diff --git a/eval_llm.py b/eval_llm.py
index 8af17d4..86709ae 100755
--- a/eval_llm.py
+++ b/eval_llm.py
@@ -6,7 +6,7 @@ import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer
 from model.model_minimind import MiniMindConfig, MiniMindForCausalLM
 from model.model_lora import *
-from trainer.trainer_utils import setup_seed
+from trainer.trainer_utils import setup_seed, get_model_params
 warnings.filterwarnings('ignore')
 
 def init_model(args):
@@ -26,7 +26,7 @@ def init_model(args):
             load_lora(model, f'./{args.save_dir}/lora/{args.lora_weight}_{args.hidden_size}.pth')
     else:
         model = AutoModelForCausalLM.from_pretrained(args.load_from, trust_remote_code=True)
-    print(f'MiniMind模型参数: {sum(p.numel() for p in model.parameters()) / 1e6:.2f} M(illion)')
+    get_model_params(model, model.config)
     return model.eval().to(args.device), tokenizer
 
 def main():
diff --git a/trainer/trainer_utils.py b/trainer/trainer_utils.py
index e2dd433..49f263f 100644
--- a/trainer/trainer_utils.py
+++ b/trainer/trainer_utils.py
@@ -1,8 +1,10 @@
 """
 训练工具函数集合
 """
-import gc
 import os
+import sys
+__package__ = "trainer"
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
 import random
 import math
 import numpy as np
@@ -10,7 +12,21 @@ import torch
 import torch.distributed as dist
 from torch.utils.data import Sampler
 from transformers import AutoTokenizer
-from model.model_minimind import MiniMindForCausalLM
+from model.model_minimind_qwen3 import MiniMindForCausalLM
+
+def get_model_params(model, config):
+    total = sum(p.numel() for p in model.parameters()) / 1e6
+    if getattr(config, 'use_moe', False):
+        n_routed = getattr(config, 'n_routed_experts', getattr(config, 'num_experts', 0))
+        n_active = getattr(config, 'num_experts_per_tok', 0)
+        n_shared = getattr(config, 'n_shared_experts', 0)
+        expert = sum(p.numel() for n, p in model.named_parameters() if 'mlp.experts.0.' in n) / 1e6
+        shared_expert = sum(p.numel() for n, p in model.named_parameters() if 'mlp.shared_experts.0.' in n) / 1e6
+        base = total - (expert * n_routed) - (shared_expert * n_shared)
+        active = base + (expert * n_active) + (shared_expert * n_shared)
+        Logger(f'Model Params: {total:.2f}M-A{active:.2f}M')
+    else:
+        Logger(f'Model Params: {total:.2f}M')
 
 
 def is_main_process():
@@ -111,7 +127,8 @@ def init_model(lm_config, from_weight='pretrain', tokenizer_path='../model', sav
         weights = torch.load(weight_path, map_location=device)
         model.load_state_dict(weights, strict=False)
 
-    Logger(f'所加载Model可训练参数：{sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6:.3f} 百万')
+    get_model_params(model, lm_config)
+    Logger(f'Trainable Params: {sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6:.3f}M')
     return model.to(device), tokenizer
 
 
@@ -138,5 +155,4 @@ class SkipBatchSampler(Sampler):
 
     def __len__(self):
         total_batches = (len(self.sampler) + self.batch_size - 1) // self.batch_size
-        return max(0, total_batches - self.skip_batches)
-
+        return max(0, total_batches - self.skip_batches)
\ No newline at end of file