diff --git a/train_sft_ds.py b/train_sft_ds.py
index 5d1e140..13cb148 100644
--- a/train_sft_ds.py
+++ b/train_sft_ds.py
@@ -63,11 +63,62 @@ class CsvLossLogger(TrainerCallback):
             with open(self.csv_path, "w", encoding="utf-8") as f:
                 f.write("step,loss,lr,total_flos\n")
 
+    # def on_log(self, args, state, control, logs=None, **kwargs):
+    #     if not is_main_process() or logs is None:
+    #         return
+    #     with open(self.csv_path, "a", encoding="utf-8") as f:
+    #         f.write(f"{state.global_step},{logs.get('loss','')},{logs.get('learning_rate','')},{logs.get('total_flos','')}\n")
+
+
+    def on_train_begin(self, args, state, control, **kwargs):
+        tmp = (getattr(state, "max_steps", 0) or getattr(args, "max_steps", 0) or 0)
+        tot = tmp if isinstance(tmp, int) and tmp > 0 else 0
+
+        rank = os.environ.get("RANK", "?")
+        host = socket.gethostname()
+        print(f"[{host} rank={rank}] total_steps={tot}", flush=True)
+
+
     def on_log(self, args, state, control, logs=None, **kwargs):
-        if not is_main_process() or logs is None:
+        if logs is None:
+            return
+
+        # ---- 控制台打印：所有 rank 都打当前步/总步 ----
+        cur = int(getattr(state, "global_step", 0) or 0)
+
+
+        # if getattr(args, "logging_steps", None) and cur % args.logging_steps != 0:
+        #     return
+
+        tmp = (getattr(state, "max_steps", 0) or getattr(args, "max_steps", 0) or 0)
+        tot = tmp if isinstance(tmp, int) and tmp > 0 else 0
+        pct = (f"{(cur / tot * 100):.1f}%" if tot else "n/a")
+
+        # —— tot 一旦可用，就再宣布一次总步数（只打印一次）
+        if tot and not hasattr(self, "_tot_announced"):
+            print(f"[{socket.gethostname()} rank={os.environ.get('RANK','?')}] total_steps={tot}", flush=True)
+            self._tot_announced = True
+
+        # if not is_main_process():
+        #     return
+
+        rank = os.environ.get("RANK", "?")
+        host = socket.gethostname()
+        print(
+            f"[{host} rank={rank}] step {cur}/{tot} ({pct})  "
+            f"loss={logs.get('loss')}  lr={logs.get('learning_rate')}",
+            flush=True
+        )
+
+        # ---- 只在主进程写 CSV，避免并发写 ----
+        if not is_main_process():
             return
         with open(self.csv_path, "a", encoding="utf-8") as f:
-            f.write(f"{state.global_step},{logs.get('loss','')},{logs.get('learning_rate','')},{logs.get('total_flos','')}\n")
+            f.write(
+                f"{cur},{logs.get('loss','')},{logs.get('learning_rate','')},{logs.get('total_flos','')}\n"
+        )
+
+
 
 
 # ----------------- 仅监督 assistant 的数据集 -----------------