Adopting SFTTrainer arguments based on new updates

sample_finetune.py

@@ -6,8 +6,8 @@ from datasets import load_dataset
 from peft import LoraConfig
 import torch
 import transformers
-from trl import SFTTrainer
-from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, BitsAndBytesConfig
+from trl import SFTTrainer, SFTConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
 
 """
 A simple example on using SFTTrainer and Accelerate to finetune Phi-4-Mini-Instruct model. For
@@ -86,6 +86,9 @@ training_config = {
     "gradient_checkpointing_kwargs":{"use_reentrant": False},
     "gradient_accumulation_steps": 1,
     "warmup_ratio": 0.2,
+    "max_seq_length": 2048,
+    "dataset_text_field": "text",
+    "packing": True,
     }
 
 peft_config = {
@@ -97,7 +100,7 @@ peft_config = {
     "target_modules": "all-linear",
     "modules_to_save": None,
 }
-train_conf = TrainingArguments(**training_config)
+train_conf = SFTConfig(**training_config)
 peft_conf = LoraConfig(**peft_config)
 
 
@@ -186,10 +189,7 @@ trainer = SFTTrainer(
     peft_config=peft_conf,
     train_dataset=processed_train_dataset,
     eval_dataset=processed_test_dataset,
-    max_seq_length=2048,
-    dataset_text_field="text",
-    tokenizer=tokenizer,
-    packing=True
+    processing_class=tokenizer,
 )
 train_result = trainer.train()
 metrics = train_result.metrics