diff --git a/pyproject.toml b/pyproject.toml
index 9a28047..15389ac 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -15,6 +15,7 @@ dependencies = [
     "numpy>=2.4.2",
     "pandas>=3.0.0",
     "pypinyin>=0.55.0",
+    "requests>=2.32.5",
     "rich>=14.3.1",
     "transformers>=5.1.0",
     "typer>=0.21.1",
diff --git a/src/trainer/model.py b/src/trainer/model.py
index 98633b9..cabfb6a 100644
--- a/src/trainer/model.py
+++ b/src/trainer/model.py
@@ -12,7 +12,7 @@ from loguru import logger
 from modelscope import AutoModel, AutoTokenizer
 from tqdm.autonotebook import tqdm
 
-from .monitor import TrainingMonitor
+from .monitor import TrainingMonitor, send_serverchan_message
 from suinput.dataset import PG
 
 
@@ -282,7 +282,7 @@ class MoEModel(nn.Module):
             avg_loss: float, 平均损失
         """
         if criterion is None:
-            criterion = nn.CrossEntropyLoss()
+            criterion = nn.NLLLoss()
 
         self.eval()
         total_loss = 0.0
@@ -300,7 +300,7 @@ class MoEModel(nn.Module):
                 # 前向传播
                 probs = self(input_ids, attention_mask, pg)
                 log_probs = torch.log(probs + 1e-12)
-                loss = nn.NLLLoss()(log_probs, labels)
+                loss = criterion(log_probs, labels)
                 total_loss += loss.item() * labels.size(0)
 
                 # 计算准确率
@@ -541,6 +541,16 @@ class MoEModel(nn.Module):
                     if processed_batches - 1 >= stop_batch:
                         break
                     global_step += 1
+        res_acc, res_loss = self.model_eval(eval_dataloader)
+        try:
+            to_wechat_response = send_serverchan_message(
+                title="训练完成",
+                content=f"训练完成，acc: {res_acc:.4f}, loss: {res_loss:.4f}",
+            )
+            logger.info(f"训练完成，acc: {res_acc:.4f}, loss: {res_loss:.4f}")
+            logger.info(f"发送消息: {to_wechat_response}")
+        except Exception as e:
+            logger.error(f"发送消息失败: {e}")
 
     def load_from_state_dict(self, state_dict_path: Union[str, Path]):
         state_dict = torch.load(
diff --git a/src/trainer/monitor.py b/src/trainer/monitor.py
index d2f4de0..00859d2 100644
--- a/src/trainer/monitor.py
+++ b/src/trainer/monitor.py
@@ -1,11 +1,40 @@
+import os
+
 from bokeh.io import push_notebook, show, output_notebook
 from bokeh.plotting import figure
 from bokeh.models import ColumnDataSource, LinearAxis, Range1d
 import pandas as pd
 import numpy as np
+import requests
 
 output_notebook()  # 在 Jupyter 中必须调用一次
 
+
+def send_serverchan_message(title, content=""):
+    """
+    向ServerChan发送消息（SendKey从环境变量读取）
+    
+    :param title: 消息标题
+    :param content: 消息内容（支持Markdown）
+    :return: 响应JSON
+    """
+    # 从环境变量获取SendKey
+    send_key = os.environ.get("SERVERCHAN_SEND_KEY")
+    
+    if not send_key:
+        raise ValueError("❌ 未找到环境变量 SERVERCHAN_SEND_KEY，请检查配置")
+    
+    url = f"https://sctapi.ftqq.com/{send_key}.send"
+    
+    data = {
+        "text": title,
+        "desp": content
+    }
+    
+    response = requests.post(url, data=data)
+    return response.json()
+
+
 class TrainingMonitor:
     """
     实时训练监控图，支持任意多个指标，自动管理左右 Y 轴。