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train.py

@@ -19,10 +19,9 @@ from transformers import (
     AutoModelForCausalLM,
     AutoTokenizer,
     BitsAndBytesConfig,
-    TrainingArguments,
 )
 from peft import LoraConfig
-from trl import SFTTrainer
+from trl import SFTTrainer, SFTConfig
 
 load_dotenv()
 
@@ -102,8 +101,8 @@ def main():
             output_texts.append(text)
         return output_texts
     
-    # Training Arguments
-    training_args = TrainingArguments(
+    # Training Arguments (SFTConfig for TRL 0.27+)
+    training_args = SFTConfig(
         output_dir=OUTPUT_DIR,
         per_device_train_batch_size=1,
         gradient_accumulation_steps=8,
@@ -130,7 +129,7 @@ def main():
         train_dataset=dataset,
         peft_config=peft_config,
         formatting_func=formatting_prompts_func,
-        tokenizer=tokenizer,
+        processing_class=tokenizer,  # renamed from 'tokenizer' in TRL 0.27+
         args=training_args,
     )