diff --git a/deploy/slim/prune/README.md b/deploy/slim/prune/README.md
index 98e43332..7b8dd169 100644
--- a/deploy/slim/prune/README.md
+++ b/deploy/slim/prune/README.md
@@ -24,6 +24,7 @@
 ```bash
 git clone https://github.com/PaddlePaddle/PaddleSlim.git
 cd PaddleSlim
+git checkout develop
 python3 setup.py install
 ```
 
@@ -48,14 +49,14 @@ python3 setup.py install
 
 进入PaddleOCR根目录，通过以下命令对模型进行敏感度分析训练：
 ```bash
-python3.7 deploy/slim/prune/sensitivity_anal.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model="your trained model"
+python3.7 deploy/slim/prune/sensitivity_anal.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model="your trained model" Global.save_model_dir=./output/prune_model/
 ```
 
 ### 4. 导出模型、预测部署
 
 在得到裁剪训练保存的模型后，我们可以将其导出为inference_model：
 ```bash
-pytho3.7 deploy/slim/prune/export_prune_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model=./output/det_db/best_accuracy  Global.save_inference_dir=inference_model
+pytho3.7 deploy/slim/prune/export_prune_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model=./output/det_db/best_accuracy  Global.save_inference_dir=./prune/prune_inference_model
 ```
 
 inference model的预测和部署参考：
diff --git a/deploy/slim/prune/README_en.md b/deploy/slim/prune/README_en.md
index 5b39dc93..fe9c5dcd 100644
--- a/deploy/slim/prune/README_en.md
+++ b/deploy/slim/prune/README_en.md
@@ -54,7 +54,7 @@ Enter the PaddleOCR root directory，perform sensitivity analysis on the model w
 
 ```bash
 
-python3.7 deploy/slim/prune/sensitivity_anal.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model="your trained model"
+python3.7 deploy/slim/prune/sensitivity_anal.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model="your trained model"  Global.save_model_dir=./output/prune_model/
 
 ```
 
@@ -63,7 +63,7 @@ python3.7 deploy/slim/prune/sensitivity_anal.py -c configs/det/ch_ppocr_v2.0/ch_
 
 We can export the pruned model as inference_model for deployment:
 ```bash
-python deploy/slim/prune/export_prune_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml  -o Global.pretrained_model=./output/det_db/best_accuracy Global.save_inference_dir=inference_model
+python deploy/slim/prune/export_prune_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml  -o Global.pretrained_model=./output/det_db/best_accuracy  Global.save_inference_dir=./prune/prune_inference_model
 ```
 
 Reference for prediction and deployment of inference model:
diff --git a/deploy/slim/quantization/quant.py b/deploy/slim/quantization/quant.py
index 7671e5f8..315e3b43 100755
--- a/deploy/slim/quantization/quant.py
+++ b/deploy/slim/quantization/quant.py
@@ -112,10 +112,6 @@ def main(config, device, logger, vdl_writer):
         config['Architecture']["Head"]['out_channels'] = char_num
     model = build_model(config['Architecture'])
 
-    # prepare to quant
-    quanter = QAT(config=quant_config, act_preprocess=PACT)
-    quanter.quantize(model)
-
     if config['Global']['distributed']:
         model = paddle.DataParallel(model)
 
@@ -136,31 +132,15 @@ def main(config, device, logger, vdl_writer):
 
     logger.info('train dataloader has {} iters, valid dataloader has {} iters'.
                 format(len(train_dataloader), len(valid_dataloader)))
+    quanter = QAT(config=quant_config, act_preprocess=PACT)
+    quanter.quantize(model)
+
     # start train
     program.train(config, train_dataloader, valid_dataloader, device, model,
                   loss_class, optimizer, lr_scheduler, post_process_class,
                   eval_class, pre_best_model_dict, logger, vdl_writer)
 
 
-def test_reader(config, device, logger):
-    loader = build_dataloader(config, 'Train', device, logger)
-    import time
-    starttime = time.time()
-    count = 0
-    try:
-        for data in loader():
-            count += 1
-            if count % 1 == 0:
-                batch_time = time.time() - starttime
-                starttime = time.time()
-                logger.info("reader: {}, {}, {}".format(
-                    count, len(data[0]), batch_time))
-    except Exception as e:
-        logger.info(e)
-    logger.info("finish reader: {}, Success!".format(count))
-
-
 if __name__ == '__main__':
     config, device, logger, vdl_writer = program.preprocess(is_train=True)
     main(config, device, logger, vdl_writer)
-    # test_reader(config, device, logger)