diff --git a/docs/docs/tutorials/distillation_demo.md b/docs/docs/tutorials/distillation_demo.md
new file mode 100644
index 0000000000000000000000000000000000000000..c565eee72fea52c07e30bab47707eec759257eb7
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@@ -0,0 +1,110 @@
+本示例将介绍如何使用PaddleSlim蒸馏接口来对模型进行蒸馏训练。
+
+## 接口介绍
+
+请参考[蒸馏API文档](https://paddlepaddle.github.io/PaddleSlim/api/single_distiller_api/)。
+
+## PaddleSlim蒸馏训练流程
+
+一般情况下，模型参数量越多，结构越复杂，其性能越好，但运算量和资源消耗也越大。**知识蒸馏** 就是一种将大模型学习到的有用信息（Dark Knowledge）压缩进更小更快的模型，而获得可以匹敌大模型结果的方法。
+
+在本示例中精度较高的大模型被称为teacher，精度稍逊但速度更快的小模型被称为student。
+
+### 1. 定义student_program
+
+```python
+student_program = fluid.Program()
+student_startup = fluid.Program()
+with fluid.program_guard(student_program, student_startup):
+    image = fluid.data(
+        name='image', shape=[None] + [3, 224, 224], dtype='float32')
+    label = fluid.data(name='label', shape=[None, 1], dtype='int64')
+    # student model definition
+    model = MobileNet()
+    out = model.net(input=image, class_dim=1000)
+    cost = fluid.layers.cross_entropy(input=out, label=label)
+    avg_cost = fluid.layers.mean(x=cost)
+```
+
+### 2. 定义teacher_program
+
+在定义好`teacher_program`后，可以一并加载训练好的pretrained_model。
+
+在`teacher_program`内需要加上`with fluid.unique_name.guard():`，保证teacher的变量命名不被`student_program`影响，从而能够正确地加载预训练参数。
+
+```python
+teacher_program = fluid.Program()
+teacher_startup = fluid.Program()
+with fluid.program_guard(teacher_program, teacher_startup):
+    with fluid.unique_name.guard():
+        image = fluid.data(
+            name='data', shape=[None] + [3, 224, 224], dtype='float32')
+        # teacher model definition
+        teacher_model = ResNet()
+        predict = teacher_model.net(image, class_dim=1000)
+exe.run(teacher_startup)
+def if_exist(var):
+    return os.path.exists(
+        os.path.join("./pretrained", var.name)
+fluid.io.load_vars(
+    exe,
+    "./pretrained",
+    main_program=teacher_program,
+    predicate=if_exist)
+```
+
+### 3.选择特征图
+
+定义好`student_program`和`teacher_program`后，我们需要从中两两对应地挑选出若干个特征图，留待后续为其添加知识蒸馏损失函数。
+
+```python
+# get all student variables
+student_vars = []
+for v in student_program.list_vars():
+    try:
+        student_vars.append((v.name, v.shape))
+    except:
+        pass
+print("="*50+"student_model_vars"+"="*50)
+print(student_vars)
+# get all teacher variables
+teacher_vars = []
+for v in teacher_program.list_vars():
+    try:
+        teacher_vars.append((v.name, v.shape))
+    except:
+        pass
+print("="*50+"teacher_model_vars"+"="*50)
+print(teacher_vars)
+```
+
+### 4. 合并Program（merge）
+
+PaddlePaddle使用Program来描述计算图，为了同时计算student和teacher两个Program，这里需要将其两者合并（merge）为一个Program。
+
+merge过程操作较多，具体细节请参考[merge API文档](https://paddlepaddle.github.io/PaddleSlim/api/single_distiller_api/#merge)。
+
+```python
+data_name_map = {'data': 'image'}
+student_program = merge(teacher_program, student_program, data_name_map, place)
+```
+
+### 5.添加蒸馏loss
+
+在添加蒸馏loss的过程中，可能还会引入部分变量（Variable），为了避免命名重复这里可以使用`with fluid.name_scope("distill"):`为新引入的变量加一个命名作用域。
+
+另外需要注意的是，merge过程为`teacher_program`的变量统一加了名称前缀，默认是`"teacher_"`, 这里在添加`l2_loss`时也要为teacher的变量加上这个前缀。
+
+```python
+with fluid.program_guard(student_program, student_startup):
+    with fluid.name_scope("distill"):
+        distill_loss = l2_loss('teacher_bn5c_branch2b.output.1.tmp_3',
+		    'depthwise_conv2d_11.tmp_0', student_program)
+        distill_weight = 1
+        loss = avg_cost + distill_loss * distill_weight
+        opt = create_optimizer()
+        opt.minimize(loss)
+exe.run(student_startup)
+```
+
+至此，我们就得到了用于蒸馏训练的`student_program`，后面就可以使用一个普通program一样对其开始训练和评估。
diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml
index 40e940c29d1d444383e45566266b5bb6c9bd244c..970e4b8f7e0ee7940098ea77929c7214c49d9ba9 100644
--- a/docs/mkdocs.yml
+++ b/docs/mkdocs.yml
@@ -7,11 +7,12 @@ nav:
   - 量化训练: tutorials/quant_aware_demo.md
   - Embedding量化: tutorials/quant_embedding_demo.md
   - SA搜索: tutorials/nas_demo.md
+  - 知识蒸馏: tutorials/distillation_demo.md
 - API:
   - 量化: api/quantization_api.md
   - 剪枝与敏感度:  api/prune_api.md
   - 模型分析: api/analysis_api.md
-  - 蒸馏: api/single_distiller_api.md
+  - 知识蒸馏: api/single_distiller_api.md
   - SA搜索: api/nas_api.md
   - 搜索空间: api/search_space.md
   - 硬件延时评估表: table_latency.md