codeparrot/scripts/codeparrot_training.py
index 16f6077f2415c..b5be3cbfc0bc2 100644
--- a/examples/research_projects/codeparrot/scripts/codeparrot_training.py
+++ b/examples/research_projects/codeparrot/scripts/codeparrot_training.py
@@ -10,7 +10,7 @@
 from accelerate.utils import ProjectConfiguration
 from arguments import TrainingArguments
 from datasets import load_dataset
-from huggingface_hub import Repository
+from huggingface_hub import HfApi
 from torch.optim import AdamW
 from torch.utils.data import IterableDataset
 from torch.utils.data.dataloader import DataLoader
@@ -204,17 +204,14 @@ def evaluate(args):
 samples_per_step = accelerator.state.num_processes * args.train_batch_size
 set_seed(args.seed)
 
-# Clone model repository
-if accelerator.is_main_process:
-    hf_repo = Repository(args.save_dir, clone_from=args.model_ckpt)
-
 # Logging
 logger, run_name = setup_logging(args)
 logger.info(accelerator.state)
 
 # Checkout new branch on repo
 if accelerator.is_main_process:
-    hf_repo.git_checkout(run_name, create_branch_ok=True)
+    api = HfApi()
+    api.create_branch(branch=run_name, repo_id=args.model_ckpt, create_branch_ok=True)
 
 # Load model and tokenizer
 model = AutoModelForCausalLM.from_pretrained(args.save_dir)
@@ -310,7 +307,7 @@ def get_lr():
         save_dir = os.path.join(args.save_dir, f"step_{step}")
         accelerator.save_state(save_dir)
         if accelerator.is_main_process:
-            hf_repo.push_to_hub(commit_message=f"step {step}")
+            api.upload_folder(commit_message=f"step {step}", repo_id=args.model_ckpt, folder_path=args.save_dir)
         model.train()
     if completed_steps >= args.max_train_steps:
         break
@@ -325,4 +322,4 @@ def get_lr():
 save_dir = os.path.join(args.save_dir, f"step_{step}")
 accelerator.save_state(save_dir)
 if accelerator.is_main_process:
-    hf_repo.push_to_hub(commit_message="final model")
+    api.upload_folder(commit_message="final model", repo_id=args.model_ckpt, folder_path=args.save_dir)
diff --git a/examples/research_projects/luke/run_luke_ner_no_trainer.py b/examples/research_projects/luke/run_luke_ner_no_trainer.py
index cac487b059d71..982658aacdc0f 100644
--- a/examples/research_projects/luke/run_luke_ner_no_trainer.py
+++ b/examples/research_projects/luke/run_luke_ner_no_trainer.py
@@ -29,7 +29,7 @@
 import torch
 from accelerate import Accelerator, DistributedDataParallelKwargs
 from datasets import ClassLabel, load_dataset, load_metric
-from huggingface_hub import Repository, create_repo
+from huggingface_hub import HfApi
 from luke_utils import DataCollatorForLukeTokenClassification, is_punctuation, padding_tensor
 from torch.utils.data import DataLoader
 from tqdm.auto import tqdm
@@ -262,9 +262,8 @@ def main():
             if repo_name is None:
                 repo_name = Path(args.output_dir).absolute().name
             # Create repo and retrieve repo_id
-            repo_id = create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
-            # Clone repo locally
-            repo = Repository(args.output_dir, clone_from=repo_id, token=args.hub_token)
+            api = HfApi()
+            repo_id = api.create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
         elif args.output_dir is not None:
             os.makedirs(args.output_dir, exist_ok=True)
     accelerator.wait_for_everyone()
@@ -697,8 +696,12 @@ def compute_metrics():
             unwrapped_model.save_pretrained(args.output_dir, save_function=accelerator.save)
             if accelerator.is_main_process:
                 tokenizer.save_pretrained(args.output_dir)
-                repo.push_to_hub(
-                    commit_message=f"Training in progress epoch {epoch}", blocking=False, auto_lfs_prune=True
+                api.upload_folder(
+                    commit_message=f"Training in progress epoch {epoch}",
+                    folder_path=args.output_dir,
+                    repo_id=repo_id,
+                    repo_type="model",
+                    token=args.hub_token,
                 )
 
     if args.output_dir is not None:
@@ -708,7 +711,13 @@ def compute_metrics():
         if accelerator.is_main_process:
             tokenizer.save_pretrained(args.output_dir)
             if args.push_to_hub:
-                repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True)
+                api.upload_folder(
+                    commit_message="End of training",
+                    folder_path=args.output_dir,
+                    repo_id=repo_id,
+                    repo_type="model",
+                    token=args.hub_token,
+                )
 
 
 if __name__ == "__main__":