From 145109382a972d53ed3eac17216dc509d1d19262 Mon Sep 17 00:00:00 2001
From: Jackmin801 <56836461+Jackmin801@users.noreply.github.com>
Date: Mon, 7 Aug 2023 22:32:25 +0800
Subject: [PATCH] Allow `trust_remote_code` in example scripts (#25248)

* pytorch examples

* pytorch mim no trainer

* cookiecutter

* flax examples

* missed line in pytorch run_glue

* tensorflow examples

* tensorflow run_clip

* tensorflow run_mlm

* tensorflow run_ner

* tensorflow run_clm

* pytorch example from_configs

* pytorch no trainer examples

* Revert "tensorflow run_clip"

This reverts commit 261f86ac1f1c9e05dd3fd0291e1a1f8e573781d5.

* fix: duplicated argument
---
 .../run_image_captioning_flax.py              | 13 ++++++
 .../flax/language-modeling/run_clm_flax.py    | 16 ++++++++
 .../flax/language-modeling/run_mlm_flax.py    | 16 ++++++++
 examples/flax/question-answering/run_qa.py    | 13 ++++++
 .../summarization/run_summarization_flax.py   | 16 ++++++++
 .../flax/text-classification/run_flax_glue.py | 13 ++++++
 .../flax/token-classification/run_flax_ner.py | 14 +++++++
 .../flax/vision/run_image_classification.py   | 14 +++++++
 .../run_audio_classification.py               | 13 ++++++
 .../contrastive-image-text/run_clip.py        | 12 ++++++
 .../run_image_classification.py               | 13 ++++++
 .../run_image_classification_no_trainer.py    | 17 +++++++-
 examples/pytorch/image-pretraining/run_mim.py | 14 ++++++-
 .../image-pretraining/run_mim_no_trainer.py   | 17 +++++++-
 examples/pytorch/language-modeling/run_clm.py | 15 ++++++-
 .../language-modeling/run_clm_no_trainer.py   | 31 +++++++++++---
 examples/pytorch/language-modeling/run_mlm.py | 15 ++++++-
 .../language-modeling/run_mlm_no_trainer.py   | 25 +++++++++---
 examples/pytorch/multiple-choice/run_swag.py  | 13 ++++++
 .../multiple-choice/run_swag_no_trainer.py    | 25 +++++++++---
 examples/pytorch/question-answering/run_qa.py | 13 ++++++
 .../question-answering/run_qa_no_trainer.py   | 25 +++++++++---
 .../question-answering/run_seq2seq_qa.py      | 13 ++++++
 .../run_semantic_segmentation.py              | 13 ++++++
 .../run_semantic_segmentation_no_trainer.py   | 22 ++++++++--
 .../run_speech_recognition_ctc.py             | 14 +++++++
 .../run_speech_recognition_ctc_adapter.py     | 15 +++++++
 .../run_speech_recognition_seq2seq.py         | 14 +++++++
 .../summarization/run_summarization.py        | 13 ++++++
 .../run_summarization_no_trainer.py           | 25 +++++++++---
 .../text-classification/run_classification.py | 13 ++++++
 .../pytorch/text-classification/run_glue.py   | 13 ++++++
 .../run_glue_no_trainer.py                    | 22 +++++++++-
 .../pytorch/text-classification/run_xnli.py   | 13 ++++++
 .../pytorch/token-classification/run_ner.py   | 14 +++++++
 .../run_ner_no_trainer.py                     | 29 +++++++++++---
 .../pytorch/translation/run_translation.py    | 13 ++++++
 .../translation/run_translation_no_trainer.py | 25 +++++++++---
 .../run_image_classification.py               | 13 ++++++
 .../tensorflow/language-modeling/run_clm.py   | 37 +++++++++++++----
 .../tensorflow/language-modeling/run_mlm.py   | 36 +++++++++++++----
 .../tensorflow/multiple-choice/run_swag.py    | 13 ++++++
 .../tensorflow/question-answering/run_qa.py   | 13 ++++++
 .../summarization/run_summarization.py        | 13 ++++++
 .../text-classification/run_glue.py           | 13 ++++++
 .../run_text_classification.py                | 14 +++++++
 .../token-classification/run_ner.py           | 40 ++++++++++++++++---
 .../tensorflow/translation/run_translation.py | 13 ++++++
 .../run_{{cookiecutter.example_shortcut}}.py  | 16 ++++++++
 49 files changed, 790 insertions(+), 65 deletions(-)

diff --git a/examples/flax/image-captioning/run_image_captioning_flax.py b/examples/flax/image-captioning/run_image_captioning_flax.py
index f71641e30f..bbc79977a4 100644
--- a/examples/flax/image-captioning/run_image_captioning_flax.py
+++ b/examples/flax/image-captioning/run_image_captioning_flax.py
@@ -198,6 +198,16 @@ class ModelArguments:
             "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
         },
     )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+                "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+                "execute code present on the Hub on your local machine."
+            )
+        },
+    )
 
 
 @dataclass
@@ -489,17 +499,20 @@ def main():
         seed=training_args.seed,
         dtype=getattr(jnp, model_args.dtype),
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
     image_processor = AutoImageProcessor.from_pretrained(
         model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
     tokenizer = AutoTokenizer.from_pretrained(
         model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         use_fast=model_args.use_fast_tokenizer,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
     tokenizer.pad_token = tokenizer.convert_ids_to_tokens(model.config.pad_token_id)
 
diff --git a/examples/flax/language-modeling/run_clm_flax.py b/examples/flax/language-modeling/run_clm_flax.py
index 7c4206a0c7..1a296a4fa9 100755
--- a/examples/flax/language-modeling/run_clm_flax.py
+++ b/examples/flax/language-modeling/run_clm_flax.py
@@ -185,6 +185,16 @@ class ModelArguments:
             "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
         },
     )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+                "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+                "execute code present on the Hub on your local machine."
+            )
+        },
+    )
 
 
 @dataclass
@@ -477,12 +487,14 @@ def main():
             model_args.config_name,
             cache_dir=model_args.cache_dir,
             token=model_args.token,
+            trust_remote_code=model_args.trust_remote_code,
         )
     elif model_args.model_name_or_path:
         config = AutoConfig.from_pretrained(
             model_args.model_name_or_path,
             cache_dir=model_args.cache_dir,
             token=model_args.token,
+            trust_remote_code=model_args.trust_remote_code,
         )
     else:
         config = CONFIG_MAPPING[model_args.model_type]()
@@ -494,6 +506,7 @@ def main():
             cache_dir=model_args.cache_dir,
             use_fast=model_args.use_fast_tokenizer,
             token=model_args.token,
+            trust_remote_code=model_args.trust_remote_code,
         )
     elif model_args.model_name_or_path:
         tokenizer = AutoTokenizer.from_pretrained(
@@ -501,6 +514,7 @@ def main():
             cache_dir=model_args.cache_dir,
             use_fast=model_args.use_fast_tokenizer,
             token=model_args.token,
+            trust_remote_code=model_args.trust_remote_code,
         )
     else:
         raise ValueError(
@@ -515,12 +529,14 @@ def main():
             seed=training_args.seed,
             dtype=getattr(jnp, model_args.dtype),
             token=model_args.token,
+            trust_remote_code=model_args.trust_remote_code,
         )
     else:
         model = FlaxAutoModelForCausalLM.from_config(
             config,
             seed=training_args.seed,
             dtype=getattr(jnp, model_args.dtype),
+            trust_remote_code=model_args.trust_remote_code,
         )
 
     # Preprocessing the datasets.
diff --git a/examples/flax/language-modeling/run_mlm_flax.py b/examples/flax/language-modeling/run_mlm_flax.py
index d5e44feaf3..0c49a2cff7 100755
--- a/examples/flax/language-modeling/run_mlm_flax.py
+++ b/examples/flax/language-modeling/run_mlm_flax.py
@@ -190,6 +190,16 @@ class ModelArguments:
             "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
         },
     )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+                "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+                "execute code present on the Hub on your local machine."
+            )
+        },
+    )
 
 
 @dataclass
@@ -509,12 +519,14 @@ def main():
             model_args.config_name,
             cache_dir=model_args.cache_dir,
             token=model_args.token,
+            trust_remote_code=model_args.trust_remote_code,
         )
     elif model_args.model_name_or_path:
         config = AutoConfig.from_pretrained(
             model_args.model_name_or_path,
             cache_dir=model_args.cache_dir,
             token=model_args.token,
+            trust_remote_code=model_args.trust_remote_code,
         )
     else:
         config = CONFIG_MAPPING[model_args.model_type]()
@@ -526,6 +538,7 @@ def main():
             cache_dir=model_args.cache_dir,
             use_fast=model_args.use_fast_tokenizer,
             token=model_args.token,
+            trust_remote_code=model_args.trust_remote_code,
         )
     elif model_args.model_name_or_path:
         tokenizer = AutoTokenizer.from_pretrained(
@@ -533,6 +546,7 @@ def main():
             cache_dir=model_args.cache_dir,
             use_fast=model_args.use_fast_tokenizer,
             token=model_args.token,
+            trust_remote_code=model_args.trust_remote_code,
         )
     else:
         raise ValueError(
@@ -652,12 +666,14 @@ def main():
             seed=training_args.seed,
             dtype=getattr(jnp, model_args.dtype),
             token=model_args.token,
+            trust_remote_code=model_args.trust_remote_code,
         )
     else:
         model = FlaxAutoModelForMaskedLM.from_config(
             config,
             seed=training_args.seed,
             dtype=getattr(jnp, model_args.dtype),
+            trust_remote_code=model_args.trust_remote_code,
         )
 
     if training_args.gradient_checkpointing:
diff --git a/examples/flax/question-answering/run_qa.py b/examples/flax/question-answering/run_qa.py
index 925e182e7e..77bd37bab8 100644
--- a/examples/flax/question-answering/run_qa.py
+++ b/examples/flax/question-answering/run_qa.py
@@ -171,6 +171,16 @@ class ModelArguments:
             "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
         },
     )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+                "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+                "execute code present on the Hub on your local machine."
+            )
+        },
+    )
     dtype: Optional[str] = field(
         default="float32",
         metadata={
@@ -534,6 +544,7 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
     tokenizer = AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
@@ -541,6 +552,7 @@ def main():
         use_fast=True,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
     # endregion
 
@@ -888,6 +900,7 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
         seed=training_args.seed,
         dtype=getattr(jnp, model_args.dtype),
     )
diff --git a/examples/flax/summarization/run_summarization_flax.py b/examples/flax/summarization/run_summarization_flax.py
index 83af31d8d2..fc1595ac7e 100644
--- a/examples/flax/summarization/run_summarization_flax.py
+++ b/examples/flax/summarization/run_summarization_flax.py
@@ -204,6 +204,16 @@ class ModelArguments:
             "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
         },
     )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+                "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+                "execute code present on the Hub on your local machine."
+            )
+        },
+    )
 
 
 @dataclass
@@ -517,12 +527,14 @@ def main():
             model_args.config_name,
             cache_dir=model_args.cache_dir,
             token=model_args.token,
+            trust_remote_code=model_args.trust_remote_code,
         )
     elif model_args.model_name_or_path:
         config = AutoConfig.from_pretrained(
             model_args.model_name_or_path,
             cache_dir=model_args.cache_dir,
             token=model_args.token,
+            trust_remote_code=model_args.trust_remote_code,
         )
     else:
         config = CONFIG_MAPPING[model_args.model_type]()
@@ -534,6 +546,7 @@ def main():
             cache_dir=model_args.cache_dir,
             use_fast=model_args.use_fast_tokenizer,
             token=model_args.token,
+            trust_remote_code=model_args.trust_remote_code,
         )
     elif model_args.model_name_or_path:
         tokenizer = AutoTokenizer.from_pretrained(
@@ -541,6 +554,7 @@ def main():
             cache_dir=model_args.cache_dir,
             use_fast=model_args.use_fast_tokenizer,
             token=model_args.token,
+            trust_remote_code=model_args.trust_remote_code,
         )
     else:
         raise ValueError(
@@ -555,12 +569,14 @@ def main():
             seed=training_args.seed,
             dtype=getattr(jnp, model_args.dtype),
             token=model_args.token,
+            trust_remote_code=model_args.trust_remote_code,
         )
     else:
         model = FlaxAutoModelForSeq2SeqLM.from_config(
             config,
             seed=training_args.seed,
             dtype=getattr(jnp, model_args.dtype),
+            trust_remote_code=model_args.trust_remote_code,
         )
 
     if training_args.gradient_checkpointing:
diff --git a/examples/flax/text-classification/run_flax_glue.py b/examples/flax/text-classification/run_flax_glue.py
index 3f6d0a5eb6..b9a23f9dd5 100755
--- a/examples/flax/text-classification/run_flax_glue.py
+++ b/examples/flax/text-classification/run_flax_glue.py
@@ -117,6 +117,16 @@ class ModelArguments:
             "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
         },
     )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+                "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+                "execute code present on the Hub on your local machine."
+            )
+        },
+    )
 
 
 @dataclass
@@ -425,16 +435,19 @@ def main():
         num_labels=num_labels,
         finetuning_task=data_args.task_name,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
     tokenizer = AutoTokenizer.from_pretrained(
         model_args.model_name_or_path,
         use_fast=not model_args.use_slow_tokenizer,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
     model = FlaxAutoModelForSequenceClassification.from_pretrained(
         model_args.model_name_or_path,
         config=config,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
 
     # Preprocessing the datasets
diff --git a/examples/flax/token-classification/run_flax_ner.py b/examples/flax/token-classification/run_flax_ner.py
index f66d9b3128..e0a0cb1eff 100644
--- a/examples/flax/token-classification/run_flax_ner.py
+++ b/examples/flax/token-classification/run_flax_ner.py
@@ -165,6 +165,16 @@ class ModelArguments:
             "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
         },
     )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+                "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+                "execute code present on the Hub on your local machine."
+            )
+        },
+    )
 
 
 @dataclass
@@ -504,6 +514,7 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
     tokenizer_name_or_path = model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path
     if config.model_type in {"gpt2", "roberta"}:
@@ -512,6 +523,7 @@ def main():
             cache_dir=model_args.cache_dir,
             revision=model_args.model_revision,
             token=model_args.token,
+            trust_remote_code=model_args.trust_remote_code,
             add_prefix_space=True,
         )
     else:
@@ -520,6 +532,7 @@ def main():
             cache_dir=model_args.cache_dir,
             revision=model_args.model_revision,
             token=model_args.token,
+            trust_remote_code=model_args.trust_remote_code,
         )
     model = FlaxAutoModelForTokenClassification.from_pretrained(
         model_args.model_name_or_path,
@@ -527,6 +540,7 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
 
     # Preprocessing the datasets
diff --git a/examples/flax/vision/run_image_classification.py b/examples/flax/vision/run_image_classification.py
index 63b638423a..d3454f2672 100644
--- a/examples/flax/vision/run_image_classification.py
+++ b/examples/flax/vision/run_image_classification.py
@@ -175,6 +175,16 @@ class ModelArguments:
             "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
         },
     )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+                "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+                "execute code present on the Hub on your local machine."
+            )
+        },
+    )
 
 
 @dataclass
@@ -352,6 +362,7 @@ def main():
             image_size=data_args.image_size,
             cache_dir=model_args.cache_dir,
             token=model_args.token,
+            trust_remote_code=model_args.trust_remote_code,
         )
     elif model_args.model_name_or_path:
         config = AutoConfig.from_pretrained(
@@ -360,6 +371,7 @@ def main():
             image_size=data_args.image_size,
             cache_dir=model_args.cache_dir,
             token=model_args.token,
+            trust_remote_code=model_args.trust_remote_code,
         )
     else:
         config = CONFIG_MAPPING[model_args.model_type]()
@@ -372,12 +384,14 @@ def main():
             seed=training_args.seed,
             dtype=getattr(jnp, model_args.dtype),
             token=model_args.token,
+            trust_remote_code=model_args.trust_remote_code,
         )
     else:
         model = FlaxAutoModelForImageClassification.from_config(
             config,
             seed=training_args.seed,
             dtype=getattr(jnp, model_args.dtype),
+            trust_remote_code=model_args.trust_remote_code,
         )
 
     # Store some constant
diff --git a/examples/pytorch/audio-classification/run_audio_classification.py b/examples/pytorch/audio-classification/run_audio_classification.py
index c1568867e2..8be727200a 100644
--- a/examples/pytorch/audio-classification/run_audio_classification.py
+++ b/examples/pytorch/audio-classification/run_audio_classification.py
@@ -167,6 +167,16 @@ class ModelArguments:
             "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
         },
     )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+                "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+                "execute code present on the Hub on your local machine."
+            )
+        },
+    )
     freeze_feature_extractor: Optional[bool] = field(
         default=None, metadata={"help": "Whether to freeze the feature extractor layers of the model."}
     )
@@ -293,6 +303,7 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
 
     # `datasets` takes care of automatically loading and resampling the audio,
@@ -353,6 +364,7 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
     model = AutoModelForAudioClassification.from_pretrained(
         model_args.model_name_or_path,
@@ -361,6 +373,7 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
         ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
     )
 
diff --git a/examples/pytorch/contrastive-image-text/run_clip.py b/examples/pytorch/contrastive-image-text/run_clip.py
index dad10ea274..86612b8b7e 100644
--- a/examples/pytorch/contrastive-image-text/run_clip.py
+++ b/examples/pytorch/contrastive-image-text/run_clip.py
@@ -102,6 +102,16 @@ class ModelArguments:
             "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
         },
     )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+                "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+                "execute code present on the Hub on your local machine."
+            )
+        },
+    )
     freeze_vision_model: bool = field(
         default=False, metadata={"help": "Whether to freeze the vision model parameters or not."}
     )
@@ -350,6 +360,7 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
 
     model = AutoModel.from_pretrained(
@@ -357,6 +368,7 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
     config = model.config
 
diff --git a/examples/pytorch/image-classification/run_image_classification.py b/examples/pytorch/image-classification/run_image_classification.py
index 35a74b2532..a7fe144da2 100644
--- a/examples/pytorch/image-classification/run_image_classification.py
+++ b/examples/pytorch/image-classification/run_image_classification.py
@@ -158,6 +158,16 @@ class ModelArguments:
             "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
         },
     )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+                "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+                "execute code present on the Hub on your local machine."
+            )
+        },
+    )
     ignore_mismatched_sizes: bool = field(
         default=False,
         metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."},
@@ -290,6 +300,7 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
     model = AutoModelForImageClassification.from_pretrained(
         model_args.model_name_or_path,
@@ -298,6 +309,7 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
         ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
     )
     image_processor = AutoImageProcessor.from_pretrained(
@@ -305,6 +317,7 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
 
     # Define torchvision transforms to be applied to each image.
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 c416273132..62434649c3 100644
--- a/examples/pytorch/image-classification/run_image_classification_no_trainer.py
+++ b/examples/pytorch/image-classification/run_image_classification_no_trainer.py
@@ -146,6 +146,16 @@ def parse_args():
         "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`."
     )
     parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.")
+    parser.add_argument(
+        "--trust_remote_code",
+        type=bool,
+        default=False,
+        help=(
+            "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+            "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+            "execute code present on the Hub on your local machine."
+        ),
+    )
     parser.add_argument(
         "--checkpointing_steps",
         type=str,
@@ -300,13 +310,18 @@ def main():
         i2label=id2label,
         label2id=label2id,
         finetuning_task="image-classification",
+        trust_remote_code=args.trust_remote_code,
+    )
+    image_processor = AutoImageProcessor.from_pretrained(
+        args.model_name_or_path,
+        trust_remote_code=args.trust_remote_code,
     )
-    image_processor = AutoImageProcessor.from_pretrained(args.model_name_or_path)
     model = AutoModelForImageClassification.from_pretrained(
         args.model_name_or_path,
         from_tf=bool(".ckpt" in args.model_name_or_path),
         config=config,
         ignore_mismatched_sizes=args.ignore_mismatched_sizes,
+        trust_remote_code=args.trust_remote_code,
     )
 
     # Preprocessing the datasets
diff --git a/examples/pytorch/image-pretraining/run_mim.py b/examples/pytorch/image-pretraining/run_mim.py
index 25e780ab48..9fa3a1b68a 100644
--- a/examples/pytorch/image-pretraining/run_mim.py
+++ b/examples/pytorch/image-pretraining/run_mim.py
@@ -169,6 +169,16 @@ class ModelArguments:
             "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
         },
     )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+                "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+                "execute code present on the Hub on your local machine."
+            )
+        },
+    )
     image_size: Optional[int] = field(
         default=None,
         metadata={
@@ -319,6 +329,7 @@ def main():
         "cache_dir": model_args.cache_dir,
         "revision": model_args.model_revision,
         "token": model_args.token,
+        "trust_remote_code": model_args.trust_remote_code,
     }
     if model_args.config_name_or_path:
         config = AutoConfig.from_pretrained(model_args.config_name_or_path, **config_kwargs)
@@ -371,10 +382,11 @@ def main():
             cache_dir=model_args.cache_dir,
             revision=model_args.model_revision,
             token=model_args.token,
+            trust_remote_code=model_args.trust_remote_code,
         )
     else:
         logger.info("Training new model from scratch")
-        model = AutoModelForMaskedImageModeling.from_config(config)
+        model = AutoModelForMaskedImageModeling.from_config(config, trust_remote_code=model_args.trust_remote_code)
 
     if training_args.do_train:
         column_names = ds["train"].column_names
diff --git a/examples/pytorch/image-pretraining/run_mim_no_trainer.py b/examples/pytorch/image-pretraining/run_mim_no_trainer.py
index 44066daee5..5053dabf91 100644
--- a/examples/pytorch/image-pretraining/run_mim_no_trainer.py
+++ b/examples/pytorch/image-pretraining/run_mim_no_trainer.py
@@ -195,6 +195,16 @@ def parse_args():
             "with private models)."
         ),
     )
+    parser.add_argument(
+        "--trust_remote_code",
+        type=bool,
+        default=False,
+        help=(
+            "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+            "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+            "execute code present on the Hub on your local machine."
+        ),
+    )
     parser.add_argument(
         "--image_size",
         type=int,
@@ -448,6 +458,7 @@ def main():
         "cache_dir": args.cache_dir,
         "revision": args.model_revision,
         "use_auth_token": True if args.use_auth_token else None,
+        "trust_remote_code": args.trust_remote_code,
     }
     if args.config_name_or_path:
         config = AutoConfig.from_pretrained(args.config_name_or_path, **config_kwargs)
@@ -498,10 +509,14 @@ def main():
             cache_dir=args.cache_dir,
             revision=args.model_revision,
             token=True if args.use_auth_token else None,
+            trust_remote_code=args.trust_remote_code,
         )
     else:
         logger.info("Training new model from scratch")
-        model = AutoModelForMaskedImageModeling.from_config(config)
+        model = AutoModelForMaskedImageModeling.from_config(
+            config,
+            trust_remote_code=args.trust_remote_code,
+        )
 
     column_names = ds["train"].column_names
 
diff --git a/examples/pytorch/language-modeling/run_clm.py b/examples/pytorch/language-modeling/run_clm.py
index c1ed1c3412..3d491f784c 100755
--- a/examples/pytorch/language-modeling/run_clm.py
+++ b/examples/pytorch/language-modeling/run_clm.py
@@ -127,6 +127,16 @@ class ModelArguments:
             "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
         },
     )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+                "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+                "execute code present on the Hub on your local machine."
+            )
+        },
+    )
     torch_dtype: Optional[str] = field(
         default=None,
         metadata={
@@ -387,6 +397,7 @@ def main():
         "cache_dir": model_args.cache_dir,
         "revision": model_args.model_revision,
         "token": model_args.token,
+        "trust_remote_code": model_args.trust_remote_code,
     }
     if model_args.config_name:
         config = AutoConfig.from_pretrained(model_args.config_name, **config_kwargs)
@@ -405,6 +416,7 @@ def main():
         "use_fast": model_args.use_fast_tokenizer,
         "revision": model_args.model_revision,
         "token": model_args.token,
+        "trust_remote_code": model_args.trust_remote_code,
     }
     if model_args.tokenizer_name:
         tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, **tokenizer_kwargs)
@@ -429,11 +441,12 @@ def main():
             cache_dir=model_args.cache_dir,
             revision=model_args.model_revision,
             token=model_args.token,
+            trust_remote_code=model_args.trust_remote_code,
             torch_dtype=torch_dtype,
             low_cpu_mem_usage=model_args.low_cpu_mem_usage,
         )
     else:
-        model = AutoModelForCausalLM.from_config(config)
+        model = AutoModelForCausalLM.from_config(config, trust_remote_code=model_args.trust_remote_code)
         n_params = sum({p.data_ptr(): p.numel() for p in model.parameters()}.values())
         logger.info(f"Training new model from scratch - Total size={n_params/2**20:.2f}M params")
 
diff --git a/examples/pytorch/language-modeling/run_clm_no_trainer.py b/examples/pytorch/language-modeling/run_clm_no_trainer.py
index 58954bd853..b195e33cc9 100755
--- a/examples/pytorch/language-modeling/run_clm_no_trainer.py
+++ b/examples/pytorch/language-modeling/run_clm_no_trainer.py
@@ -193,6 +193,16 @@ def parse_args():
         "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`."
     )
     parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.")
+    parser.add_argument(
+        "--trust_remote_code",
+        type=bool,
+        default=False,
+        help=(
+            "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+            "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+            "execute code present on the Hub on your local machine."
+        ),
+    )
     parser.add_argument(
         "--checkpointing_steps",
         type=str,
@@ -362,17 +372,27 @@ def main():
     # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.
     if args.config_name:
-        config = AutoConfig.from_pretrained(args.config_name)
+        config = AutoConfig.from_pretrained(
+            args.config_name,
+            trust_remote_code=args.trust_remote_code,
+        )
     elif args.model_name_or_path:
-        config = AutoConfig.from_pretrained(args.model_name_or_path)
+        config = AutoConfig.from_pretrained(
+            args.model_name_or_path,
+            trust_remote_code=args.trust_remote_code,
+        )
     else:
         config = CONFIG_MAPPING[args.model_type]()
         logger.warning("You are instantiating a new config instance from scratch.")
 
     if args.tokenizer_name:
-        tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=not args.use_slow_tokenizer)
+        tokenizer = AutoTokenizer.from_pretrained(
+            args.tokenizer_name, use_fast=not args.use_slow_tokenizer, trust_remote_code=args.trust_remote_code
+        )
     elif args.model_name_or_path:
-        tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=not args.use_slow_tokenizer)
+        tokenizer = AutoTokenizer.from_pretrained(
+            args.model_name_or_path, use_fast=not args.use_slow_tokenizer, trust_remote_code=args.trust_remote_code
+        )
     else:
         raise ValueError(
             "You are instantiating a new tokenizer from scratch. This is not supported by this script."
@@ -385,10 +405,11 @@ def main():
             from_tf=bool(".ckpt" in args.model_name_or_path),
             config=config,
             low_cpu_mem_usage=args.low_cpu_mem_usage,
+            trust_remote_code=args.trust_remote_code,
         )
     else:
         logger.info("Training new model from scratch")
-        model = AutoModelForCausalLM.from_config(config)
+        model = AutoModelForCausalLM.from_config(config, trust_remote_code=args.trust_remote_code)
 
     # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch
     # on a small vocab and want a smaller embedding size, remove this test.
diff --git a/examples/pytorch/language-modeling/run_mlm.py b/examples/pytorch/language-modeling/run_mlm.py
index d6c756edc3..84347804db 100755
--- a/examples/pytorch/language-modeling/run_mlm.py
+++ b/examples/pytorch/language-modeling/run_mlm.py
@@ -123,6 +123,16 @@ class ModelArguments:
             "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
         },
     )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+                "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+                "execute code present on the Hub on your local machine."
+            )
+        },
+    )
     low_cpu_mem_usage: bool = field(
         default=False,
         metadata={
@@ -380,6 +390,7 @@ def main():
         "cache_dir": model_args.cache_dir,
         "revision": model_args.model_revision,
         "token": model_args.token,
+        "trust_remote_code": model_args.trust_remote_code,
     }
     if model_args.config_name:
         config = AutoConfig.from_pretrained(model_args.config_name, **config_kwargs)
@@ -398,6 +409,7 @@ def main():
         "use_fast": model_args.use_fast_tokenizer,
         "revision": model_args.model_revision,
         "token": model_args.token,
+        "trust_remote_code": model_args.trust_remote_code,
     }
     if model_args.tokenizer_name:
         tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, **tokenizer_kwargs)
@@ -417,11 +429,12 @@ def main():
             cache_dir=model_args.cache_dir,
             revision=model_args.model_revision,
             token=model_args.token,
+            trust_remote_code=model_args.trust_remote_code,
             low_cpu_mem_usage=model_args.low_cpu_mem_usage,
         )
     else:
         logger.info("Training new model from scratch")
-        model = AutoModelForMaskedLM.from_config(config)
+        model = AutoModelForMaskedLM.from_config(config, trust_remote_code=model_args.trust_remote_code)
 
     # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch
     # on a small vocab and want a smaller embedding size, remove this test.
diff --git a/examples/pytorch/language-modeling/run_mlm_no_trainer.py b/examples/pytorch/language-modeling/run_mlm_no_trainer.py
index af1e8d5209..2fe8beca1b 100755
--- a/examples/pytorch/language-modeling/run_mlm_no_trainer.py
+++ b/examples/pytorch/language-modeling/run_mlm_no_trainer.py
@@ -200,6 +200,16 @@ def parse_args():
         "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`."
     )
     parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.")
+    parser.add_argument(
+        "--trust_remote_code",
+        type=bool,
+        default=False,
+        help=(
+            "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+            "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+            "execute code present on the Hub on your local machine."
+        ),
+    )
     parser.add_argument(
         "--checkpointing_steps",
         type=str,
@@ -367,17 +377,21 @@ def main():
     # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.
     if args.config_name:
-        config = AutoConfig.from_pretrained(args.config_name)
+        config = AutoConfig.from_pretrained(args.config_name, trust_remote_code=args.trust_remote_code)
     elif args.model_name_or_path:
-        config = AutoConfig.from_pretrained(args.model_name_or_path)
+        config = AutoConfig.from_pretrained(args.model_name_or_path, trust_remote_code=args.trust_remote_code)
     else:
         config = CONFIG_MAPPING[args.model_type]()
         logger.warning("You are instantiating a new config instance from scratch.")
 
     if args.tokenizer_name:
-        tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=not args.use_slow_tokenizer)
+        tokenizer = AutoTokenizer.from_pretrained(
+            args.tokenizer_name, use_fast=not args.use_slow_tokenizer, trust_remote_code=args.trust_remote_code
+        )
     elif args.model_name_or_path:
-        tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=not args.use_slow_tokenizer)
+        tokenizer = AutoTokenizer.from_pretrained(
+            args.model_name_or_path, use_fast=not args.use_slow_tokenizer, trust_remote_code=args.trust_remote_code
+        )
     else:
         raise ValueError(
             "You are instantiating a new tokenizer from scratch. This is not supported by this script."
@@ -390,10 +404,11 @@ def main():
             from_tf=bool(".ckpt" in args.model_name_or_path),
             config=config,
             low_cpu_mem_usage=args.low_cpu_mem_usage,
+            trust_remote_code=args.trust_remote_code,
         )
     else:
         logger.info("Training new model from scratch")
-        model = AutoModelForMaskedLM.from_config(config)
+        model = AutoModelForMaskedLM.from_config(config, trust_remote_code=args.trust_remote_code)
 
     # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch
     # on a small vocab and want a smaller embedding size, remove this test.
diff --git a/examples/pytorch/multiple-choice/run_swag.py b/examples/pytorch/multiple-choice/run_swag.py
index 61f72fd605..d5f4be5699 100755
--- a/examples/pytorch/multiple-choice/run_swag.py
+++ b/examples/pytorch/multiple-choice/run_swag.py
@@ -95,6 +95,16 @@ class ModelArguments:
             "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
         },
     )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+                "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+                "execute code present on the Hub on your local machine."
+            )
+        },
+    )
 
 
 @dataclass
@@ -328,6 +338,7 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
     tokenizer = AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
@@ -335,6 +346,7 @@ def main():
         use_fast=model_args.use_fast_tokenizer,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
     model = AutoModelForMultipleChoice.from_pretrained(
         model_args.model_name_or_path,
@@ -343,6 +355,7 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
 
     # When using your own dataset or a different dataset from swag, you will probably need to change this.
diff --git a/examples/pytorch/multiple-choice/run_swag_no_trainer.py b/examples/pytorch/multiple-choice/run_swag_no_trainer.py
index 26ae2d4944..e7ec89a429 100755
--- a/examples/pytorch/multiple-choice/run_swag_no_trainer.py
+++ b/examples/pytorch/multiple-choice/run_swag_no_trainer.py
@@ -182,6 +182,16 @@ def parse_args():
         "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`."
     )
     parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.")
+    parser.add_argument(
+        "--trust_remote_code",
+        type=bool,
+        default=False,
+        help=(
+            "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+            "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+            "execute code present on the Hub on your local machine."
+        ),
+    )
     parser.add_argument(
         "--checkpointing_steps",
         type=str,
@@ -374,17 +384,21 @@ def main():
     # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.
     if args.config_name:
-        config = AutoConfig.from_pretrained(args.model_name_or_path)
+        config = AutoConfig.from_pretrained(args.model_name_or_path, trust_remote_code=args.trust_remote_code)
     elif args.model_name_or_path:
-        config = AutoConfig.from_pretrained(args.model_name_or_path)
+        config = AutoConfig.from_pretrained(args.model_name_or_path, trust_remote_code=args.trust_remote_code)
     else:
         config = CONFIG_MAPPING[args.model_type]()
         logger.warning("You are instantiating a new config instance from scratch.")
 
     if args.tokenizer_name:
-        tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=not args.use_slow_tokenizer)
+        tokenizer = AutoTokenizer.from_pretrained(
+            args.tokenizer_name, use_fast=not args.use_slow_tokenizer, trust_remote_code=args.trust_remote_code
+        )
     elif args.model_name_or_path:
-        tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=not args.use_slow_tokenizer)
+        tokenizer = AutoTokenizer.from_pretrained(
+            args.model_name_or_path, use_fast=not args.use_slow_tokenizer, trust_remote_code=args.trust_remote_code
+        )
     else:
         raise ValueError(
             "You are instantiating a new tokenizer from scratch. This is not supported by this script."
@@ -396,10 +410,11 @@ def main():
             args.model_name_or_path,
             from_tf=bool(".ckpt" in args.model_name_or_path),
             config=config,
+            trust_remote_code=args.trust_remote_code,
         )
     else:
         logger.info("Training new model from scratch")
-        model = AutoModelForMultipleChoice.from_config(config)
+        model = AutoModelForMultipleChoice.from_config(config, trust_remote_code=args.trust_remote_code)
 
     # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch
     # on a small vocab and want a smaller embedding size, remove this test.
diff --git a/examples/pytorch/question-answering/run_qa.py b/examples/pytorch/question-answering/run_qa.py
index d823b28256..76f836ece6 100755
--- a/examples/pytorch/question-answering/run_qa.py
+++ b/examples/pytorch/question-answering/run_qa.py
@@ -95,6 +95,16 @@ class ModelArguments:
             "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
         },
     )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+                "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+                "execute code present on the Hub on your local machine."
+            )
+        },
+    )
 
 
 @dataclass
@@ -336,6 +346,7 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
     tokenizer = AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
@@ -343,6 +354,7 @@ def main():
         use_fast=True,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
     model = AutoModelForQuestionAnswering.from_pretrained(
         model_args.model_name_or_path,
@@ -351,6 +363,7 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
 
     # Tokenizer check: this script requires a fast tokenizer.
diff --git a/examples/pytorch/question-answering/run_qa_no_trainer.py b/examples/pytorch/question-answering/run_qa_no_trainer.py
index 2a7cdc7273..0fc438d70b 100755
--- a/examples/pytorch/question-answering/run_qa_no_trainer.py
+++ b/examples/pytorch/question-answering/run_qa_no_trainer.py
@@ -273,6 +273,16 @@ def parse_args():
         "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`."
     )
     parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.")
+    parser.add_argument(
+        "--trust_remote_code",
+        type=bool,
+        default=False,
+        help=(
+            "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+            "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+            "execute code present on the Hub on your local machine."
+        ),
+    )
     parser.add_argument(
         "--checkpointing_steps",
         type=str,
@@ -415,17 +425,21 @@ def main():
     # download model & vocab.
 
     if args.config_name:
-        config = AutoConfig.from_pretrained(args.config_name)
+        config = AutoConfig.from_pretrained(args.config_name, trust_remote_code=args.trust_remote_code)
     elif args.model_name_or_path:
-        config = AutoConfig.from_pretrained(args.model_name_or_path)
+        config = AutoConfig.from_pretrained(args.model_name_or_path, trust_remote_code=args.trust_remote_code)
     else:
         config = CONFIG_MAPPING[args.model_type]()
         logger.warning("You are instantiating a new config instance from scratch.")
 
     if args.tokenizer_name:
-        tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
+        tokenizer = AutoTokenizer.from_pretrained(
+            args.tokenizer_name, use_fast=True, trust_remote_code=args.trust_remote_code
+        )
     elif args.model_name_or_path:
-        tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=True)
+        tokenizer = AutoTokenizer.from_pretrained(
+            args.model_name_or_path, use_fast=True, trust_remote_code=args.trust_remote_code
+        )
     else:
         raise ValueError(
             "You are instantiating a new tokenizer from scratch. This is not supported by this script."
@@ -437,10 +451,11 @@ def main():
             args.model_name_or_path,
             from_tf=bool(".ckpt" in args.model_name_or_path),
             config=config,
+            trust_remote_code=args.trust_remote_code,
         )
     else:
         logger.info("Training new model from scratch")
-        model = AutoModelForQuestionAnswering.from_config(config)
+        model = AutoModelForQuestionAnswering.from_config(config, trust_remote_code=args.trust_remote_code)
 
     # Preprocessing the datasets.
     # Preprocessing is slighlty different for training and evaluation.
diff --git a/examples/pytorch/question-answering/run_seq2seq_qa.py b/examples/pytorch/question-answering/run_seq2seq_qa.py
index ac3fb64974..404a27ba3a 100644
--- a/examples/pytorch/question-answering/run_seq2seq_qa.py
+++ b/examples/pytorch/question-answering/run_seq2seq_qa.py
@@ -96,6 +96,16 @@ class ModelArguments:
             "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
         },
     )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+                "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+                "execute code present on the Hub on your local machine."
+            )
+        },
+    )
 
 
 @dataclass
@@ -381,6 +391,7 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
     tokenizer = AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
@@ -388,6 +399,7 @@ def main():
         use_fast=model_args.use_fast_tokenizer,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
     model = AutoModelForSeq2SeqLM.from_pretrained(
         model_args.model_name_or_path,
@@ -396,6 +408,7 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
 
     # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch
diff --git a/examples/pytorch/semantic-segmentation/run_semantic_segmentation.py b/examples/pytorch/semantic-segmentation/run_semantic_segmentation.py
index ce63e15fac..a7bde9eb21 100644
--- a/examples/pytorch/semantic-segmentation/run_semantic_segmentation.py
+++ b/examples/pytorch/semantic-segmentation/run_semantic_segmentation.py
@@ -257,6 +257,16 @@ class ModelArguments:
             "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
         },
     )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+                "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+                "execute code present on the Hub on your local machine."
+            )
+        },
+    )
 
 
 def main():
@@ -393,6 +403,7 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
     model = AutoModelForSemanticSegmentation.from_pretrained(
         model_args.model_name_or_path,
@@ -401,12 +412,14 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
     image_processor = AutoImageProcessor.from_pretrained(
         model_args.image_processor_name or model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
 
     # Define torchvision transforms to be applied to each image + target.
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 af9880901c..f0518ada5d 100644
--- a/examples/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
+++ b/examples/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
@@ -273,6 +273,16 @@ def parse_args():
         "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`."
     )
     parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.")
+    parser.add_argument(
+        "--trust_remote_code",
+        type=bool,
+        default=False,
+        help=(
+            "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+            "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+            "execute code present on the Hub on your local machine."
+        ),
+    )
     parser.add_argument(
         "--checkpointing_steps",
         type=str,
@@ -400,9 +410,15 @@ def main():
     label2id = {v: k for k, v in id2label.items()}
 
     # Load pretrained model and image processor
-    config = AutoConfig.from_pretrained(args.model_name_or_path, id2label=id2label, label2id=label2id)
-    image_processor = AutoImageProcessor.from_pretrained(args.model_name_or_path)
-    model = AutoModelForSemanticSegmentation.from_pretrained(args.model_name_or_path, config=config)
+    config = AutoConfig.from_pretrained(
+        args.model_name_or_path, id2label=id2label, label2id=label2id, trust_remote_code=args.trust_remote_code
+    )
+    image_processor = AutoImageProcessor.from_pretrained(
+        args.model_name_or_path, trust_remote_code=args.trust_remote_code
+    )
+    model = AutoModelForSemanticSegmentation.from_pretrained(
+        args.model_name_or_path, config=config, trust_remote_code=args.trust_remote_code
+    )
 
     # Preprocessing the datasets
     # Define torchvision transforms to be applied to each image + target.
diff --git a/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py b/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py
index bff864ddb3..3674dd7d41 100755
--- a/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py
+++ b/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py
@@ -244,6 +244,16 @@ class DataTrainingArguments:
             "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
         },
     )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+                "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+                "execute code present on the Hub on your local machine."
+            )
+        },
+    )
     unk_token: str = field(
         default="[UNK]",
         metadata={"help": "The unk token for the tokenizer"},
@@ -505,6 +515,7 @@ def main():
         model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         token=data_args.token,
+        trust_remote_code=data_args.trust_remote_code,
     )
 
     # 4. Next, if no tokenizer file is defined,
@@ -561,12 +572,14 @@ def main():
     tokenizer = AutoTokenizer.from_pretrained(
         tokenizer_name_or_path,
         token=data_args.token,
+        trust_remote_code=data_args.trust_remote_code,
         **tokenizer_kwargs,
     )
     feature_extractor = AutoFeatureExtractor.from_pretrained(
         model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         token=data_args.token,
+        trust_remote_code=data_args.trust_remote_code,
     )
 
     # adapt config
@@ -595,6 +608,7 @@ def main():
         cache_dir=model_args.cache_dir,
         config=config,
         token=data_args.token,
+        trust_remote_code=data_args.trust_remote_code,
     )
 
     # freeze encoder
diff --git a/examples/pytorch/speech-recognition/run_speech_recognition_ctc_adapter.py b/examples/pytorch/speech-recognition/run_speech_recognition_ctc_adapter.py
index be9021874a..b91fc87d05 100755
--- a/examples/pytorch/speech-recognition/run_speech_recognition_ctc_adapter.py
+++ b/examples/pytorch/speech-recognition/run_speech_recognition_ctc_adapter.py
@@ -247,6 +247,16 @@ class DataTrainingArguments:
             "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
         },
     )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+                "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+                "execute code present on the Hub on your local machine."
+            )
+        },
+    )
     unk_token: str = field(
         default="[UNK]",
         metadata={"help": "The unk token for the tokenizer"},
@@ -501,6 +511,7 @@ def main():
         model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         token=data_args.token,
+        trust_remote_code=data_args.trust_remote_code,
     )
 
     # 4. Next, if no tokenizer file is defined,
@@ -517,6 +528,7 @@ def main():
         tokenizer = AutoTokenizer.from_pretrained(
             tokenizer_name_or_path,
             token=data_args.token,
+            trust_remote_code=data_args.trust_remote_code,
         )
         vocab_dict = tokenizer.vocab.copy()
         if tokenizer.target_lang is None:
@@ -584,12 +596,14 @@ def main():
     tokenizer = AutoTokenizer.from_pretrained(
         tokenizer_name_or_path,
         token=data_args.token,
+        trust_remote_code=data_args.trust_remote_code,
         **tokenizer_kwargs,
     )
     feature_extractor = AutoFeatureExtractor.from_pretrained(
         model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         token=data_args.token,
+        trust_remote_code=data_args.trust_remote_code,
     )
 
     # adapt config
@@ -615,6 +629,7 @@ def main():
         cache_dir=model_args.cache_dir,
         config=config,
         token=data_args.token,
+        trust_remote_code=data_args.trust_remote_code,
         ignore_mismatched_sizes=True,
     )
 
diff --git a/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py b/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py
index aea7d3b7fd..f9744994ad 100755
--- a/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py
+++ b/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py
@@ -101,6 +101,16 @@ class ModelArguments:
             "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
         },
     )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+                "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+                "execute code present on the Hub on your local machine."
+            )
+        },
+    )
     freeze_feature_encoder: bool = field(
         default=True, metadata={"help": "Whether to freeze the feature encoder layers of the model."}
     )
@@ -384,6 +394,7 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
 
     config.update({"forced_decoder_ids": model_args.forced_decoder_ids, "suppress_tokens": model_args.suppress_tokens})
@@ -397,6 +408,7 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
     tokenizer = AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
@@ -404,6 +416,7 @@ def main():
         use_fast=model_args.use_fast_tokenizer,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
     model = AutoModelForSpeechSeq2Seq.from_pretrained(
         model_args.model_name_or_path,
@@ -411,6 +424,7 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
 
     if model.config.decoder_start_token_id is None:
diff --git a/examples/pytorch/summarization/run_summarization.py b/examples/pytorch/summarization/run_summarization.py
index 4a27df7582..d145a8549d 100755
--- a/examples/pytorch/summarization/run_summarization.py
+++ b/examples/pytorch/summarization/run_summarization.py
@@ -115,6 +115,16 @@ class ModelArguments:
             "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
         },
     )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+                "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+                "execute code present on the Hub on your local machine."
+            )
+        },
+    )
     resize_position_embeddings: Optional[bool] = field(
         default=None,
         metadata={
@@ -431,6 +441,7 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
     tokenizer = AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
@@ -438,6 +449,7 @@ def main():
         use_fast=model_args.use_fast_tokenizer,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
     model = AutoModelForSeq2SeqLM.from_pretrained(
         model_args.model_name_or_path,
@@ -446,6 +458,7 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
 
     # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch
diff --git a/examples/pytorch/summarization/run_summarization_no_trainer.py b/examples/pytorch/summarization/run_summarization_no_trainer.py
index 543b6bfaa2..65a924a800 100644
--- a/examples/pytorch/summarization/run_summarization_no_trainer.py
+++ b/examples/pytorch/summarization/run_summarization_no_trainer.py
@@ -266,6 +266,16 @@ def parse_args():
         "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`."
     )
     parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.")
+    parser.add_argument(
+        "--trust_remote_code",
+        type=bool,
+        default=False,
+        help=(
+            "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+            "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+            "execute code present on the Hub on your local machine."
+        ),
+    )
     parser.add_argument(
         "--checkpointing_steps",
         type=str,
@@ -406,17 +416,21 @@ def main():
     # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.
     if args.config_name:
-        config = AutoConfig.from_pretrained(args.config_name)
+        config = AutoConfig.from_pretrained(args.config_name, trust_remote_code=args.trust_remote_code)
     elif args.model_name_or_path:
-        config = AutoConfig.from_pretrained(args.model_name_or_path)
+        config = AutoConfig.from_pretrained(args.model_name_or_path, trust_remote_code=args.trust_remote_code)
     else:
         config = CONFIG_MAPPING[args.model_type]()
         logger.warning("You are instantiating a new config instance from scratch.")
 
     if args.tokenizer_name:
-        tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=not args.use_slow_tokenizer)
+        tokenizer = AutoTokenizer.from_pretrained(
+            args.tokenizer_name, use_fast=not args.use_slow_tokenizer, trust_remote_code=args.trust_remote_code
+        )
     elif args.model_name_or_path:
-        tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=not args.use_slow_tokenizer)
+        tokenizer = AutoTokenizer.from_pretrained(
+            args.model_name_or_path, use_fast=not args.use_slow_tokenizer, trust_remote_code=args.trust_remote_code
+        )
     else:
         raise ValueError(
             "You are instantiating a new tokenizer from scratch. This is not supported by this script."
@@ -428,10 +442,11 @@ def main():
             args.model_name_or_path,
             from_tf=bool(".ckpt" in args.model_name_or_path),
             config=config,
+            trust_remote_code=args.trust_remote_code,
         )
     else:
         logger.info("Training new model from scratch")
-        model = AutoModelForSeq2SeqLM.from_config(config)
+        model = AutoModelForSeq2SeqLM.from_config(config, trust_remote_code=args.trust_remote_code)
 
     # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch
     # on a small vocab and want a smaller embedding size, remove this test.
diff --git a/examples/pytorch/text-classification/run_classification.py b/examples/pytorch/text-classification/run_classification.py
index a5a4ad49f8..68d2475fd5 100755
--- a/examples/pytorch/text-classification/run_classification.py
+++ b/examples/pytorch/text-classification/run_classification.py
@@ -243,6 +243,16 @@ class ModelArguments:
             "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
         },
     )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+                "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+                "execute code present on the Hub on your local machine."
+            )
+        },
+    )
     ignore_mismatched_sizes: bool = field(
         default=False,
         metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."},
@@ -482,6 +492,7 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
 
     if is_regression:
@@ -500,6 +511,7 @@ def main():
         use_fast=model_args.use_fast_tokenizer,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
     model = AutoModelForSequenceClassification.from_pretrained(
         model_args.model_name_or_path,
@@ -508,6 +520,7 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
         ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
     )
 
diff --git a/examples/pytorch/text-classification/run_glue.py b/examples/pytorch/text-classification/run_glue.py
index 3f8bcc2fbb..1d702ef524 100755
--- a/examples/pytorch/text-classification/run_glue.py
+++ b/examples/pytorch/text-classification/run_glue.py
@@ -204,6 +204,16 @@ class ModelArguments:
             "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
         },
     )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+                "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+                "execute code present on the Hub on your local machine."
+            )
+        },
+    )
     ignore_mismatched_sizes: bool = field(
         default=False,
         metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."},
@@ -375,6 +385,7 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
     tokenizer = AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
@@ -382,6 +393,7 @@ def main():
         use_fast=model_args.use_fast_tokenizer,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
     model = AutoModelForSequenceClassification.from_pretrained(
         model_args.model_name_or_path,
@@ -390,6 +402,7 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
         ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
     )
 
diff --git a/examples/pytorch/text-classification/run_glue_no_trainer.py b/examples/pytorch/text-classification/run_glue_no_trainer.py
index e137f87638..1ab8bb0db8 100644
--- a/examples/pytorch/text-classification/run_glue_no_trainer.py
+++ b/examples/pytorch/text-classification/run_glue_no_trainer.py
@@ -156,6 +156,16 @@ def parse_args():
         "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`."
     )
     parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.")
+    parser.add_argument(
+        "--trust_remote_code",
+        type=bool,
+        default=False,
+        help=(
+            "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+            "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+            "execute code present on the Hub on your local machine."
+        ),
+    )
     parser.add_argument(
         "--checkpointing_steps",
         type=str,
@@ -309,13 +319,21 @@ def main():
     #
     # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.
-    config = AutoConfig.from_pretrained(args.model_name_or_path, num_labels=num_labels, finetuning_task=args.task_name)
-    tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=not args.use_slow_tokenizer)
+    config = AutoConfig.from_pretrained(
+        args.model_name_or_path,
+        num_labels=num_labels,
+        finetuning_task=args.task_name,
+        trust_remote_code=args.trust_remote_code,
+    )
+    tokenizer = AutoTokenizer.from_pretrained(
+        args.model_name_or_path, use_fast=not args.use_slow_tokenizer, trust_remote_code=args.trust_remote_code
+    )
     model = AutoModelForSequenceClassification.from_pretrained(
         args.model_name_or_path,
         from_tf=bool(".ckpt" in args.model_name_or_path),
         config=config,
         ignore_mismatched_sizes=args.ignore_mismatched_sizes,
+        trust_remote_code=args.trust_remote_code,
     )
 
     # Preprocessing the datasets
diff --git a/examples/pytorch/text-classification/run_xnli.py b/examples/pytorch/text-classification/run_xnli.py
index 459a59282e..1fae632518 100755
--- a/examples/pytorch/text-classification/run_xnli.py
+++ b/examples/pytorch/text-classification/run_xnli.py
@@ -168,6 +168,16 @@ class ModelArguments:
             "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
         },
     )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+                "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+                "execute code present on the Hub on your local machine."
+            )
+        },
+    )
     ignore_mismatched_sizes: bool = field(
         default=False,
         metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."},
@@ -292,6 +302,7 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
     tokenizer = AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
@@ -300,6 +311,7 @@ def main():
         use_fast=model_args.use_fast_tokenizer,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
     model = AutoModelForSequenceClassification.from_pretrained(
         model_args.model_name_or_path,
@@ -308,6 +320,7 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
         ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
     )
 
diff --git a/examples/pytorch/token-classification/run_ner.py b/examples/pytorch/token-classification/run_ner.py
index 0e9d160412..fae5142c0c 100755
--- a/examples/pytorch/token-classification/run_ner.py
+++ b/examples/pytorch/token-classification/run_ner.py
@@ -95,6 +95,16 @@ class ModelArguments:
             "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
         },
     )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+                "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+                "execute code present on the Hub on your local machine."
+            )
+        },
+    )
     ignore_mismatched_sizes: bool = field(
         default=False,
         metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."},
@@ -362,6 +372,7 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
 
     tokenizer_name_or_path = model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path
@@ -372,6 +383,7 @@ def main():
             use_fast=True,
             revision=model_args.model_revision,
             token=model_args.token,
+            trust_remote_code=model_args.trust_remote_code,
             add_prefix_space=True,
         )
     else:
@@ -381,6 +393,7 @@ def main():
             use_fast=True,
             revision=model_args.model_revision,
             token=model_args.token,
+            trust_remote_code=model_args.trust_remote_code,
         )
 
     model = AutoModelForTokenClassification.from_pretrained(
@@ -390,6 +403,7 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
         ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
     )
 
diff --git a/examples/pytorch/token-classification/run_ner_no_trainer.py b/examples/pytorch/token-classification/run_ner_no_trainer.py
index dc57b514e7..a38f0e47d0 100755
--- a/examples/pytorch/token-classification/run_ner_no_trainer.py
+++ b/examples/pytorch/token-classification/run_ner_no_trainer.py
@@ -210,6 +210,16 @@ def parse_args():
         "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`."
     )
     parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.")
+    parser.add_argument(
+        "--trust_remote_code",
+        type=bool,
+        default=False,
+        help=(
+            "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+            "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+            "execute code present on the Hub on your local machine."
+        ),
+    )
     parser.add_argument(
         "--checkpointing_steps",
         type=str,
@@ -388,9 +398,13 @@ def main():
     # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.
     if args.config_name:
-        config = AutoConfig.from_pretrained(args.config_name, num_labels=num_labels)
+        config = AutoConfig.from_pretrained(
+            args.config_name, num_labels=num_labels, trust_remote_code=args.trust_remote_code
+        )
     elif args.model_name_or_path:
-        config = AutoConfig.from_pretrained(args.model_name_or_path, num_labels=num_labels)
+        config = AutoConfig.from_pretrained(
+            args.model_name_or_path, num_labels=num_labels, trust_remote_code=args.trust_remote_code
+        )
     else:
         config = CONFIG_MAPPING[args.model_type]()
         logger.warning("You are instantiating a new config instance from scratch.")
@@ -403,9 +417,13 @@ def main():
         )
 
     if config.model_type in {"bloom", "gpt2", "roberta"}:
-        tokenizer = AutoTokenizer.from_pretrained(tokenizer_name_or_path, use_fast=True, add_prefix_space=True)
+        tokenizer = AutoTokenizer.from_pretrained(
+            tokenizer_name_or_path, use_fast=True, add_prefix_space=True, trust_remote_code=args.trust_remote_code
+        )
     else:
-        tokenizer = AutoTokenizer.from_pretrained(tokenizer_name_or_path, use_fast=True)
+        tokenizer = AutoTokenizer.from_pretrained(
+            tokenizer_name_or_path, use_fast=True, trust_remote_code=args.trust_remote_code
+        )
 
     if args.model_name_or_path:
         model = AutoModelForTokenClassification.from_pretrained(
@@ -413,10 +431,11 @@ def main():
             from_tf=bool(".ckpt" in args.model_name_or_path),
             config=config,
             ignore_mismatched_sizes=args.ignore_mismatched_sizes,
+            trust_remote_code=args.trust_remote_code,
         )
     else:
         logger.info("Training new model from scratch")
-        model = AutoModelForTokenClassification.from_config(config)
+        model = AutoModelForTokenClassification.from_config(config, trust_remote_code=args.trust_remote_code)
 
     # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch
     # on a small vocab and want a smaller embedding size, remove this test.
diff --git a/examples/pytorch/translation/run_translation.py b/examples/pytorch/translation/run_translation.py
index 179ef71ad3..1e39ab31ba 100755
--- a/examples/pytorch/translation/run_translation.py
+++ b/examples/pytorch/translation/run_translation.py
@@ -105,6 +105,16 @@ class ModelArguments:
             "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
         },
     )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+                "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+                "execute code present on the Hub on your local machine."
+            )
+        },
+    )
 
 
 @dataclass
@@ -380,6 +390,7 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
     tokenizer = AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
@@ -387,6 +398,7 @@ def main():
         use_fast=model_args.use_fast_tokenizer,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
     model = AutoModelForSeq2SeqLM.from_pretrained(
         model_args.model_name_or_path,
@@ -395,6 +407,7 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
 
     # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch
diff --git a/examples/pytorch/translation/run_translation_no_trainer.py b/examples/pytorch/translation/run_translation_no_trainer.py
index 77efa14f4f..b61e715d37 100644
--- a/examples/pytorch/translation/run_translation_no_trainer.py
+++ b/examples/pytorch/translation/run_translation_no_trainer.py
@@ -257,6 +257,16 @@ def parse_args():
         "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`."
     )
     parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.")
+    parser.add_argument(
+        "--trust_remote_code",
+        type=bool,
+        default=False,
+        help=(
+            "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+            "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+            "execute code present on the Hub on your local machine."
+        ),
+    )
     parser.add_argument(
         "--checkpointing_steps",
         type=str,
@@ -386,17 +396,21 @@ def main():
     # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.
     if args.config_name:
-        config = AutoConfig.from_pretrained(args.config_name)
+        config = AutoConfig.from_pretrained(args.config_name, trust_remote_code=args.trust_remote_code)
     elif args.model_name_or_path:
-        config = AutoConfig.from_pretrained(args.model_name_or_path)
+        config = AutoConfig.from_pretrained(args.model_name_or_path, trust_remote_code=args.trust_remote_code)
     else:
         config = CONFIG_MAPPING[args.model_type]()
         logger.warning("You are instantiating a new config instance from scratch.")
 
     if args.tokenizer_name:
-        tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=not args.use_slow_tokenizer)
+        tokenizer = AutoTokenizer.from_pretrained(
+            args.tokenizer_name, use_fast=not args.use_slow_tokenizer, trust_remote_code=args.trust_remote_code
+        )
     elif args.model_name_or_path:
-        tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=not args.use_slow_tokenizer)
+        tokenizer = AutoTokenizer.from_pretrained(
+            args.model_name_or_path, use_fast=not args.use_slow_tokenizer, trust_remote_code=args.trust_remote_code
+        )
     else:
         raise ValueError(
             "You are instantiating a new tokenizer from scratch. This is not supported by this script."
@@ -408,10 +422,11 @@ def main():
             args.model_name_or_path,
             from_tf=bool(".ckpt" in args.model_name_or_path),
             config=config,
+            trust_remote_code=args.trust_remote_code,
         )
     else:
         logger.info("Training new model from scratch")
-        model = AutoModelForSeq2SeqLM.from_config(config)
+        model = AutoModelForSeq2SeqLM.from_config(config, trust_remote_code=args.trust_remote_code)
 
     # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch
     # on a small vocab and want a smaller embedding size, remove this test.
diff --git a/examples/tensorflow/image-classification/run_image_classification.py b/examples/tensorflow/image-classification/run_image_classification.py
index 23e04c9318..5223b7db0b 100644
--- a/examples/tensorflow/image-classification/run_image_classification.py
+++ b/examples/tensorflow/image-classification/run_image_classification.py
@@ -173,6 +173,16 @@ class ModelArguments:
             "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
         },
     )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+                "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+                "execute code present on the Hub on your local machine."
+            )
+        },
+    )
     ignore_mismatched_sizes: bool = field(
         default=False,
         metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."},
@@ -323,12 +333,14 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
     image_processor = AutoImageProcessor.from_pretrained(
         model_args.image_processor_name or model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
 
     # If we don't have a validation split, split off a percentage of train as validation.
@@ -449,6 +461,7 @@ def main():
             cache_dir=model_args.cache_dir,
             revision=model_args.model_revision,
             token=model_args.token,
+            trust_remote_code=model_args.trust_remote_code,
             ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
         )
         num_replicas = training_args.strategy.num_replicas_in_sync
diff --git a/examples/tensorflow/language-modeling/run_clm.py b/examples/tensorflow/language-modeling/run_clm.py
index 7c068d5c8c..43d5a1d8a3 100755
--- a/examples/tensorflow/language-modeling/run_clm.py
+++ b/examples/tensorflow/language-modeling/run_clm.py
@@ -128,6 +128,16 @@ class ModelArguments:
             "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
         },
     )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+                "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+                "execute code present on the Hub on your local machine."
+            )
+        },
+    )
 
     def __post_init__(self):
         if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
@@ -366,17 +376,26 @@ def main():
     # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.
     if model_args.config_name:
-        config = AutoConfig.from_pretrained(model_args.config_name)
+        config = AutoConfig.from_pretrained(
+            model_args.config_name,
+            trust_remote_code=model_args.trust_remote_code,
+        )
     elif model_args.model_name_or_path:
-        config = AutoConfig.from_pretrained(model_args.model_name_or_path)
+        config = AutoConfig.from_pretrained(
+            model_args.model_name_or_path, trust_remote_code=model_args.trust_remote_code
+        )
     else:
         config = CONFIG_MAPPING[model_args.model_type]()
         logger.warning("You are instantiating a new config instance from scratch.")
 
     if model_args.tokenizer_name:
-        tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name)
+        tokenizer = AutoTokenizer.from_pretrained(
+            model_args.tokenizer_name, trust_remote_code=model_args.trust_remote_code
+        )
     elif model_args.model_name_or_path:
-        tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path)
+        tokenizer = AutoTokenizer.from_pretrained(
+            model_args.model_name_or_path, trust_remote_code=model_args.trust_remote_code
+        )
     else:
         raise ValueError(
             "You are instantiating a new tokenizer from scratch. This is not supported by this script."
@@ -479,12 +498,16 @@ def main():
     with training_args.strategy.scope():
         # region Prepare model
         if checkpoint is not None:
-            model = TFAutoModelForCausalLM.from_pretrained(checkpoint, config=config)
+            model = TFAutoModelForCausalLM.from_pretrained(
+                checkpoint, config=config, trust_remote_code=model_args.trust_remote_code
+            )
         elif model_args.model_name_or_path:
-            model = TFAutoModelForCausalLM.from_pretrained(model_args.model_name_or_path, config=config)
+            model = TFAutoModelForCausalLM.from_pretrained(
+                model_args.model_name_or_path, config=config, trust_remote_code=model_args.trust_remote_code
+            )
         else:
             logger.info("Training new model from scratch")
-            model = TFAutoModelForCausalLM.from_config(config)
+            model = TFAutoModelForCausalLM.from_config(config, trust_remote_code=model_args.trust_remote_code)
 
         # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch
         # on a small vocab and want a smaller embedding size, remove this test.
diff --git a/examples/tensorflow/language-modeling/run_mlm.py b/examples/tensorflow/language-modeling/run_mlm.py
index 7ea1a2f853..75edfa954a 100755
--- a/examples/tensorflow/language-modeling/run_mlm.py
+++ b/examples/tensorflow/language-modeling/run_mlm.py
@@ -126,6 +126,16 @@ class ModelArguments:
             "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
         },
     )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+                "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+                "execute code present on the Hub on your local machine."
+            )
+        },
+    )
 
     def __post_init__(self):
         if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
@@ -348,19 +358,25 @@ def main():
     # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.
     if checkpoint is not None:
-        config = AutoConfig.from_pretrained(checkpoint)
+        config = AutoConfig.from_pretrained(checkpoint, trust_remote_code=model_args.trust_remote_code)
     elif model_args.config_name:
-        config = AutoConfig.from_pretrained(model_args.config_name)
+        config = AutoConfig.from_pretrained(model_args.config_name, trust_remote_code=model_args.trust_remote_code)
     elif model_args.model_name_or_path:
-        config = AutoConfig.from_pretrained(model_args.model_name_or_path)
+        config = AutoConfig.from_pretrained(
+            model_args.model_name_or_path, trust_remote_code=model_args.trust_remote_code
+        )
     else:
         config = CONFIG_MAPPING[model_args.model_type]()
         logger.warning("You are instantiating a new config instance from scratch.")
 
     if model_args.tokenizer_name:
-        tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name)
+        tokenizer = AutoTokenizer.from_pretrained(
+            model_args.tokenizer_name, trust_remote_code=model_args.trust_remote_code
+        )
     elif model_args.model_name_or_path:
-        tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path)
+        tokenizer = AutoTokenizer.from_pretrained(
+            model_args.model_name_or_path, trust_remote_code=model_args.trust_remote_code
+        )
     else:
         raise ValueError(
             "You are instantiating a new tokenizer from scratch. This is not supported by this script."
@@ -495,12 +511,16 @@ def main():
     with training_args.strategy.scope():
         # region Prepare model
         if checkpoint is not None:
-            model = TFAutoModelForMaskedLM.from_pretrained(checkpoint, config=config)
+            model = TFAutoModelForMaskedLM.from_pretrained(
+                checkpoint, config=config, trust_remote_code=model_args.trust_remote_code
+            )
         elif model_args.model_name_or_path:
-            model = TFAutoModelForMaskedLM.from_pretrained(model_args.model_name_or_path, config=config)
+            model = TFAutoModelForMaskedLM.from_pretrained(
+                model_args.model_name_or_path, config=config, trust_remote_code=model_args.trust_remote_code
+            )
         else:
             logger.info("Training new model from scratch")
-            model = TFAutoModelForMaskedLM.from_config(config)
+            model = TFAutoModelForMaskedLM.from_config(config, trust_remote_code=model_args.trust_remote_code)
 
         # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch
         # on a small vocab and want a smaller embedding size, remove this test.
diff --git a/examples/tensorflow/multiple-choice/run_swag.py b/examples/tensorflow/multiple-choice/run_swag.py
index 170117ad9d..d78060dfa4 100644
--- a/examples/tensorflow/multiple-choice/run_swag.py
+++ b/examples/tensorflow/multiple-choice/run_swag.py
@@ -162,6 +162,16 @@ class ModelArguments:
             "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
         },
     )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+                "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+                "execute code present on the Hub on your local machine."
+            )
+        },
+    )
 
 
 @dataclass
@@ -349,6 +359,7 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
     tokenizer = AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
@@ -356,6 +367,7 @@ def main():
         use_fast=model_args.use_fast_tokenizer,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
     # endregion
 
@@ -442,6 +454,7 @@ def main():
             cache_dir=model_args.cache_dir,
             revision=model_args.model_revision,
             token=model_args.token,
+            trust_remote_code=model_args.trust_remote_code,
         )
 
         num_replicas = training_args.strategy.num_replicas_in_sync
diff --git a/examples/tensorflow/question-answering/run_qa.py b/examples/tensorflow/question-answering/run_qa.py
index 8bbc986d0e..6152263d6b 100755
--- a/examples/tensorflow/question-answering/run_qa.py
+++ b/examples/tensorflow/question-answering/run_qa.py
@@ -93,6 +93,16 @@ class ModelArguments:
             "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
         },
     )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+                "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+                "execute code present on the Hub on your local machine."
+            )
+        },
+    )
 
 
 @dataclass
@@ -352,6 +362,7 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
     tokenizer = AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
@@ -359,6 +370,7 @@ def main():
         use_fast=True,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
     # endregion
 
@@ -639,6 +651,7 @@ def main():
             cache_dir=model_args.cache_dir,
             revision=model_args.model_revision,
             token=model_args.token,
+            trust_remote_code=model_args.trust_remote_code,
         )
         if training_args.do_train:
             training_dataset = model.prepare_tf_dataset(
diff --git a/examples/tensorflow/summarization/run_summarization.py b/examples/tensorflow/summarization/run_summarization.py
index 82359f0921..03c434330b 100644
--- a/examples/tensorflow/summarization/run_summarization.py
+++ b/examples/tensorflow/summarization/run_summarization.py
@@ -115,6 +115,16 @@ class ModelArguments:
             "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
         },
     )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+                "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+                "execute code present on the Hub on your local machine."
+            )
+        },
+    )
 
 
 @dataclass
@@ -402,6 +412,7 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
     tokenizer = AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
@@ -409,6 +420,7 @@ def main():
         use_fast=model_args.use_fast_tokenizer,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
 
     prefix = data_args.source_prefix if data_args.source_prefix is not None else ""
@@ -527,6 +539,7 @@ def main():
             cache_dir=model_args.cache_dir,
             revision=model_args.model_revision,
             token=model_args.token,
+            trust_remote_code=model_args.trust_remote_code,
         )
 
         # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch
diff --git a/examples/tensorflow/text-classification/run_glue.py b/examples/tensorflow/text-classification/run_glue.py
index daeed0c6ea..785e88a71d 100644
--- a/examples/tensorflow/text-classification/run_glue.py
+++ b/examples/tensorflow/text-classification/run_glue.py
@@ -180,6 +180,16 @@ class ModelArguments:
             "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
         },
     )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+                "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+                "execute code present on the Hub on your local machine."
+            )
+        },
+    )
 
 
 # endregion
@@ -298,6 +308,7 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
     tokenizer = AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
@@ -305,6 +316,7 @@ def main():
         use_fast=model_args.use_fast_tokenizer,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
     # endregion
 
@@ -388,6 +400,7 @@ def main():
             cache_dir=model_args.cache_dir,
             revision=model_args.model_revision,
             token=model_args.token,
+            trust_remote_code=model_args.trust_remote_code,
         )
         # endregion
 
diff --git a/examples/tensorflow/text-classification/run_text_classification.py b/examples/tensorflow/text-classification/run_text_classification.py
index a68aed942d..0d2ea87b96 100644
--- a/examples/tensorflow/text-classification/run_text_classification.py
+++ b/examples/tensorflow/text-classification/run_text_classification.py
@@ -186,6 +186,16 @@ class ModelArguments:
             "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
         },
     )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+                "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+                "execute code present on the Hub on your local machine."
+            )
+        },
+    )
 
 
 # endregion
@@ -315,6 +325,7 @@ def main():
             cache_dir=model_args.cache_dir,
             revision=model_args.model_revision,
             token=model_args.token,
+            trust_remote_code=model_args.trust_remote_code,
         )
     else:
         config = AutoConfig.from_pretrained(
@@ -322,12 +333,14 @@ def main():
             cache_dir=model_args.cache_dir,
             revision=model_args.model_revision,
             token=model_args.token,
+            trust_remote_code=model_args.trust_remote_code,
         )
     tokenizer = AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
     # endregion
 
@@ -416,6 +429,7 @@ def main():
             cache_dir=model_args.cache_dir,
             revision=model_args.model_revision,
             token=model_args.token,
+            trust_remote_code=model_args.trust_remote_code,
         )
         # endregion
 
diff --git a/examples/tensorflow/token-classification/run_ner.py b/examples/tensorflow/token-classification/run_ner.py
index ed8ab00e4f..bcf47eb83c 100644
--- a/examples/tensorflow/token-classification/run_ner.py
+++ b/examples/tensorflow/token-classification/run_ner.py
@@ -91,6 +91,16 @@ class ModelArguments:
             "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
         },
     )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+                "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+                "execute code present on the Hub on your local machine."
+            )
+        },
+    )
 
 
 @dataclass
@@ -304,9 +314,17 @@ def main():
     # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.
     if model_args.config_name:
-        config = AutoConfig.from_pretrained(model_args.config_name, num_labels=num_labels)
+        config = AutoConfig.from_pretrained(
+            model_args.config_name,
+            num_labels=num_labels,
+            trust_remote_code=model_args.trust_remote_code,
+        )
     elif model_args.model_name_or_path:
-        config = AutoConfig.from_pretrained(model_args.model_name_or_path, num_labels=num_labels)
+        config = AutoConfig.from_pretrained(
+            model_args.model_name_or_path,
+            num_labels=num_labels,
+            trust_remote_code=model_args.trust_remote_code,
+        )
     else:
         config = CONFIG_MAPPING[model_args.model_type]()
         logger.warning("You are instantiating a new config instance from scratch.")
@@ -319,9 +337,18 @@ def main():
         )
 
     if config.model_type in {"gpt2", "roberta"}:
-        tokenizer = AutoTokenizer.from_pretrained(tokenizer_name_or_path, use_fast=True, add_prefix_space=True)
+        tokenizer = AutoTokenizer.from_pretrained(
+            tokenizer_name_or_path,
+            use_fast=True,
+            add_prefix_space=True,
+            trust_remote_code=model_args.trust_remote_code,
+        )
     else:
-        tokenizer = AutoTokenizer.from_pretrained(tokenizer_name_or_path, use_fast=True)
+        tokenizer = AutoTokenizer.from_pretrained(
+            tokenizer_name_or_path,
+            use_fast=True,
+            trust_remote_code=model_args.trust_remote_code,
+        )
     # endregion
 
     # region Preprocessing the raw datasets
@@ -392,10 +419,13 @@ def main():
             model = TFAutoModelForTokenClassification.from_pretrained(
                 model_args.model_name_or_path,
                 config=config,
+                trust_remote_code=model_args.trust_remote_code,
             )
         else:
             logger.info("Training new model from scratch")
-            model = TFAutoModelForTokenClassification.from_config(config)
+            model = TFAutoModelForTokenClassification.from_config(
+                config, trust_remote_code=model_args.trust_remote_code
+            )
 
         # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch
         # on a small vocab and want a smaller embedding size, remove this test.
diff --git a/examples/tensorflow/translation/run_translation.py b/examples/tensorflow/translation/run_translation.py
index 68e9e9c16b..2673983ec9 100644
--- a/examples/tensorflow/translation/run_translation.py
+++ b/examples/tensorflow/translation/run_translation.py
@@ -109,6 +109,16 @@ class ModelArguments:
             "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
         },
     )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+                "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+                "execute code present on the Hub on your local machine."
+            )
+        },
+    )
 
 
 @dataclass
@@ -366,6 +376,7 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
     tokenizer = AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
@@ -373,6 +384,7 @@ def main():
         use_fast=model_args.use_fast_tokenizer,
         revision=model_args.model_revision,
         token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
     )
 
     prefix = data_args.source_prefix if data_args.source_prefix is not None else ""
@@ -480,6 +492,7 @@ def main():
             cache_dir=model_args.cache_dir,
             revision=model_args.model_revision,
             token=model_args.token,
+            trust_remote_code=model_args.trust_remote_code,
         )
 
         # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch
diff --git a/templates/adding_a_new_example_script/{{cookiecutter.directory_name}}/run_{{cookiecutter.example_shortcut}}.py b/templates/adding_a_new_example_script/{{cookiecutter.directory_name}}/run_{{cookiecutter.example_shortcut}}.py
index c6b72f4fa3..12e59b8b24 100755
--- a/templates/adding_a_new_example_script/{{cookiecutter.directory_name}}/run_{{cookiecutter.example_shortcut}}.py
+++ b/templates/adding_a_new_example_script/{{cookiecutter.directory_name}}/run_{{cookiecutter.example_shortcut}}.py
@@ -122,6 +122,16 @@ class ModelArguments:
             "with private models)."
         },
     )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+                "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+                "execute code present on the Hub on your local machine."
+            )
+        },
+    )
 {% endif %}
 
 
@@ -290,6 +300,7 @@ def main():
         "cache_dir": model_args.cache_dir,
         "revision": model_args.model_revision,
         "token": True if model_args.token else None,
+        "trust_remote_code": model_args.trust_remote_code,
     }
     if model_args.config_name:
         config = AutoConfig.from_pretrained(model_args.config_name, **config_kwargs)
@@ -304,6 +315,7 @@ def main():
         "use_fast": model_args.use_fast_tokenizer,
         "revision": model_args.model_revision,
         "token": True if model_args.token else None,
+        "trust_remote_code": model_args.trust_remote_code,
     }
     if model_args.tokenizer_name:
         tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, **tokenizer_kwargs)
@@ -323,6 +335,7 @@ def main():
             cache_dir=model_args.cache_dir,
             revision=model_args.model_revision,
             token=True if model_args.token else None,
+            trust_remote_code=model_args.trust_remote_code,
         )
     else:
         logger.info("Training new model from scratch")
@@ -337,6 +350,7 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=True if model_args.token else None,
+        trust_remote_code=model_args.trust_remote_code,
     )
     tokenizer = AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
@@ -344,6 +358,7 @@ def main():
         use_fast=model_args.use_fast_tokenizer,
         revision=model_args.model_revision,
         token=True if model_args.token else None,
+        trust_remote_code=model_args.trust_remote_code,
     )
     model = AutoModelForSequenceClassification.from_pretrained(
         model_args.model_name_or_path,
@@ -352,6 +367,7 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=True if model_args.token else None,
+        trust_remote_code=model_args.trust_remote_code,
     )
 {% endif %}