Update trainer configuration in train.py to align evaluation strategy with save strategy. Set eval_steps to match save_steps for consistent evaluation frequency.

train.py

@@ -207,8 +207,10 @@ def create_trainer(
                 fp16=not is_bfloat16_supported(),
                 bf16=is_bfloat16_supported(),
                 logging_steps=1,
-                eval_steps=10,  # Evaluate every 10 steps
+                save_strategy="steps",
                 save_steps=30,
+                eval_strategy="steps",  # Match save_strategy
+                eval_steps=30,  # Match save_steps
                 save_total_limit=2,
                 optim="adamw_8bit",
                 weight_decay=0.01,