diff --git a/docs/lite/docs/source_zh_cn/infer/quick_start_c.md b/docs/lite/docs/source_zh_cn/infer/quick_start_c.md
index c1d001fb64944f5f8394a9bad47db77eb51a9d39..15cdd9c3e58c3d8a7fbbf40be1e21c7e6b764694 100644
--- a/docs/lite/docs/source_zh_cn/infer/quick_start_c.md
+++ b/docs/lite/docs/source_zh_cn/infer/quick_start_c.md
@@ -54,7 +54,7 @@
   执行完成后将能得到如下结果，打印输出Tensor的名称、输出Tensor的大小，输出Tensor的数量以及前50个数据：
 
   ```text
-  Tensor name: Softmax-65, tensor size is 4004 ,elements num: 1001.
+  Tensor name: Softmax-65, tensor size is 4004, elements num: 1001.
   output data is:
   0.000011 0.000015 0.000015 0.000079 0.000070 0.000702 0.000120 0.000590 0.000009 0.000004 0.000004 0.000002 0.000002 0.000002 0.000010 0.000055 0.000006 0.000010 0.000003 0.000010 0.000002 0.000005 0.000001 0.000002 0.000004 0.000006 0.000008 0.000003 0.000015 0.000005 0.000011 0.000020 0.000006 0.000002 0.000011 0.000170 0.000005 0.000009 0.000006 0.000002 0.000003 0.000009 0.000005 0.000006 0.000003 0.000011 0.000005 0.000027 0.000003 0.000050 0.000016
   ```
diff --git a/docs/mindflow/docs/source_en/index.rst b/docs/mindflow/docs/source_en/index.rst
index a488ab19e5d23c2a02b046aebaea52024f900c10..5db0c553d4fdfca0c7a753aaec76029a136744eb 100644
--- a/docs/mindflow/docs/source_en/index.rst
+++ b/docs/mindflow/docs/source_en/index.rst
@@ -36,7 +36,7 @@ Code repository address: <https://gitee.com/mindspore/mindscience/tree/master/Mi
    physics_driven/poisson_geometry
    physics_driven/taylor_green2D
    physics_driven/navier_stokes_inverse
-   physics_driven/boltzmannD1V3
+   physics_driven/boltzmann
    physics_driven/kovasznay
    physics_driven/periodic_hill
    physics_driven/poisson_point_source
diff --git a/docs/mindflow/docs/source_en/physics_driven/boltzmannD1V3.ipynb b/docs/mindflow/docs/source_en/physics_driven/boltzmann.ipynb
similarity index 99%
rename from docs/mindflow/docs/source_en/physics_driven/boltzmannD1V3.ipynb
rename to docs/mindflow/docs/source_en/physics_driven/boltzmann.ipynb
index 23a6d226cc16e3f2faaef329d83190dfefbb27cf..b7f257a01bdbf4af6f420f12c282c0888488d28b 100644
--- a/docs/mindflow/docs/source_en/physics_driven/boltzmannD1V3.ipynb
+++ b/docs/mindflow/docs/source_en/physics_driven/boltzmann.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# Neural Representation Method for Boltzmann Equation\n",
     "\n",
-    "[![DownloadNotebook](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_notebook_en.svg)](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/master/mindflow/en/physics_driven/mindspore_boltzmannD1V3.ipynb)&emsp;[![DownloadCode](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_download_code_en.svg)](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/master/mindflow/en/physics_driven/mindspore_boltzmannD1V3.py)&emsp;[![View Source On Gitee](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source_en.svg)](https://gitee.com/mindspore/docs/blob/master/docs/mindflow/docs/source_en/physics_driven/boltzmannD1V3.ipynb)\n",
+    "[![DownloadNotebook](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_notebook_en.svg)](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/master/mindflow/en/physics_driven/mindspore_boltzmann.ipynb)&emsp;[![DownloadCode](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_download_code_en.svg)](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/master/mindflow/en/physics_driven/mindspore_boltzmann.py)&emsp;[![View Source On Gitee](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source_en.svg)](https://gitee.com/mindspore/docs/blob/master/docs/mindflow/docs/source_en/physics_driven/boltzmann.ipynb)\n",
     "\n",
     "## Problem Description\n",
     "\n",
diff --git a/docs/mindformers/docs/source_zh_cn/advanced_development/accuracy_comparison.md b/docs/mindformers/docs/source_zh_cn/advanced_development/accuracy_comparison.md
index b9c122ef44907f67f2dcc8db1754674cef3a099f..88a9a879ed462cd9060ce74136eed8d7aaa5703d 100644
--- a/docs/mindformers/docs/source_zh_cn/advanced_development/accuracy_comparison.md
+++ b/docs/mindformers/docs/source_zh_cn/advanced_development/accuracy_comparison.md
@@ -1,5 +1,7 @@
 # 与 Megatron-LM 比对训练精度
 
+[![查看源文件](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source.svg)](https://gitee.com/mindspore/docs/blob/master/docs/mindformers/docs/source_zh_cn/advanced_development/accuracy_comparison.md)
+
 ## 1. 概述
 
 在大模型训练系统中，模型层级的数值精度验证是保障训练稳定性和结果可信度的关键环节。随着训练任务日益复杂，模型结构日趋庞大，确保不同实现之间在模型整体行为上的对齐，显得尤为重要。
diff --git a/tutorials/source_en/dataset/sampler.md b/tutorials/source_en/dataset/sampler.md
index 8d3c2858a1a8763a7fb5d8eac86bc4c29afd1a76..9009bbb30232537175d169927b3c80bdeedeb735 100644
--- a/tutorials/source_en/dataset/sampler.md
+++ b/tutorials/source_en/dataset/sampler.md
@@ -82,6 +82,43 @@ for d in dataset:
 [Tensor(shape=[], dtype=Int32, value= 4)]
 ```
 
+#### Iterable Datasets
+
+An iterable dataset is a dataset that implements the `__iter__` and `__next__` methods, indicating that the data samples can be accessed step-by-step in an iterative manner. This type of dataset is particularly suitable for situations where random access is too costly or infeasible.
+
+For example, when accessing a dataset in the form of iter(dataset), you can read a data stream returned from a database or a remote server.
+
+The following constructs a simple iterator and loads it into `GeneratorDataset`.
+
+```python
+# Iterator as input source
+class IterableDataset():
+    def __init__(self, start, end):
+        '''init the class object to hold the data'''
+        self.start = start
+        self.end = end
+    def __next__(self):
+        '''iter one data and return'''
+        return next(self.data)
+    def __iter__(self):
+        '''reset the iter'''
+        self.data = iter(range(self.start, self.end))
+        return self
+
+loader = IterableDataset(1, 5)
+dataset = GeneratorDataset(source=loader, column_names=["data"])
+
+for d in dataset:
+    print(d)
+```
+
+```text
+[Tensor(shape=[], dtype=Int64, value= 1)]
+[Tensor(shape=[], dtype=Int64, value= 2)]
+[Tensor(shape=[], dtype=Int64, value= 3)]
+[Tensor(shape=[], dtype=Int64, value= 4)]
+```
+
 #### Generator
 
 Generator also belongs to iterable dataset types, and it can be a Python's generator to return data until the generator throws a `StopIteration` exception.
diff --git a/tutorials/source_en/orange_pi/environment_setup.md b/tutorials/source_en/orange_pi/environment_setup.md
index ba3fa9b1d86e2d923f897b9bc7b2da6a3c95e8a8..84d9ab0a0e3d5a9ead9106c3974dec1de82c792a 100644
--- a/tutorials/source_en/orange_pi/environment_setup.md
+++ b/tutorials/source_en/orange_pi/environment_setup.md
@@ -285,8 +285,7 @@ Step 5 Add execution permissions to the kernels package.
 Step 6 Execute the following command to upgrade the software.
 
 ```bash
-(base) root@orangepiaipro: /home/HwHiAiUser/Downloads#./
-Ascend-cann-kernels-310b_8.0.0_linux-aarch64.run --install
+(base) root@orangepiaipro: /home/HwHiAiUser/Downloads#./Ascend-cann-kernels-310b_8.0.0_linux-aarch64.run --install
 ```
 
 After the upgrade is completed, if the following message is displayed, the software upgrade is successful:
diff --git a/tutorials/source_zh_cn/beginner/autograd.ipynb b/tutorials/source_zh_cn/beginner/autograd.ipynb
index d84e8ebfe580d273a9ac575cf07d0b24eeaab29c..4728b1a89efa22de1f558ebfcde01216d3603120 100644
--- a/tutorials/source_zh_cn/beginner/autograd.ipynb
+++ b/tutorials/source_zh_cn/beginner/autograd.ipynb
@@ -156,11 +156,11 @@
      "output_type": "stream",
      "text": [
       "(Tensor(shape=[5, 3], dtype=Float32, value=\n",
-      "[[ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02],\n",
-      " [ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02],\n",
-      " [ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02],\n",
-      " [ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02],\n",
-      " [ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02]]), Tensor(shape=[3], dtype=Float32, value= [ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02]))\n"
+      " [[ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02],\n",
+      "  [ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02],\n",
+      "  [ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02],\n",
+      "  [ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02],\n",
+      "  [ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02]]), Tensor(shape=[3], dtype=Float32, value= [ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02]))\n"
      ]
     }
    ],
@@ -207,11 +207,11 @@
      "output_type": "stream",
      "text": [
       "(Tensor(shape=[5, 3], dtype=Float32, value=\n",
-      "[[ 1.32618928e+00,  1.01589143e+00,  1.04216456e+00],\n",
-      " [ 1.32618928e+00,  1.01589143e+00,  1.04216456e+00],\n",
-      " [ 1.32618928e+00,  1.01589143e+00,  1.04216456e+00],\n",
-      " [ 1.32618928e+00,  1.01589143e+00,  1.04216456e+00],\n",
-      " [ 1.32618928e+00,  1.01589143e+00,  1.04216456e+00]]), Tensor(shape=[3], dtype=Float32, value= [ 1.32618928e+00,  1.01589143e+00,  1.04216456e+00]))\n"
+      " [[ 1.32618928e+00,  1.01589143e+00,  1.04216456e+00],\n",
+      "  [ 1.32618928e+00,  1.01589143e+00,  1.04216456e+00],\n",
+      "  [ 1.32618928e+00,  1.01589143e+00,  1.04216456e+00],\n",
+      "  [ 1.32618928e+00,  1.01589143e+00,  1.04216456e+00],\n",
+      "  [ 1.32618928e+00,  1.01589143e+00,  1.04216456e+00]]), Tensor(shape=[3], dtype=Float32, value= [ 1.32618928e+00,  1.01589143e+00,  1.04216456e+00]))\n"
      ]
     }
    ],
@@ -250,11 +250,11 @@
      "output_type": "stream",
      "text": [
       "(Tensor(shape=[5, 3], dtype=Float32, value=\n",
-      "[[ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02],\n",
-      " [ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02],\n",
-      " [ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02],\n",
-      " [ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02],\n",
-      " [ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02]]), Tensor(shape=[3], dtype=Float32, value= [ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02]))\n"
+      " [[ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02],\n",
+      "  [ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02],\n",
+      "  [ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02],\n",
+      "  [ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02],\n",
+      "  [ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02]]), Tensor(shape=[3], dtype=Float32, value= [ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02]))\n"
      ]
     }
    ],
@@ -303,11 +303,11 @@
      "output_type": "stream",
      "text": [
       "(Tensor(shape=[5, 3], dtype=Float32, value=\n",
-      "[[ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02],\n",
-      " [ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02],\n",
-      " [ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02],\n",
-      " [ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02],\n",
-      " [ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02]]), Tensor(shape=[3], dtype=Float32, value= [ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02])) [ 3.8211915 -2.994512  -1.932323 ]\n"
+      " [[ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02],\n",
+      "  [ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02],\n",
+      "  [ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02],\n",
+      "  [ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02],\n",
+      "  [ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02]]), Tensor(shape=[3], dtype=Float32, value= [ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02])) [ 3.8211915 -2.994512  -1.932323 ]\n"
      ]
     }
    ],
@@ -424,11 +424,11 @@
      "output_type": "stream",
      "text": [
       "(Tensor(shape=[5, 3], dtype=Float32, value=\n",
-      "[[ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02],\n",
-      " [ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02],\n",
-      " [ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02],\n",
-      " [ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02],\n",
-      " [ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02]]), Tensor(shape=[3], dtype=Float32, value= [ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02]))\n"
+      " [[ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02],\n",
+      "  [ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02],\n",
+      "  [ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02],\n",
+      "  [ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02],\n",
+      "  [ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02]]), Tensor(shape=[3], dtype=Float32, value= [ 3.26189250e-01,  1.58914644e-02,  4.21645455e-02]))\n"
      ]
     }
    ],