huggingface · SunMarc · Oct 3, 2025 · Oct 3, 2025
diff --git a/ISSUES.md b/ISSUES.md
@@ -153,7 +153,7 @@ You are not required to read the following guidelines before opening an issue. H
     cd examples/seq2seq
     torchrun --nproc_per_node=2 ./finetune_trainer.py \
     --model_name_or_path sshleifer/distill-mbart-en-ro-12-4 --data_dir wmt_en_ro \
-    --output_dir output_dir --overwrite_output_dir \
+    --output_dir output_dir \
     --do_train --n_train 500 --num_train_epochs 1 \
     --per_device_train_batch_size 1  --freeze_embeds \
     --src_lang en_XX --tgt_lang ro_RO --task translation \

diff --git a/docs/source/ar/run_scripts.md b/docs/source/ar/run_scripts.md
@@ -93,7 +93,6 @@ python examples/pytorch/summarization/run_summarization.py \
     --output_dir /tmp/tst-summarization \
     --per_device_train_batch_size=4 \
     --per_device_eval_batch_size=4 \
-    --overwrite_output_dir \
     --predict_with_generate
 ```
 
@@ -117,7 +116,6 @@ torchrun \
     --output_dir /tmp/tst-summarization \
     --per_device_train_batch_size=4 \
     --per_device_eval_batch_size=4 \
-    --overwrite_output_dir \
     --predict_with_generate
 ```
 
@@ -140,7 +138,6 @@ python xla_spawn.py --num_cores 8 \
     --output_dir /tmp/tst-summarization \
     --per_device_train_batch_size=4 \
     --per_device_eval_batch_size=4 \
-    --overwrite_output_dir \
     --predict_with_generate
 ```
 
@@ -197,7 +194,6 @@ python examples/pytorch/summarization/run_summarization.py \
     --summary_column summary_column_name \
     --source_prefix "summarize: " \
     --output_dir /tmp/tst-summarization \
-    --overwrite_output_dir \
     --per_device_train_batch_size=4 \
     --per_device_eval_batch_size=4 \
     --predict_with_generate
@@ -225,7 +221,6 @@ python examples/pytorch/summarization/run_summarization.py \
     --output_dir /tmp/tst-summarization \
     --per_device_train_batch_size=4 \
     --per_device_eval_batch_size=4 \
-    --overwrite_output_dir \
     --predict_with_generate
 ```
 
@@ -239,25 +234,6 @@ examples/pytorch/summarization/run_summarization.py -h
 
 خيار آخر مفيد لتمكينه هو استئناف التدريب من نقطة تفتيش سابقة. سيضمن ذلك أنك تستطيع الاستمرار من حيث توقفت دون البدء من جديد إذا تم مقاطعة تدريبك. هناك طريقتان لاستئناف التدريب من نقطة تفتيش.
 
-تستخدم الطريقة الأولى المعلمة `output_dir previous_output_dir` لاستئناف التدريب من أحدث نقطة تفتيش مخزنة في `output_dir`. في هذه الحالة، يجب عليك إزالة `overwrite_output_dir`:
-
-```bash
-python examples/pytorch/summarization/run_summarization.py
-    --model_name_or_path google-t5/t5-small \
-    --do_train \
-    --do_eval \
-    --dataset_name cnn_dailymail \
-    --dataset_config "3.0.0" \
-    --source_prefix "summarize: " \
-    --output_dir /tmp/tst-summarization \
-    --per_device_train_batch_size=4 \
-    --per_device_eval_batch_size=4 \
-    --output_dir previous_output_dir \
-    --predict_with_generate
-```
-
-تستخدم الطريقة الثانية معلمة `resume_from_checkpoint path_to_specific_checkpoint` لاستئناف التدريب من مجلد نقطة تفتيش محددة.
-
 ```bash
 python examples/pytorch/summarization/run_summarization.py
     --model_name_or_path google-t5/t5-small \
@@ -269,7 +245,6 @@ python examples/pytorch/summarization/run_summarization.py
     --output_dir /tmp/tst-summarization \
     --per_device_train_batch_size=4 \
     --per_device_eval_batch_size=4 \
-    --overwrite_output_dir \
     --resume_from_checkpoint path_to_specific_checkpoint \
     --predict_with_generate
 ```
@@ -301,6 +276,5 @@ python examples/pytorch/summarization/run_summarization.py
     --output_dir /tmp/tst-summarization \
     --per_device_train_batch_size=4 \
     --per_device_eval_batch_size=4 \
-    --overwrite_output_dir \
     --predict_with_generate
 ```
diff --git a/docs/source/ar/trainer.md b/docs/source/ar/trainer.md
@@ -611,7 +611,6 @@ accelerate launch \
     --learning_rate 5e-5 \
     --num_train_epochs 3 \
     --output_dir /tmp/$TASK_NAME/ \
-    --overwrite_output_dir
 ```
 
 يمكنك أيضًا تحديد المعلمات من ملف `config_file.yaml` مباشرة في سطر الأوامر:
@@ -634,7 +633,6 @@ accelerate launch --num_processes=2 \
     --learning_rate 5e-5 \
     --num_train_epochs 3 \
     --output_dir /tmp/$TASK_NAME/ \
-    --overwrite_output_dir
 ```
 
 اطلع على برنامج تعليمي [Launching your Accelerate scripts](https://huggingface.co/docs/accelerate/basic_tutorials/launch) لمعرفة المزيد حول `accelerate_launch` والتكوينات المخصصة.
diff --git a/docs/source/de/run_scripts.md b/docs/source/de/run_scripts.md
@@ -98,7 +98,6 @@ python examples/pytorch/summarization/run_summarization.py \
     --output_dir /tmp/tst-summarization \
     --per_device_train_batch_size=4 \
     --per_device_eval_batch_size=4 \
-    --overwrite_output_dir \
     --predict_with_generate
 ```
 
@@ -122,7 +121,6 @@ torchrun \
     --output_dir /tmp/tst-summarization \
     --per_device_train_batch_size=4 \
     --per_device_eval_batch_size=4 \
-    --overwrite_output_dir \
     --predict_with_generate
 ```
 
@@ -144,7 +142,6 @@ python xla_spawn.py --num_cores 8 \
     --output_dir /tmp/tst-summarization \
     --per_device_train_batch_size=4 \
     --per_device_eval_batch_size=4 \
-    --overwrite_output_dir \
     --predict_with_generate
 ```
 
@@ -201,7 +198,6 @@ python examples/pytorch/summarization/run_summarization.py \
     --summary_column summary_column_name \
     --source_prefix "summarize: " \
     --output_dir /tmp/tst-summarization \
-    --overwrite_output_dir \
     --per_device_train_batch_size=4 \
     --per_device_eval_batch_size=4 \
     --predict_with_generate
@@ -229,7 +225,6 @@ python examples/pytorch/summarization/run_summarization.py \
     --output_dir /tmp/tst-summarization \
     --per_device_train_batch_size=4 \
     --per_device_eval_batch_size=4 \
-    --overwrite_output_dir \
     --predict_with_generate
 ```
 
@@ -243,25 +238,6 @@ examples/pytorch/summarization/run_summarization.py -h
 
 Eine weitere hilfreiche Option, die Sie aktivieren können, ist die Wiederaufnahme des Trainings von einem früheren Kontrollpunkt aus. Auf diese Weise können Sie im Falle einer Unterbrechung Ihres Trainings dort weitermachen, wo Sie aufgehört haben, ohne von vorne beginnen zu müssen. Es gibt zwei Methoden, um das Training von einem Kontrollpunkt aus wieder aufzunehmen.
 
-Die erste Methode verwendet das Argument `output_dir previous_output_dir`, um das Training ab dem letzten in `output_dir` gespeicherten Kontrollpunkt wieder aufzunehmen. In diesem Fall sollten Sie `overwrite_output_dir` entfernen:
-
-```bash
-python examples/pytorch/summarization/run_summarization.py
-    --model_name_or_path google-t5/t5-small \
-    --do_train \
-    --do_eval \
-    --dataset_name cnn_dailymail \
-    --dataset_config "3.0.0" \
-    --source_prefix "summarize: " \
-    --output_dir /tmp/tst-summarization \
-    --per_device_train_batch_size=4 \
-    --per_device_eval_batch_size=4 \
-    --output_dir previous_output_dir \
-    --predict_with_generate
-```
-
-Die zweite Methode verwendet das Argument `Resume_from_checkpoint path_to_specific_checkpoint`, um das Training ab einem bestimmten Checkpoint-Ordner wieder aufzunehmen.
-
 ```bash
 python examples/pytorch/summarization/run_summarization.py
     --model_name_or_path google-t5/t5-small \
@@ -273,7 +249,6 @@ python examples/pytorch/summarization/run_summarization.py
     --output_dir /tmp/tst-summarization \
     --per_device_train_batch_size=4 \
     --per_device_eval_batch_size=4 \
-    --overwrite_output_dir \
     --resume_from_checkpoint path_to_specific_checkpoint \
     --predict_with_generate
 ```
@@ -305,6 +280,5 @@ python examples/pytorch/summarization/run_summarization.py
     --output_dir /tmp/tst-summarization \
     --per_device_train_batch_size=4 \
     --per_device_eval_batch_size=4 \
-    --overwrite_output_dir \
     --predict_with_generate
 ```
diff --git a/docs/source/en/deepspeed.md b/docs/source/en/deepspeed.md
@@ -593,7 +593,7 @@ To deploy DeepSpeed on multiple GPUs, add `--num_gpus`. You don't need to add `-
 deepspeed --num_gpus=2 examples/pytorch/translation/run_translation.py \
 --deepspeed tests/deepspeed/ds_config_zero3.json \
 --model_name_or_path google-t5/t5-small --per_device_train_batch_size 1 \
---output_dir output_dir --overwrite_output_dir --fp16 \
+--output_dir output_dir --fp16 \
 --do_train --max_train_samples 500 --num_train_epochs 1 \
 --dataset_name wmt16 --dataset_config "ro-en" \
 --source_lang en --target_lang ro
@@ -616,7 +616,7 @@ To deploy DeepSpeed on a single GPU, add `--num_gpus`. You don't need to add `--
 deepspeed --num_gpus=1 examples/pytorch/translation/run_translation.py \
 --deepspeed tests/deepspeed/ds_config_zero2.json \
 --model_name_or_path google-t5/t5-small --per_device_train_batch_size 1 \
---output_dir output_dir --overwrite_output_dir --fp16 \
+--output_dir output_dir --fp16 \
 --do_train --max_train_samples 500 --num_train_epochs 1 \
 --dataset_name wmt16 --dataset_config "ro-en" \
 --source_lang en --target_lang ro

diff --git a/docs/source/en/run_scripts.md b/docs/source/en/run_scripts.md
@@ -61,9 +61,8 @@ The example below fine-tunes [T5-small](https://huggingface.co/google-t5/t5-smal
 
 The example script downloads and preprocesses a dataset, and then fine-tunes it with [`Trainer`] with a supported model architecture.
 
-Resuming training from a checkpoint is very useful if training is interrupted because you don't have to start over again. There are two ways to resume training from a checkpoint.
+Resuming training from a checkpoint is very useful if training is interrupted because you don't have to start over again: 
 
-* `--output dir previous_output_dir` resumes training from the latest checkpoint stored in `output_dir`. Remove the `--overwrite_output_dir` parameter if you're using this method.
 * `--resume_from_checkpoint path_to_specific_checkpoint` resumes training from a specific checkpoint folder.
 
 Share your model on the [Hub](https://huggingface.co/) with the `--push_to_hub` parameter. It creates a repository and uploads the model to the folder name specified in `--output_dir`. You could also use the `--push_to_hub_model_id` parameter to specify the repository name.
@@ -85,9 +84,6 @@ python examples/pytorch/summarization/run_summarization.py \
     --per_device_eval_batch_size=4 \
     --push_to_hub \
     --push_to_hub_model_id finetuned-t5-cnn_dailymail \
-    # remove if using `output_dir previous_output_dir`
-    # --overwrite_output_dir \
-    --output_dir previous_output_dir \
     # --resume_from_checkpoint path_to_specific_checkpoint \
     --predict_with_generate \
 ```
@@ -168,7 +164,6 @@ python examples/pytorch/summarization/run_summarization.py \
     --summary_column summary_column_name \
     --source_prefix "summarize: " \
     --output_dir /tmp/tst-summarization \
-    --overwrite_output_dir \
     --per_device_train_batch_size=4 \
     --per_device_eval_batch_size=4 \
     --predict_with_generate \

diff --git a/docs/source/en/trainer.md b/docs/source/en/trainer.md
@@ -361,8 +361,7 @@ accelerate launch \
     --per_device_train_batch_size 16 \
     --learning_rate 5e-5 \
     --num_train_epochs 3 \
-    --output_dir /tmp/$TASK_NAME/ \
-    --overwrite_output_dir
+    --output_dir /tmp/$TASK_NAME/
 ```
 
 > [!TIP]

diff --git a/docs/source/es/run_scripts.md b/docs/source/es/run_scripts.md
@@ -98,7 +98,6 @@ python examples/pytorch/summarization/run_summarization.py \
     --output_dir /tmp/tst-summarization \
     --per_device_train_batch_size=4 \
     --per_device_eval_batch_size=4 \
-    --overwrite_output_dir \
     --predict_with_generate
 ```
 
@@ -122,7 +121,6 @@ torchrun \
     --output_dir /tmp/tst-summarization \
     --per_device_train_batch_size=4 \
     --per_device_eval_batch_size=4 \
-    --overwrite_output_dir \
     --predict_with_generate
 ```
 
@@ -144,7 +142,6 @@ python xla_spawn.py --num_cores 8 \
     --output_dir /tmp/tst-summarization \
     --per_device_train_batch_size=4 \
     --per_device_eval_batch_size=4 \
-    --overwrite_output_dir \
     --predict_with_generate
 ```
 
@@ -201,7 +198,6 @@ python examples/pytorch/summarization/run_summarization.py \
     --summary_column summary_column_name \
     --source_prefix "summarize: " \
     --output_dir /tmp/tst-summarization \
-    --overwrite_output_dir \
     --per_device_train_batch_size=4 \
     --per_device_eval_batch_size=4 \
     --predict_with_generate
@@ -229,7 +225,6 @@ python examples/pytorch/summarization/run_summarization.py \
     --output_dir /tmp/tst-summarization \
     --per_device_train_batch_size=4 \
     --per_device_eval_batch_size=4 \
-    --overwrite_output_dir \
     --predict_with_generate
 ```
 
@@ -243,25 +238,6 @@ examples/pytorch/summarization/run_summarization.py -h
 
 Otra opción útil para habilitar es reanudar el entrenamiento desde un punto de control anterior. Esto asegurará que puedas continuar donde lo dejaste sin comenzar de nuevo si tu entrenamiento se interrumpe. Hay dos métodos para reanudar el entrenamiento desde un punto de control.
 
-El primer método utiliza el argumento `output_dir previous_output_dir` para reanudar el entrenamiento desde el último punto de control almacenado en `output_dir`. En este caso, debes eliminar `overwrite_output_dir`:
-
-```bash
-python examples/pytorch/summarization/run_summarization.py
-    --model_name_or_path google-t5/t5-small \
-    --do_train \
-    --do_eval \
-    --dataset_name cnn_dailymail \
-    --dataset_config "3.0.0" \
-    --source_prefix "summarize: " \
-    --output_dir /tmp/tst-summarization \
-    --per_device_train_batch_size=4 \
-    --per_device_eval_batch_size=4 \
-    --output_dir previous_output_dir \
-    --predict_with_generate
-```
-
-El segundo método utiliza el argumento `resume_from_checkpoint path_to_specific_checkpoint` para reanudar el entrenamiento desde una carpeta de punto de control específica.
-
 ```bash
 python examples/pytorch/summarization/run_summarization.py
     --model_name_or_path google-t5/t5-small \
@@ -273,7 +249,6 @@ python examples/pytorch/summarization/run_summarization.py
     --output_dir /tmp/tst-summarization \
     --per_device_train_batch_size=4 \
     --per_device_eval_batch_size=4 \
-    --overwrite_output_dir \
     --resume_from_checkpoint path_to_specific_checkpoint \
     --predict_with_generate
 ```
@@ -305,6 +280,5 @@ python examples/pytorch/summarization/run_summarization.py
     --output_dir /tmp/tst-summarization \
     --per_device_train_batch_size=4 \
     --per_device_eval_batch_size=4 \
-    --overwrite_output_dir \
     --predict_with_generate
 ```
diff --git a/docs/source/es/trainer.md b/docs/source/es/trainer.md
@@ -381,7 +381,6 @@ accelerate launch \
     --learning_rate 5e-5 \
     --num_train_epochs 3 \
     --output_dir /tmp/$TASK_NAME/ \
-    --overwrite_output_dir
 ```
 
 También puedes especificar los parámetros del archivo config_file.yaml directamente en la línea de comandos:
@@ -404,7 +403,6 @@ accelerate launch --num_processes=2 \
     --learning_rate 5e-5 \
     --num_train_epochs 3 \
     --output_dir /tmp/$TASK_NAME/ \
-    --overwrite_output_dir
 ```
 
 Consulta el tutorial [Lanzamiento de tus scripts con Accelerate](https://huggingface.co/docs/accelerate/basic_tutorials/launch) para obtener más información sobre `accelerate_launch` y las configuraciones personalizadas.