use max_length to replace max_seq_length; correct README for

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,
 )

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,

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,

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
 ```

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()

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),
 )

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,

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,

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,

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()

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()

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,

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,