diff --git a/deploy/slim/README.md b/deploy/slim/README.md
index f5b0bf0edaec5dd72f5e342f4e2d7ebded484990..335b915b4d0cdf09505a3f56459cc853e41b9661 100644
--- a/deploy/slim/README.md
+++ b/deploy/slim/README.md
@@ -66,7 +66,7 @@ cd PaddleClas
 以CPU为例，若使用GPU，则将命令中改成`cpu`改成`gpu`
 
 ```bash
-python3.7 deploy/slim/slim.py -m train -c ppcls/configs/slim/ResNet50_vd_quantalization.yaml -o Global.device=cpu
+python3.7 deploy/slim/slim.py -m train -c ppcls/configs/slim/ResNet50_vd_quantization.yaml -o Global.device=cpu
 ```
 
 其中`yaml`文件解析详见[参考文档](../../docs/zh_CN/tutorials/config_description.md)。为了保证精度，`yaml`文件中已经使用`pretrained model`.
@@ -81,7 +81,7 @@ python3.7 -m paddle.distributed.launch \
     --gpus="0,1,2,3" \
       deploy/slim/slim.py \
       -m train \
-      -c ppcls/configs/slim/ResNet50_vd_quantalization.yaml
+      -c ppcls/configs/slim/ResNet50_vd_quantization.yaml
 ```
 
 ##### 3.1.2 离线量化
@@ -131,7 +131,7 @@ python3.7 -m paddle.distributed.launch \
 python3.7 deploy/slim/slim.py \
     -m export \
     -c ppcls/configs/slim/ResNet50_vd_prune.yaml \
-        -o Global.save_inference_dir=./inference
+    -o Global.save_inference_dir=./inference
 ```
 
 
diff --git a/deploy/slim/README_en.md b/deploy/slim/README_en.md
index dce481922a3d149a6283df855e672796e09e0cdc..815dbc10f9606deb26a770743d1c8edd92e52b20 100644
--- a/deploy/slim/README_en.md
+++ b/deploy/slim/README_en.md
@@ -67,7 +67,7 @@ The training command is as follow:
 If using GPU, change the `cpu` to `gpu` in the following command.
 
 ```bash
-python3.7 deploy/slim/slim.py -m train -c ppcls/configs/slim/ResNet50_vd_quantalization.yaml -o Global.device=cpu
+python3.7 deploy/slim/slim.py -m train -c ppcls/configs/slim/ResNet50_vd_quantization.yaml -o Global.device=cpu
 ```
 
 The description of `yaml` file can be found  in this [doc](../../docs/en/tutorials/config_en.md). To get better accuracy, the `pretrained model`is used in `yaml`.
@@ -82,7 +82,7 @@ python3.7 -m paddle.distributed.launch \
     --gpus="0,1,2,3" \
       deploy/slim/slim.py \
       -m train \
-      -c ppcls/configs/slim/ResNet50_vd_quantalization.yaml
+      -c ppcls/configs/slim/ResNet50_vd_quantization.yaml
 ```
 
 ##### 3.1.2 Offline quantization
@@ -132,7 +132,7 @@ After getting the compressed model, we can export it as inference model for pred
 python3.7 deploy/slim/slim.py \
     -m export \
     -c ppcls/configs/slim/ResNet50_vd_prune.yaml \
-        -o Global.save_inference_dir=./inference
+    -o Global.save_inference_dir=./inference
 ```
 
 ### 5. Deploy
diff --git a/log/endpoints.log b/log/endpoints.log
new file mode 100644
index 0000000000000000000000000000000000000000..438da7f1e2835134fcc6eb9580cb70c419bd8ce7
--- /dev/null
+++ b/log/endpoints.log
@@ -0,0 +1,5 @@
+PADDLE_TRAINER_ENDPOINTS: 
+127.0.0.1:51466
+127.0.0.1:58283
+127.0.0.1:34005
+127.0.0.1:58331
\ No newline at end of file
diff --git a/ppcls/configs/slim/ResNet50_vd_quantization.yaml b/ppcls/configs/slim/ResNet50_vd_quantization.yaml
index 41fcd0c868e7c55695a532eb9ed9641fa089d32d..aeccaeaae497e7f538427aad671cb54af7f64847 100644
--- a/ppcls/configs/slim/ResNet50_vd_quantization.yaml
+++ b/ppcls/configs/slim/ResNet50_vd_quantization.yaml
@@ -42,7 +42,7 @@ Optimizer:
   momentum: 0.9
   lr:
     name: Cosine
-    learning_rate: 0.1
+    learning_rate: 0.01
   regularizer:
     name: 'L2'
     coeff: 0.00007