diff --git a/examples/alora_finetuning/README.md b/examples/alora_finetuning/README.md
index e6b8da0bcd..da0f1133a7 100644
--- a/examples/alora_finetuning/README.md
+++ b/examples/alora_finetuning/README.md
@@ -32,7 +32,7 @@ trainer = Trainer(
     model=peft_model,
     train_dataset=dataset,
     dataset_text_field="text",
-    max_seq_length=2048,
+    max_length=2048,
     tokenizer=tokenizer,
     data_collator=data_collator,
 )
diff --git a/examples/bone_finetuning/README.md b/examples/bone_finetuning/README.md
index 42b604f92d..bfc25f780b 100644
--- a/examples/bone_finetuning/README.md
+++ b/examples/bone_finetuning/README.md
@@ -28,7 +28,7 @@ peft_model.print_trainable_parameters()
 
 dataset = load_dataset("imdb", split="train[:1%]")
 
-training_args = SFTConfig(dataset_text_field="text", max_seq_length=128)
+training_args = SFTConfig(dataset_text_field="text", max_length=128)
 trainer = SFTTrainer(
     model=peft_model,
     args=training_args,
diff --git a/examples/corda_finetuning/README.md b/examples/corda_finetuning/README.md
index 9d72fc2f11..d86bcdcd7c 100644
--- a/examples/corda_finetuning/README.md
+++ b/examples/corda_finetuning/README.md
@@ -109,7 +109,7 @@ preprocess_corda(model, lora_config, run_model=run_model)
 peft_model = get_peft_model(model, lora_config)
 peft_model.print_trainable_parameters()
 
-training_args = SFTConfig(dataset_text_field="text", max_seq_length=128)
+training_args = SFTConfig(dataset_text_field="text", max_length=128)
 trainer = SFTTrainer(
     model=peft_model,
     args=training_args,
diff --git a/examples/delora_finetuning/README.md b/examples/delora_finetuning/README.md
index 20fc858bec..20c18ae059 100644
--- a/examples/delora_finetuning/README.md
+++ b/examples/delora_finetuning/README.md
@@ -26,7 +26,7 @@ peft_model.print_trainable_parameters()
 
 dataset = load_dataset("imdb", split="train[:1%]")
 
-training_args = SFTConfig(dataset_text_field="text", max_seq_length=128)
+training_args = SFTConfig(dataset_text_field="text", max_length=128)
 trainer = SFTTrainer(
     model=peft_model,
     args=training_args,
@@ -52,7 +52,7 @@ peft_model = PeftModel.from_pretrained(model, "delora-llama-3-8b")
 ## Advanced Usage
 In this script the default DeLoRA layers are the query and value layers of the Llama model. Adding adapters on more layers will increase memory usage. If you wish to choose a different set of layers for DeLoRA to be applied on, you can simply define it using:
 ```bash
-python examples/delora_finetuning/delora_finetuning.py --base_model meta-llama/Meta-Llama-3-8B --delora_target_modules "q_proj,k_proj,v_proj,o_proj" 
+python examples/delora_finetuning/delora_finetuning.py --base_model meta-llama/Meta-Llama-3-8B --target_modules "q_proj,k_proj,v_proj,o_proj" 
 ```
 
 Using different lambdas for different layers is also possible by setting `lambda_pattern`.
@@ -74,7 +74,7 @@ python delora_finetuning.py \
     --rank 32 \
     --delora_lambda 15 \
     --module_dropout 0.1 \
-    --delora_target_modules "q_proj,v_proj" \
+    --target_modules "q_proj,v_proj" \
     --hub_model_id "YOUR_HF_REPO" \
     --push_to_hub
 ```
diff --git a/examples/dora_finetuning/README.md b/examples/dora_finetuning/README.md
index 91ffb2a529..20250476e4 100644
--- a/examples/dora_finetuning/README.md
+++ b/examples/dora_finetuning/README.md
@@ -24,7 +24,7 @@ trainer = transformers.Trainer(
     model=peft_model,
     train_dataset=dataset,
     dataset_text_field="text",
-    max_seq_length=2048,
+    max_length=2048,
     tokenizer=tokenizer,
 )
 trainer.train()
diff --git a/examples/lorafa_finetune/README.md b/examples/lorafa_finetune/README.md
index 432c93ad83..1c1314dbd6 100644
--- a/examples/lorafa_finetune/README.md
+++ b/examples/lorafa_finetune/README.md
@@ -40,7 +40,7 @@ trainer = transformers.Trainer(
     model=peft_model,
     train_dataset=dataset,
     dataset_text_field="text",
-    max_seq_length=2048,
+    max_length=2048,
     processing_class=tokenizer,
     optimizers=(optimizer, None),
 )
diff --git a/examples/miss_finetuning/README.md b/examples/miss_finetuning/README.md
index bcc1bb33d8..1e11064d8d 100644
--- a/examples/miss_finetuning/README.md
+++ b/examples/miss_finetuning/README.md
@@ -36,7 +36,7 @@ peft_model.print_trainable_parameters()
 
 dataset = load_dataset("imdb", split="train[:1%]")
 
-training_args = SFTConfig(dataset_text_field="text", max_seq_length=128)
+training_args = SFTConfig(dataset_text_field="text", max_length=128)
 trainer = SFTTrainer(
     model=peft_model,
     args=training_args,
diff --git a/examples/olora_finetuning/README.md b/examples/olora_finetuning/README.md
index bb548b77b2..3b9bbf68ee 100644
--- a/examples/olora_finetuning/README.md
+++ b/examples/olora_finetuning/README.md
@@ -18,7 +18,7 @@ lora_config = LoraConfig(
     init_lora_weights="olora"
 )
 peft_model = get_peft_model(model, lora_config)
-training_args = SFTConfig(dataset_text_field="text", max_seq_length=128)
+training_args = SFTConfig(dataset_text_field="text", max_length=128)
 trainer = SFTTrainer(
     model=peft_model,
     train_dataset=dataset,
diff --git a/examples/pissa_finetuning/README.md b/examples/pissa_finetuning/README.md
index 4c0734bdcf..6ab1515679 100644
--- a/examples/pissa_finetuning/README.md
+++ b/examples/pissa_finetuning/README.md
@@ -23,7 +23,7 @@ peft_model.print_trainable_parameters()
 
 dataset = load_dataset("imdb", split="train[:1%]")
 
-training_args = SFTConfig(dataset_text_field="text", max_seq_length=128)
+training_args = SFTConfig(dataset_text_field="text", max_length=128)
 trainer = SFTTrainer(
     model=peft_model,
     args=training_args,
diff --git a/examples/randlora_finetuning/README.md b/examples/randlora_finetuning/README.md
index fa9d2d61de..f8eb7d77d0 100644
--- a/examples/randlora_finetuning/README.md
+++ b/examples/randlora_finetuning/README.md
@@ -20,7 +20,7 @@ trainer = transformers.Trainer(
     model=peft_model,
     train_dataset=dataset,
     dataset_text_field="text",
-    max_seq_length=2048,
+    max_length=2048,
     processing_class=tokenizer,
 )
 trainer.train()
diff --git a/examples/road_finetuning/README.md b/examples/road_finetuning/README.md
index b9ce14017c..00a043936f 100644
--- a/examples/road_finetuning/README.md
+++ b/examples/road_finetuning/README.md
@@ -26,7 +26,7 @@ trainer = transformers.Trainer(
     model=peft_model,
     train_dataset=dataset,
     dataset_text_field="text",
-    max_seq_length=2048,
+    max_length=2048,
     tokenizer=tokenizer,
 )
 trainer.train()
diff --git a/examples/shira_finetuning/README.md b/examples/shira_finetuning/README.md
index 45b1e99be6..76d062e2c7 100644
--- a/examples/shira_finetuning/README.md
+++ b/examples/shira_finetuning/README.md
@@ -18,7 +18,7 @@ shira_config = ShiraConfig(
     r=32,
 )
 peft_model = get_peft_model(model, shira_config)
-training_args = SFTConfig(dataset_text_field="text", max_seq_length=128)
+training_args = SFTConfig(dataset_text_field="text", max_length=128)
 trainer = SFTTrainer(
     model=peft_model,
     train_dataset=dataset,
diff --git a/examples/waveft_finetuning/README.md b/examples/waveft_finetuning/README.md
index ad2d231698..820ead27aa 100644
--- a/examples/waveft_finetuning/README.md
+++ b/examples/waveft_finetuning/README.md
@@ -20,7 +20,7 @@ waveft_config = WaveFTConfig(
     n_frequency=2592,
 )
 peft_model = get_peft_model(model, waveft_config)
-training_args = SFTConfig(dataset_text_field="text", max_seq_length=128)
+training_args = SFTConfig(dataset_text_field="text", max_length=128)
 trainer = SFTTrainer(
     model=peft_model,
     train_dataset=dataset,