diff --git a/examples/research_projects/jax-projects/wav2vec2/run_wav2vec2_pretrain_flax.py b/examples/research_projects/jax-projects/wav2vec2/run_wav2vec2_pretrain_flax.py
index 7eb286b496fc..4911ecb571af 100755
--- a/examples/research_projects/jax-projects/wav2vec2/run_wav2vec2_pretrain_flax.py
+++ b/examples/research_projects/jax-projects/wav2vec2/run_wav2vec2_pretrain_flax.py
@@ -48,9 +48,6 @@ class ModelArguments:
     freeze_feature_extractor: Optional[bool] = field(
         default=True, metadata={"help": "Whether to freeze the feature extractor layers of the model."}
     )
-    gradient_checkpointing: Optional[bool] = field(
-        default=False, metadata={"help": "Whether to freeze the feature extractor layers of the model."}
-    )
     verbose_logging: Optional[bool] = field(
         default=False,
         metadata={"help": "Whether to log verbose messages or not."},
@@ -356,7 +353,6 @@ def normalize(batch):
     config = Wav2Vec2Config.from_pretrained(
         model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
-        gradient_checkpointing=model_args.gradient_checkpointing,
     )
 
     if not config.do_stable_layer_norm or config.feat_extract_norm != "layer":
@@ -366,6 +362,10 @@ def normalize(batch):
 
     model = FlaxWav2Vec2ForPreTraining(config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype))
 
+    # Activate gradient checkpointing if needed
+    if training_args.gradient_checkpointing:
+        model.gradient_checkpointing_enable()
+
     data_collator = FlaxDataCollatorForWav2Vec2Pretraining(
         model=model, feature_extractor=feature_extractor, pad_to_multiple_of=data_args.pad_to_multiple_of
     )