diff --git a/.gitattributes b/.gitattributes
new file mode 100644
index 0000000..d748b27
--- /dev/null
+++ b/.gitattributes
@@ -0,0 +1,2 @@
+* text=auto eol=lf
+*.ipynb filter=nbstripout
\ No newline at end of file
diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml
new file mode 100644
index 0000000..53f37c9
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/bug_report.yml
@@ -0,0 +1,96 @@
+name: 🐞 Bug report
+description: Create a report to help us reproduce and fix the bug
+title: "[Bug] "
+labels: ['bug']
+
+body:
+- type: checkboxes
+  attributes:
+    label: Checklist
+    options:
+    - label: 1. I have searched related issues but cannot get the expected help.
+    - label: 2. The bug has not been fixed in the latest version.
+    - label: 3. I have read [Contributing Guidlines](https://github.com/mindspore-lab/step_into_llm/wiki/Contributing-Guidelines).
+    - label: 4. Please note that if the bug-related issue you submitted lacks corresponding environment info and a minimal reproducible demo, it will be challenging for us to reproduce and resolve the issue, reducing the likelihood of receiving feedback.
+    - label: 5. If the issue you raised is not a bug but a question, please raise a discussion at Discussions. Otherwise, it will be closed.
+- type: textarea
+  attributes:
+    label: Describe the bug
+    description: |
+      A clear and concise description of what the bug is, including:
+        - What happened (actual behavior)
+        - What you expected (expected outcome)
+        - **Please attach error logs or screenshots if possible**—this helps us locate the issue faster.
+  validations:
+    required: true
+- type: textarea
+  attributes:
+    label: Reproduction
+    description: |
+      What command or script did you run? Please list the exact steps to reproduce the bug.
+    placeholder: |
+      A placeholder for the command.
+  validations:
+    required: true
+- type: checkboxes
+  id: hardware
+  attributes:
+    label: Hardware Environment
+    description: Which hardware type does this bug relate to (select all that apply)?
+    options:
+      - label: Ascend
+      - label: GPU
+      - label: CPU
+  validations:
+    required: true
+- type: input
+  attributes:
+    label: OS Platform
+    description: Which operating system are you using? (e.g., Ubuntu 20.04)
+    placeholder: "Ubuntu 20.04"
+  validations:
+    required: true
+- type: input
+  attributes:
+    label: Python Version
+    description: What version of Python are you using? (e.g., 3.9.7)
+    placeholder: "3.9.7"
+  validations:
+    required: true
+- type: input
+  attributes:
+    label: MindSpore Version
+    description: What version of MindSpore are you using? (e.g., 2.7.1)
+    placeholder: "2.7.1"
+  validations:
+    required: true
+- type: input
+  id: mindspore_nlp
+  attributes:
+    label: (Optional) MindSpore NLP Version
+    placeholder: e.g., 0.5.0
+  validations:
+    required: false
+- type: input
+  id: mindspore_transformers
+  attributes:
+    label: (Optional) MindSpore Transformers Version
+    placeholder: e.g., 1.7.0
+  validations:
+    required: false
+- type: textarea
+  id: other_suites
+  attributes:
+    label: (Optional) Other Toolkit or Suite Versions
+    description: |
+      Specify versions of any other MindSpore-related suites (e.g., MindSpore Lite, vLLM-MindSpore) or relevant third-party packages.
+    placeholder: |
+      e.g.,
+      - MindSpore Lite 2.7.0
+      - vLLM-MindSpore 0.4.0
+  validations:
+    required: false
+- type: textarea
+  attributes:
+    label: Additional Context
+    description: Any other details.
\ No newline at end of file
diff --git a/.github/ISSUE_TEMPLATE/feature_request.yml b/.github/ISSUE_TEMPLATE/feature_request.yml
new file mode 100644
index 0000000..2e41ee7
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/feature_request.yml
@@ -0,0 +1,39 @@
+name: 🚀 Feature request
+description: Suggest an idea for this project
+title: "[Feature] "
+
+body:
+- type: checkboxes
+  attributes:
+    label: Checklist
+    options:
+    - label: 1. I have read [Contributing Guidlines](https://github.com/mindspore-lab/step_into_llm/wiki/Contributing-Guidelines).
+    - label: 2. If the issue you raised is not a feature but a question, please raise a discussion at Discussions. Otherwise, it will be closed.
+- type: textarea
+  attributes:
+    label: Motivation
+    description: |
+      A clear and concise description of the motivation of the feature.
+  validations:
+    required: true
+- type: checkboxes
+  id: hardware
+  attributes:
+    label: Hardware Environment
+    description: Which hardware does this feature need to support (select all that apply)?
+    options:
+      - label: Ascend
+      - label: GPU
+      - label: CPU
+  validations:
+    required: true
+- type: textarea
+  attributes:
+    label: Related resources
+    description: |
+      If there is an official code release or third-party implementations, please also provide the information here, which would be very helpful.
+- type: textarea
+  attributes:
+    label: Your Contribution
+    description: |
+      Is there any way that you could help, e.g., by submitting a PR?
diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md
new file mode 100644
index 0000000..1b62f90
--- /dev/null
+++ b/.github/PULL_REQUEST_TEMPLATE.md
@@ -0,0 +1,34 @@
+<!--
+Thank you for contributing to this project! 🎉
+Please make sure your pull request follows the contribution standards so the review and merge process can go smoothly.
+-->
+
+# Description
+<!--
+Briefly describe the purpose of this PR — what it does, why it’s needed, and which issue(s) it addresses.
+Use “Fixes #<issue_number>” if applicable.
+-->
+
+# Changes
+<!--
+Summarize key modifications — new features, bug fixes, refactors, or documentation updates.
+-->
+
+# Testing & Benchmark
+<!--
+If applicable, include test results (e.g., unit tests, accuracy verification, performance benchmarks).
+Please attach **logs, screenshots or reports** demonstrating that the code runs successfully and produces the expected results.
+-->
+
+# Checklist
+
+- [ ] Read and followed the [Contributing Guidelines](https://github.com/mindspore-lab/step_into_llm/wiki/Contributing-Guidelines).
+- [ ] Self-tested locally to ensure the code runs correctly and achieves expected results (all CI checks expected to pass).
+- [ ] Updated documentation if needed.
+- [ ] Verified accuracy or performance benchmarks if applicable.
+
+# Reviewers
+<!--
+Please note: each PR must be reviewed by at least **two committers** once all tests have passed.
+You may optionally tag members or contributors who might be interested in your PR.
+-->
diff --git a/01.LLM_Theory_Course/01.Industry_Model_Introduction/01.Classic_Model_Technical_Analysis/01.BERT/bert_emotect_finetune.ipynb b/01.LLM_Theory_Course/01.Industry_Model_Introduction/01.Classic_Model_Technical_Analysis/01.BERT/bert_emotect_finetune.ipynb
index 6ceed46..74760b1 100644
--- a/01.LLM_Theory_Course/01.Industry_Model_Introduction/01.Classic_Model_Technical_Analysis/01.BERT/bert_emotect_finetune.ipynb
+++ b/01.LLM_Theory_Course/01.Industry_Model_Introduction/01.Classic_Model_Technical_Analysis/01.BERT/bert_emotect_finetune.ipynb
@@ -16,109 +16,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
-      "Collecting mindspore==2.5.0\n",
-      "  Using cached https://ms-release.obs.cn-north-4.myhuaweicloud.com/2.5.0/MindSpore/unified/aarch64/mindspore-2.5.0-cp39-cp39-linux_aarch64.whl (345.0 MB)\n",
-      "Requirement already satisfied: pip in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (25.1)\n",
-      "\u001b[31mERROR: Could not find a version that satisfies the requirement install (from versions: none)\u001b[0m\u001b[31m\n",
-      "\u001b[0m\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.1.1\u001b[0m\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n",
-      "\u001b[31mERROR: No matching distribution found for install\u001b[0m\u001b[31m\n",
-      "\u001b[0m"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/2.5.0/MindSpore/unified/aarch64/mindspore-2.5.0-cp39-cp39-linux_aarch64.whl --trusted-host ms-release.obs.cn-north-4.myhuaweicloud.com -i https://pypi.tuna.tsinghua.edu.cn/simple"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Looking in indexes: https://repo.huaweicloud.com/repository/pypi/simple/\n",
-      "Requirement already satisfied: mindnlp==0.4.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (0.4.0)\n",
-      "Requirement already satisfied: mindspore>=2.2.14 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (2.5.0)\n",
-      "Requirement already satisfied: tqdm in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (4.67.1)\n",
-      "Requirement already satisfied: requests in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (2.32.3)\n",
-      "Requirement already satisfied: datasets in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (3.6.0)\n",
-      "Requirement already satisfied: evaluate in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.4.3)\n",
-      "Requirement already satisfied: tokenizers==0.19.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.19.1)\n",
-      "Requirement already satisfied: safetensors in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.5.3)\n",
-      "Requirement already satisfied: sentencepiece in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.2.0)\n",
-      "Requirement already satisfied: regex in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (2024.11.6)\n",
-      "Requirement already satisfied: addict in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (2.4.0)\n",
-      "Requirement already satisfied: ml-dtypes in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.5.1)\n",
-      "Requirement already satisfied: pyctcdecode in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.5.0)\n",
-      "Requirement already satisfied: jieba in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.42.1)\n",
-      "Requirement already satisfied: pytest==7.2.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (7.2.0)\n",
-      "Requirement already satisfied: pillow>=10.0.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (11.2.1)\n",
-      "Requirement already satisfied: attrs>=19.2.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (25.3.0)\n",
-      "Requirement already satisfied: iniconfig in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (2.1.0)\n",
-      "Requirement already satisfied: packaging in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (24.2)\n",
-      "Requirement already satisfied: pluggy<2.0,>=0.12 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (1.5.0)\n",
-      "Requirement already satisfied: exceptiongroup>=1.0.0rc8 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (1.2.0)\n",
-      "Requirement already satisfied: tomli>=1.0.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (2.2.1)\n",
-      "Requirement already satisfied: huggingface-hub<1.0,>=0.16.4 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from tokenizers==0.19.1->mindnlp==0.4.0) (0.32.3)\n",
-      "Requirement already satisfied: filelock in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.0) (3.18.0)\n",
-      "Requirement already satisfied: fsspec>=2023.5.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.0) (2025.3.0)\n",
-      "Requirement already satisfied: pyyaml>=5.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.0) (6.0.2)\n",
-      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.0) (4.12.2)\n",
-      "Requirement already satisfied: hf-xet<2.0.0,>=1.1.2 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.0) (1.1.2)\n",
-      "Requirement already satisfied: numpy<2.0.0,>=1.20.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (1.26.4)\n",
-      "Requirement already satisfied: protobuf>=3.13.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (6.30.2)\n",
-      "Requirement already satisfied: asttokens>=2.0.4 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (3.0.0)\n",
-      "Requirement already satisfied: scipy>=1.5.4 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (1.13.1)\n",
-      "Requirement already satisfied: psutil>=5.6.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (5.9.0)\n",
-      "Requirement already satisfied: astunparse>=1.6.3 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (1.6.3)\n",
-      "Requirement already satisfied: dill>=0.3.7 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (0.3.8)\n",
-      "Requirement already satisfied: wheel<1.0,>=0.23.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from astunparse>=1.6.3->mindspore>=2.2.14->mindnlp==0.4.0) (0.45.1)\n",
-      "Requirement already satisfied: six<2.0,>=1.6.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from astunparse>=1.6.3->mindspore>=2.2.14->mindnlp==0.4.0) (1.17.0)\n",
-      "Requirement already satisfied: pyarrow>=15.0.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (20.0.0)\n",
-      "Requirement already satisfied: pandas in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (2.2.3)\n",
-      "Requirement already satisfied: xxhash in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (3.5.0)\n",
-      "Requirement already satisfied: multiprocess<0.70.17 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (0.70.16)\n",
-      "Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (3.12.7)\n",
-      "Requirement already satisfied: aiohappyeyeballs>=2.5.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (2.6.1)\n",
-      "Requirement already satisfied: aiosignal>=1.1.2 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (1.3.2)\n",
-      "Requirement already satisfied: async-timeout<6.0,>=4.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (5.0.1)\n",
-      "Requirement already satisfied: frozenlist>=1.1.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (1.6.0)\n",
-      "Requirement already satisfied: multidict<7.0,>=4.5 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (6.4.4)\n",
-      "Requirement already satisfied: propcache>=0.2.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (0.3.1)\n",
-      "Requirement already satisfied: yarl<2.0,>=1.17.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (1.20.0)\n",
-      "Requirement already satisfied: idna>=2.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from yarl<2.0,>=1.17.0->aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (3.10)\n",
-      "Requirement already satisfied: charset-normalizer<4,>=2 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from requests->mindnlp==0.4.0) (3.4.1)\n",
-      "Requirement already satisfied: urllib3<3,>=1.21.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from requests->mindnlp==0.4.0) (2.4.0)\n",
-      "Requirement already satisfied: certifi>=2017.4.17 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from requests->mindnlp==0.4.0) (2025.4.26)\n",
-      "Requirement already satisfied: python-dateutil>=2.8.2 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pandas->datasets->mindnlp==0.4.0) (2.9.0.post0)\n",
-      "Requirement already satisfied: pytz>=2020.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pandas->datasets->mindnlp==0.4.0) (2025.2)\n",
-      "Requirement already satisfied: tzdata>=2022.7 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pandas->datasets->mindnlp==0.4.0) (2025.2)\n",
-      "Requirement already satisfied: pygtrie<3.0,>=2.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pyctcdecode->mindnlp==0.4.0) (2.5.0)\n",
-      "Requirement already satisfied: hypothesis<7,>=6.14 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pyctcdecode->mindnlp==0.4.0) (6.133.2)\n",
-      "Requirement already satisfied: sortedcontainers<3.0.0,>=2.1.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from hypothesis<7,>=6.14->pyctcdecode->mindnlp==0.4.0) (2.4.0)\n",
-      "\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.1.1\u001b[0m\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!pip install mindnlp==0.4.0"
    ]
@@ -156,30 +69,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.\n",
-      "  setattr(self, word, getattr(machar, word).flat[0])\n",
-      "/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.\n",
-      "  return self._float_to_str(self.smallest_subnormal)\n",
-      "/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for <class 'numpy.float32'> type is zero.\n",
-      "  setattr(self, word, getattr(machar, word).flat[0])\n",
-      "/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for <class 'numpy.float32'> type is zero.\n",
-      "  return self._float_to_str(self.smallest_subnormal)\n",
-      "Building prefix dict from the default dictionary ...\n",
-      "Loading model from cache /tmp/jieba.cache\n",
-      "Loading model cost 0.908 seconds.\n",
-      "Prefix dict has been built successfully.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import os\n",
     "\n",
@@ -192,7 +86,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
@@ -244,35 +138,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "--2025-06-03 16:26:40--  https://baidu-nlp.bj.bcebos.com/emotion_detection-dataset-1.0.0.tar.gz\n",
-      "正在解析主机 baidu-nlp.bj.bcebos.com (baidu-nlp.bj.bcebos.com)... 36.110.192.178, 2409:8c04:1001:1203:0:ff:b0bb:4f27\n",
-      "正在连接 baidu-nlp.bj.bcebos.com (baidu-nlp.bj.bcebos.com)|36.110.192.178|:443... 已连接。\n",
-      "已发出 HTTP 请求，正在等待回应... 200 OK\n",
-      "长度：1710581 (1.6M) [application/x-gzip]\n",
-      "正在保存至: “emotion_detection.tar.gz”\n",
-      "\n",
-      "emotion_detection.t 100%[===================>]   1.63M  7.02MB/s  用时 0.2s      \n",
-      "\n",
-      "2025-06-03 16:26:41 (7.02 MB/s) - 已保存 “emotion_detection.tar.gz” [1710581/1710581])\n",
-      "\n",
-      "data/\n",
-      "data/test.tsv\n",
-      "data/infer.tsv\n",
-      "data/dev.tsv\n",
-      "data/train.tsv\n",
-      "data/vocab.txt\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# download dataset\n",
     "!wget https://baidu-nlp.bj.bcebos.com/emotion_detection-dataset-1.0.0.tar.gz -O emotion_detection.tar.gz\n",
@@ -290,7 +160,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
@@ -302,7 +172,7 @@
     "    is_ascend = mindspore.get_context('device_target') == 'Ascend'\n",
     "\n",
     "    column_names = [\"label\", \"text_a\"]\n",
-    "    \n",
+    "\n",
     "    dataset = GeneratorDataset(source, column_names=column_names, shuffle=shuffle)\n",
     "    # transforms\n",
     "    type_cast_op = transforms.TypeCast(mindspore.int32)\n",
@@ -334,76 +204,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "70d7820ca2334d3ba52d2b57e7a23918",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0.00/49.0 [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5d0038ad08204ceabeed2317ad9c5bd3",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0.00B [00:00, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0952751c1ec14f538b7ea1c9ff9fe37b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0.00B [00:00, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d02cf9b1f4904dfd9c5c223f0d797cb4",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0.00/324 [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/mindnlp/transformers/tokenization_utils_base.py:1526: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted, and will be then set to `False` by default. \n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from mindnlp.transformers import BertTokenizer\n",
     "tokenizer = BertTokenizer.from_pretrained('bert-base-chinese')"
@@ -411,29 +216,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "tokenizer.pad_token_id"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
@@ -446,75 +240,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['input_ids', 'attention_mask', 'labels']"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "dataset_train.get_col_names()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[Tensor(shape=[32, 64], dtype=Int64, value=\n",
-      "[[ 101, 1968, 1968 ...    0,    0,    0],\n",
-      " [ 101,  679, 4761 ...    0,    0,    0],\n",
-      " [ 101,  679, 3236 ...    0,    0,    0],\n",
-      " ...\n",
-      " [ 101, 7583, 7583 ...    0,    0,    0],\n",
-      " [ 101,  872,  679 ...    0,    0,    0],\n",
-      " [ 101, 2876, 2805 ...    0,    0,    0]]), Tensor(shape=[32, 64], dtype=Int64, value=\n",
-      "[[1, 1, 1 ... 0, 0, 0],\n",
-      " [1, 1, 1 ... 0, 0, 0],\n",
-      " [1, 1, 1 ... 0, 0, 0],\n",
-      " ...\n",
-      " [1, 1, 1 ... 0, 0, 0],\n",
-      " [1, 1, 1 ... 0, 0, 0],\n",
-      " [1, 1, 1 ... 0, 0, 0]]), Tensor(shape=[32], dtype=Int32, value= [1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 2, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, \n",
-      " 1, 1, 1, 1, 2, 1, 0, 0])]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(next(dataset_train.create_tuple_iterator()))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "env: HF_ENDPOINT=https://hf-mirror.com\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%env HF_ENDPOINT=https://hf-mirror.com"
    ]
@@ -530,36 +282,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "15fc8f5f71ab4c5ea26e9e6b9e5f0743",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0.00/392M [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[WARNING] DEVICE(3513,ffffa2651020,python):2025-06-03-16:41:44.906.806 [mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_vmm_adapter.h:147] CheckVmmDriverVersion] Open file /etc/ascend_install.info failed.\n",
-      "[WARNING] DEVICE(3513,ffffa2651020,python):2025-06-03-16:41:44.906.895 [mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_vmm_adapter.h:186] CheckVmmDriverVersion] Driver version is less than 24.0.0, vmm is disabled by default, drvier_version: 23.0.6\n",
-      "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-chinese and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
-      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from mindnlp.transformers import BertForSequenceClassification, BertModel\n",
     "\n",
@@ -569,7 +296,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
@@ -580,18 +307,18 @@
     "def compute_metrics(eval_pred):\n",
     "    predictions = eval_pred.predictions\n",
     "    labels = eval_pred.label_ids\n",
-    "    \n",
+    "\n",
     "    if len(predictions.shape) > 1:\n",
     "        predictions = np.argmax(predictions, axis=-1)\n",
     "\n",
     "    accuracy = (predictions == labels).mean()\n",
-    "    \n",
+    "\n",
     "    return {\"accuracy\": float(accuracy)}"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
@@ -624,156 +351,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a557eb2f81dd4b7893e2173ae25c116b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/1510 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      ".{'loss': 0.3728, 'learning_rate': 1.7350993377483446e-05, 'epoch': 0.66}\n",
-      "."
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
-       "version_major": 2,
-       "version_minor": 0
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'eval_loss': 0.17702096700668335, 'eval_accuracy': 0.9351851851851852, 'eval_runtime': 1.5801, 'eval_samples_per_second': 21.518, 'eval_steps_per_second': 21.518, 'epoch': 1.0}\n",
-      "{'loss': 0.2414, 'learning_rate': 1.4701986754966889e-05, 'epoch': 1.32}\n",
-      "{'loss': 0.18, 'learning_rate': 1.2052980132450332e-05, 'epoch': 1.99}\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'eval_loss': 0.11940661072731018, 'eval_accuracy': 0.9629629629629629, 'eval_runtime': 1.8033, 'eval_samples_per_second': 18.854, 'eval_steps_per_second': 18.854, 'epoch': 2.0}\n",
-      "{'loss': 0.126, 'learning_rate': 9.403973509933776e-06, 'epoch': 2.65}\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'eval_loss': 0.09447185695171356, 'eval_accuracy': 0.9694444444444444, 'eval_runtime': 1.8203, 'eval_samples_per_second': 18.678, 'eval_steps_per_second': 18.678, 'epoch': 3.0}\n",
-      "{'loss': 0.1036, 'learning_rate': 6.754966887417219e-06, 'epoch': 3.31}\n",
-      "{'loss': 0.0753, 'learning_rate': 4.105960264900663e-06, 'epoch': 3.97}\n"
-     ]
-    },
-    {
-     "data": {
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'eval_loss': 0.056950367987155914, 'eval_accuracy': 0.9861111111111112, 'eval_runtime': 1.5702, 'eval_samples_per_second': 21.653, 'eval_steps_per_second': 21.653, 'epoch': 4.0}\n",
-      "{'loss': 0.0568, 'learning_rate': 1.456953642384106e-06, 'epoch': 4.64}\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
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-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'eval_loss': 0.03606536239385605, 'eval_accuracy': 0.9916666666666667, 'eval_runtime': 1.8247, 'eval_samples_per_second': 18.633, 'eval_steps_per_second': 18.633, 'epoch': 5.0}\n",
-      "{'train_runtime': 666.5481, 'train_samples_per_second': 72.493, 'train_steps_per_second': 2.265, 'train_loss': 0.1572099215147511, 'epoch': 5.0}\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "TrainOutput(global_step=1510, training_loss=0.1572099215147511, metrics={'train_runtime': 666.5481, 'train_samples_per_second': 72.493, 'train_steps_per_second': 2.265, 'train_loss': 0.1572099215147511, 'epoch': 5.0})"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# start training\n",
     "trainer.train()"
@@ -790,26 +372,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 34/34 [00:01<00:00, 18.97it/s, acc=0.992]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy: 0.9917\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from tqdm import tqdm\n",
     "import numpy as np\n",
@@ -844,33 +411,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 34/34 [00:01<00:00, 19.84it/s, acc=0.992]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy: 0.9917279411764706\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "acc = evaluate_fn(model, dataset_val)\n",
     "print(f\"Accuracy: {acc}\")"
@@ -887,7 +432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
@@ -898,7 +443,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
@@ -918,31 +463,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "inputs: '我 要 客观', predict: '中性' , label: '中性'\n",
-      "inputs: '靠 你 真是 说 废话 吗', predict: '消极' , label: '消极'\n",
-      "inputs: '口嗅 会', predict: '中性' , label: '中性'\n",
-      "inputs: '每次 是 表妹 带 窝 飞 因为 窝路痴', predict: '中性' , label: '中性'\n",
-      "inputs: '别说 废话 我 问 你 个 问题', predict: '消极' , label: '消极'\n",
-      "inputs: '4967 是 新加坡 那 家 银行', predict: '中性' , label: '中性'\n",
-      "inputs: '是 我 喜欢 兔子', predict: '积极' , label: '积极'\n",
-      "inputs: '你 写 过 黄山 奇石 吗', predict: '中性' , label: '中性'\n",
-      "inputs: '一个一个 慢慢来', predict: '中性' , label: '中性'\n",
-      "inputs: '我 玩 过 这个 一点 都 不 好玩', predict: '消极' , label: '消极'\n",
-      "inputs: '网上 开发 女孩 的 QQ', predict: '中性' , label: '中性'\n",
-      "inputs: '背 你 猜 对 了', predict: '中性' , label: '中性'\n",
-      "inputs: '我 讨厌 你 ， 哼哼 哼 。 。', predict: '消极' , label: '消极'\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from mindspore import Tensor\n",
     "\n",
@@ -961,19 +486,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "inputs: '家人们咱就是说一整个无语住了 绝绝子叠buff', predict: '中性'\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "predict(\"家人们咱就是说一整个无语住了 绝绝子叠buff\")"
    ]
diff --git a/01.LLM_Theory_Course/01.Industry_Model_Introduction/01.Classic_Model_Technical_Analysis/01.BERT/bert_introduction.ipynb b/01.LLM_Theory_Course/01.Industry_Model_Introduction/01.Classic_Model_Technical_Analysis/01.BERT/bert_introduction.ipynb
index 99ba599..b59b374 100644
--- a/01.LLM_Theory_Course/01.Industry_Model_Introduction/01.Classic_Model_Technical_Analysis/01.BERT/bert_introduction.ipynb
+++ b/01.LLM_Theory_Course/01.Industry_Model_Introduction/01.Classic_Model_Technical_Analysis/01.BERT/bert_introduction.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "35bd8ac0",
+   "id": "0",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -15,7 +15,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "576ed71b",
+   "id": "1",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -28,7 +28,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "67f34239",
+   "id": "2",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -44,7 +44,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "63fabf36",
+   "id": "3",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -72,7 +72,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "553a7118",
+   "id": "4",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -89,7 +89,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9558644f",
+   "id": "5",
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -107,7 +107,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f242c5a3",
+   "id": "6",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -131,7 +131,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e25a5669",
+   "id": "7",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -157,7 +157,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3b7e625e",
+   "id": "8",
    "metadata": {},
    "source": [
     "<div align=center><img src=\"./assets/bert-base-bert-large-encoders.png\"></div>"
@@ -165,7 +165,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e42d1ce8",
+   "id": "9",
    "metadata": {},
    "source": [
     "接受输入序列后，BERT会输出每个位置对应的向量（长度等于hidden size），在后续下游任务中，我们会选取与任务相关的位置的向量，输入到最终输出层中得到结果。\n",
@@ -177,7 +177,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a27de31d",
+   "id": "10",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -198,87 +198,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "d6c39086",
+   "execution_count": null,
+   "id": "11",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Looking in indexes: https://repo.huaweicloud.com/repository/pypi/simple/\n",
-      "Requirement already satisfied: mindnlp==0.4.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (0.4.0)\n",
-      "Requirement already satisfied: mindspore>=2.2.14 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (2.4.1)\n",
-      "Requirement already satisfied: tqdm in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (4.67.1)\n",
-      "Requirement already satisfied: requests in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (2.32.3)\n",
-      "Requirement already satisfied: datasets in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (3.6.0)\n",
-      "Requirement already satisfied: evaluate in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.4.3)\n",
-      "Requirement already satisfied: tokenizers==0.19.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.19.1)\n",
-      "Requirement already satisfied: safetensors in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.5.3)\n",
-      "Requirement already satisfied: sentencepiece in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.2.0)\n",
-      "Requirement already satisfied: regex in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (2024.11.6)\n",
-      "Requirement already satisfied: addict in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (2.4.0)\n",
-      "Requirement already satisfied: ml-dtypes in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.5.1)\n",
-      "Requirement already satisfied: pyctcdecode in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.5.0)\n",
-      "Requirement already satisfied: jieba in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.42.1)\n",
-      "Requirement already satisfied: pytest==7.2.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (7.2.0)\n",
-      "Requirement already satisfied: pillow>=10.0.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (11.2.1)\n",
-      "Requirement already satisfied: attrs>=19.2.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (25.3.0)\n",
-      "Requirement already satisfied: iniconfig in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (2.1.0)\n",
-      "Requirement already satisfied: packaging in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (24.2)\n",
-      "Requirement already satisfied: pluggy<2.0,>=0.12 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (1.5.0)\n",
-      "Requirement already satisfied: exceptiongroup>=1.0.0rc8 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (1.2.0)\n",
-      "Requirement already satisfied: tomli>=1.0.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (2.2.1)\n",
-      "Requirement already satisfied: huggingface-hub<1.0,>=0.16.4 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from tokenizers==0.19.1->mindnlp==0.4.0) (0.31.1)\n",
-      "Requirement already satisfied: filelock in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.0) (3.18.0)\n",
-      "Requirement already satisfied: fsspec>=2023.5.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.0) (2025.3.0)\n",
-      "Requirement already satisfied: pyyaml>=5.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.0) (6.0.2)\n",
-      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.0) (4.12.2)\n",
-      "Requirement already satisfied: hf-xet<2.0.0,>=1.1.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.0) (1.1.0)\n",
-      "Requirement already satisfied: numpy<2.0.0,>=1.20.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (1.26.4)\n",
-      "Requirement already satisfied: protobuf>=3.13.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (6.30.2)\n",
-      "Requirement already satisfied: asttokens>=2.0.4 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (3.0.0)\n",
-      "Requirement already satisfied: scipy>=1.5.4 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (1.13.1)\n",
-      "Requirement already satisfied: psutil>=5.6.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (5.9.0)\n",
-      "Requirement already satisfied: astunparse>=1.6.3 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (1.6.3)\n",
-      "Requirement already satisfied: wheel<1.0,>=0.23.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from astunparse>=1.6.3->mindspore>=2.2.14->mindnlp==0.4.0) (0.45.1)\n",
-      "Requirement already satisfied: six<2.0,>=1.6.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from astunparse>=1.6.3->mindspore>=2.2.14->mindnlp==0.4.0) (1.17.0)\n",
-      "Requirement already satisfied: pyarrow>=15.0.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (20.0.0)\n",
-      "Requirement already satisfied: dill<0.3.9,>=0.3.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (0.3.8)\n",
-      "Requirement already satisfied: pandas in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (2.2.3)\n",
-      "Requirement already satisfied: xxhash in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (3.5.0)\n",
-      "Requirement already satisfied: multiprocess<0.70.17 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (0.70.16)\n",
-      "Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (3.11.18)\n",
-      "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (2.6.1)\n",
-      "Requirement already satisfied: aiosignal>=1.1.2 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (1.3.2)\n",
-      "Requirement already satisfied: async-timeout<6.0,>=4.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (5.0.1)\n",
-      "Requirement already satisfied: frozenlist>=1.1.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (1.6.0)\n",
-      "Requirement already satisfied: multidict<7.0,>=4.5 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (6.4.3)\n",
-      "Requirement already satisfied: propcache>=0.2.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (0.3.1)\n",
-      "Requirement already satisfied: yarl<2.0,>=1.17.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (1.20.0)\n",
-      "Requirement already satisfied: idna>=2.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from yarl<2.0,>=1.17.0->aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (3.10)\n",
-      "Requirement already satisfied: charset-normalizer<4,>=2 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from requests->mindnlp==0.4.0) (3.4.1)\n",
-      "Requirement already satisfied: urllib3<3,>=1.21.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from requests->mindnlp==0.4.0) (2.4.0)\n",
-      "Requirement already satisfied: certifi>=2017.4.17 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from requests->mindnlp==0.4.0) (2025.4.26)\n",
-      "Requirement already satisfied: python-dateutil>=2.8.2 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pandas->datasets->mindnlp==0.4.0) (2.9.0.post0)\n",
-      "Requirement already satisfied: pytz>=2020.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pandas->datasets->mindnlp==0.4.0) (2025.2)\n",
-      "Requirement already satisfied: tzdata>=2022.7 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pandas->datasets->mindnlp==0.4.0) (2025.2)\n",
-      "Requirement already satisfied: pygtrie<3.0,>=2.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pyctcdecode->mindnlp==0.4.0) (2.5.0)\n",
-      "Requirement already satisfied: hypothesis<7,>=6.14 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pyctcdecode->mindnlp==0.4.0) (6.131.15)\n",
-      "Requirement already satisfied: sortedcontainers<3.0.0,>=2.1.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from hypothesis<7,>=6.14->pyctcdecode->mindnlp==0.4.0) (2.4.0)\n",
-      "\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.1.1\u001b[0m\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n",
-      "Looking in indexes: https://repo.huaweicloud.com/repository/pypi/simple/\n",
-      "Requirement already satisfied: pytesseract in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (0.3.13)\n",
-      "Requirement already satisfied: packaging>=21.3 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytesseract) (24.2)\n",
-      "Requirement already satisfied: Pillow>=8.0.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytesseract) (11.2.1)\n",
-      "\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.1.1\u001b[0m\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# install mindnlp\n",
     "!pip install mindnlp==0.4.0\n",
@@ -287,94 +210,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "0ad0c6f0",
+   "execution_count": null,
+   "id": "12",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[WARNING] GE_ADPT(6108,ffffab724020,python):2025-05-13-12:28:08.673.160 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclmdlBundleGetModelId failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclmdlBundleGetModelId\n",
-      "[WARNING] GE_ADPT(6108,ffffab724020,python):2025-05-13-12:28:08.673.218 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclmdlBundleLoadFromMem failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclmdlBundleLoadFromMem\n",
-      "[WARNING] GE_ADPT(6108,ffffab724020,python):2025-05-13-12:28:08.673.244 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclmdlBundleUnload failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclmdlBundleUnload\n",
-      "[WARNING] GE_ADPT(6108,ffffab724020,python):2025-05-13-12:28:08.673.383 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclrtGetMemUceInfo failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclrtGetMemUceInfo\n",
-      "[WARNING] GE_ADPT(6108,ffffab724020,python):2025-05-13-12:28:08.673.408 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclrtDeviceTaskAbort failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclrtDeviceTaskAbort\n",
-      "[WARNING] GE_ADPT(6108,ffffab724020,python):2025-05-13-12:28:08.673.431 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclrtMemUceRepair failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclrtMemUceRepair\n",
-      "[WARNING] GE_ADPT(6108,ffffab724020,python):2025-05-13-12:28:08.674.937 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol acltdtCleanChannel failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libacl_tdt_channel.so: undefined symbol: acltdtCleanChannel\n",
-      "/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.\n",
-      "  setattr(self, word, getattr(machar, word).flat[0])\n",
-      "/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.\n",
-      "  return self._float_to_str(self.smallest_subnormal)\n",
-      "/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for <class 'numpy.float32'> type is zero.\n",
-      "  setattr(self, word, getattr(machar, word).flat[0])\n",
-      "/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for <class 'numpy.float32'> type is zero.\n",
-      "  return self._float_to_str(self.smallest_subnormal)\n",
-      "Building prefix dict from the default dictionary ...\n",
-      "Loading model from cache /tmp/jieba.cache\n",
-      "Loading model cost 1.042 seconds.\n",
-      "Prefix dict has been built successfully.\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6421e22f124345fe924aa445c22403a2",
-       "version_major": 2,
-       "version_minor": 0
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-       "0.00B [00:00, ?B/s]"
-      ]
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-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "30735811eedd4f2ea525d38c702c177f",
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-      ]
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-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2e931b32674045c599aeb89a2088f180",
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'input_ids': [101, 2393, 3159, 2089, 6968, 2080, 4651, 4121, 12839, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}\n",
-      "['[CLS]', 'help', 'prince', 'may', '##uk', '##o', 'transfer', 'huge', 'inheritance', '[SEP]']\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/mindnlp/transformers/tokenization_utils_base.py:1526: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted, and will be then set to `False` by default. \n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from mindnlp.transformers import BertTokenizer\n",
     "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
@@ -389,7 +228,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "baf4efc6",
+   "id": "13",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -410,7 +249,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fb1d92c5",
+   "id": "14",
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -422,8 +261,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "d04340e2",
+   "execution_count": null,
+   "id": "15",
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -456,7 +295,7 @@
     "            position_ids = position_ids.expand_dims(0).expand_as(input_ids)\n",
     "        if token_type_ids is None:\n",
     "            token_type_ids = ops.zeros_like(input_ids)\n",
-    "        \n",
+    "\n",
     "        words_embeddings = self.word_embeddings(input_ids)\n",
     "        position_embeddings = self.position_embeddings(position_ids)\n",
     "        token_type_embeddings = self.token_type_embeddings(token_type_ids)\n",
@@ -468,7 +307,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "24afcb4a",
+   "id": "16",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -487,7 +326,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "931ad4de",
+   "id": "17",
    "metadata": {},
    "source": [
     "### BERT self-attention 层\n",
@@ -497,8 +336,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "b5068b5c",
+   "execution_count": null,
+   "id": "18",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -570,7 +409,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9983cb64",
+   "id": "19",
    "metadata": {},
    "source": [
     "### BERT self-attention 输出层 \n",
@@ -583,8 +422,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "d7ec92f7",
+   "execution_count": null,
+   "id": "20",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -609,7 +448,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b3ef35f5",
+   "id": "21",
    "metadata": {},
    "source": [
     "### BERT feed-forward 层"
@@ -617,8 +456,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "073f2754",
+   "execution_count": null,
+   "id": "22",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -640,7 +479,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bd863caa",
+   "id": "23",
    "metadata": {},
    "source": [
     "### BERT 最后的Add&Norm"
@@ -648,8 +487,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "0732f767",
+   "execution_count": null,
+   "id": "24",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -673,7 +512,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "66995cbe",
+   "id": "25",
    "metadata": {},
    "source": [
     "### BERT Encoder\n",
@@ -686,8 +525,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "7752a2ff",
+   "execution_count": null,
+   "id": "26",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -746,7 +585,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "546a1b39",
+   "id": "27",
    "metadata": {},
    "source": [
     "## BERT 输出\n",
@@ -760,7 +599,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3466d706",
+   "id": "28",
    "metadata": {},
    "source": [
     "### BERT Pooler"
@@ -768,8 +607,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "50b79508",
+   "execution_count": null,
+   "id": "29",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -791,7 +630,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9e0df426",
+   "id": "30",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -810,7 +649,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ef77d903",
+   "id": "31",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -831,7 +670,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0fa96afc",
+   "id": "32",
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -848,7 +687,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3f4469a1",
+   "id": "33",
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -860,29 +699,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "53d4e932",
+   "execution_count": null,
+   "id": "34",
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
     }
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[WARNING] DEVICE(6108,ffffab724020,python):2025-05-13-12:28:58.320.316 [mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_memory_adapter.cc:116] Initialize] Free memory size is less than half of total memory size.Device 0 Device HBM total size:34359738368 Device HBM free size:66969600 may be other processes occupying this card, check as: ps -ef|grep python\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[MS_ALLOC_CONF]Runtime config:  enable_vmm:True  vmm_align_size:2MB\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "activation_map = {\n",
     "    'relu': nn.ReLU(),\n",
@@ -897,7 +721,7 @@
     "        self.dense = nn.Dense(config.hidden_size, config.hidden_size, weight_init=TruncatedNormal(config.initializer_range))\n",
     "        self.transform_act_fn = activation_map.get(config.hidden_act, nn.GELU(False))\n",
     "        self.layer_norm = nn.LayerNorm((config.hidden_size,), epsilon=config.layer_norm_eps)\n",
-    "    \n",
+    "\n",
     "    def construct(self, hidden_states):\n",
     "        hidden_states = self.dense(hidden_states)\n",
     "        hidden_states = self.transform_act_fn(hidden_states)\n",
@@ -907,7 +731,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c825d61f",
+   "id": "35",
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -919,8 +743,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "de0cc3f8",
+   "execution_count": null,
+   "id": "36",
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -956,7 +780,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f64c0e09",
+   "id": "37",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -979,7 +803,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a209b829",
+   "id": "38",
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -991,8 +815,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "e1784651",
+   "execution_count": null,
+   "id": "39",
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -1004,7 +828,7 @@
     "    def __init__(self, config):\n",
     "        super(BertPooler, self).__init__()\n",
     "        self.dense = nn.Dense(config.hidden_size, config.hidden_size, activation='tanh', weight_init=TruncatedNormal(config.initializer_range))\n",
-    "    \n",
+    "\n",
     "    def construct(self, hidden_states):\n",
     "        first_token_tensor = hidden_states[:, 0]\n",
     "        pooled_output = self.dense(first_token_tensor)\n",
@@ -1013,7 +837,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1b75af11",
+   "id": "40",
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -1027,8 +851,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "edc1f2a8",
+   "execution_count": null,
+   "id": "41",
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -1041,7 +865,7 @@
     "        super(BertPreTrainingHeads, self).__init__()\n",
     "        self.predictions = BertLMPredictionHead(config)\n",
     "        self.seq_relationship = nn.Dense(config.hidden_size, 2, weight_init=TruncatedNormal(config.initializer_range))\n",
-    "    \n",
+    "\n",
     "    def construct(self, sequence_output, pooled_output, masked_lm_positions):\n",
     "        prediction_scores = self.predictions(sequence_output, masked_lm_positions)\n",
     "        seq_relationship_score = self.seq_relationship(pooled_output)\n",
@@ -1050,7 +874,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e5bd5d8b",
+   "id": "42",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1070,8 +894,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "0dc5a310",
+   "execution_count": null,
+   "id": "43",
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
diff --git a/01.LLM_Theory_Course/01.Industry_Model_Introduction/01.Classic_Model_Technical_Analysis/01.BERT/bert_pretrain.ipynb b/01.LLM_Theory_Course/01.Industry_Model_Introduction/01.Classic_Model_Technical_Analysis/01.BERT/bert_pretrain.ipynb
index 860c363..3641c25 100644
--- a/01.LLM_Theory_Course/01.Industry_Model_Introduction/01.Classic_Model_Technical_Analysis/01.BERT/bert_pretrain.ipynb
+++ b/01.LLM_Theory_Course/01.Industry_Model_Introduction/01.Classic_Model_Technical_Analysis/01.BERT/bert_pretrain.ipynb
@@ -7,7 +7,7 @@
     }
    },
    "cell_type": "markdown",
-   "id": "da7c69f9",
+   "id": "0",
    "metadata": {},
    "source": [
     "### DataParallel\n",
@@ -18,7 +18,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "48000382",
+   "id": "1",
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/01.LLM_Theory_Course/01.Industry_Model_Introduction/01.Classic_Model_Technical_Analysis/02.GPT/gpt_imdb_finetune.ipynb b/01.LLM_Theory_Course/01.Industry_Model_Introduction/01.Classic_Model_Technical_Analysis/02.GPT/gpt_imdb_finetune.ipynb
index 66ecb2d..7990ff3 100644
--- a/01.LLM_Theory_Course/01.Industry_Model_Introduction/01.Classic_Model_Technical_Analysis/02.GPT/gpt_imdb_finetune.ipynb
+++ b/01.LLM_Theory_Course/01.Industry_Model_Introduction/01.Classic_Model_Technical_Analysis/02.GPT/gpt_imdb_finetune.ipynb
@@ -9,200 +9,55 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
-      "Collecting mindspore==2.5.0\n",
-      "  Downloading https://ms-release.obs.cn-north-4.myhuaweicloud.com/2.5.0/MindSpore/unified/aarch64/mindspore-2.5.0-cp39-cp39-linux_aarch64.whl (345.0 MB)\n",
-      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m345.0/345.0 MB\u001b[0m \u001b[31m112.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
-      "\u001b[?25hRequirement already satisfied: pip in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (25.0.1)\n",
-      "\u001b[31mERROR: Could not find a version that satisfies the requirement install (from versions: none)\u001b[0m\u001b[31m\n",
-      "\u001b[0m\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.1.1\u001b[0m\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n",
-      "\u001b[31mERROR: No matching distribution found for install\u001b[0m\u001b[31m\n",
-      "\u001b[0m"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!pip install pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/2.5.0/MindSpore/unified/aarch64/mindspore-2.5.0-cp39-cp39-linux_aarch64.whl --trusted-host ms-release.obs.cn-north-4.myhuaweicloud.com -i https://pypi.tuna.tsinghua.edu.cn/simple"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Looking in indexes: https://repo.huaweicloud.com/repository/pypi/simple/\n",
-      "Requirement already satisfied: mindnlp==0.4.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (0.4.0)\n",
-      "Requirement already satisfied: mindspore>=2.2.14 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (2.4.1)\n",
-      "Requirement already satisfied: tqdm in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (4.67.1)\n",
-      "Requirement already satisfied: requests in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (2.32.3)\n",
-      "Requirement already satisfied: datasets in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (3.6.0)\n",
-      "Requirement already satisfied: evaluate in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.4.3)\n",
-      "Requirement already satisfied: tokenizers==0.19.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.19.1)\n",
-      "Requirement already satisfied: safetensors in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.5.3)\n",
-      "Requirement already satisfied: sentencepiece in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.2.0)\n",
-      "Requirement already satisfied: regex in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (2024.11.6)\n",
-      "Requirement already satisfied: addict in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (2.4.0)\n",
-      "Requirement already satisfied: ml-dtypes in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.5.1)\n",
-      "Requirement already satisfied: pyctcdecode in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.5.0)\n",
-      "Requirement already satisfied: jieba in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.42.1)\n",
-      "Requirement already satisfied: pytest==7.2.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (7.2.0)\n",
-      "Requirement already satisfied: pillow>=10.0.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (11.1.0)\n",
-      "Requirement already satisfied: attrs>=19.2.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (25.3.0)\n",
-      "Requirement already satisfied: iniconfig in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (2.1.0)\n",
-      "Requirement already satisfied: packaging in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (24.2)\n",
-      "Requirement already satisfied: pluggy<2.0,>=0.12 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (1.5.0)\n",
-      "Requirement already satisfied: exceptiongroup>=1.0.0rc8 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (1.2.0)\n",
-      "Requirement already satisfied: tomli>=1.0.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (2.2.1)\n",
-      "Requirement already satisfied: huggingface-hub<1.0,>=0.16.4 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from tokenizers==0.19.1->mindnlp==0.4.0) (0.32.3)\n",
-      "Requirement already satisfied: numpy<2.0.0,>=1.20.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (1.26.4)\n",
-      "Requirement already satisfied: protobuf>=3.13.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (6.30.2)\n",
-      "Requirement already satisfied: asttokens>=2.0.4 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (2.0.5)\n",
-      "Requirement already satisfied: scipy>=1.5.4 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (1.13.1)\n",
-      "Requirement already satisfied: psutil>=5.6.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (5.9.0)\n",
-      "Requirement already satisfied: astunparse>=1.6.3 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (1.6.3)\n",
-      "Requirement already satisfied: filelock in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (3.18.0)\n",
-      "Requirement already satisfied: pyarrow>=15.0.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (20.0.0)\n",
-      "Requirement already satisfied: dill<0.3.9,>=0.3.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (0.3.8)\n",
-      "Requirement already satisfied: pandas in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (2.2.3)\n",
-      "Requirement already satisfied: xxhash in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (3.5.0)\n",
-      "Requirement already satisfied: multiprocess<0.70.17 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (0.70.16)\n",
-      "Requirement already satisfied: fsspec<=2025.3.0,>=2023.1.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (2025.3.0)\n",
-      "Requirement already satisfied: pyyaml>=5.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (6.0.2)\n",
-      "Requirement already satisfied: charset-normalizer<4,>=2 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from requests->mindnlp==0.4.0) (3.4.1)\n",
-      "Requirement already satisfied: idna<4,>=2.5 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from requests->mindnlp==0.4.0) (3.10)\n",
-      "Requirement already satisfied: urllib3<3,>=1.21.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from requests->mindnlp==0.4.0) (2.3.0)\n",
-      "Requirement already satisfied: certifi>=2017.4.17 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from requests->mindnlp==0.4.0) (2025.1.31)\n",
-      "Requirement already satisfied: pygtrie<3.0,>=2.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pyctcdecode->mindnlp==0.4.0) (2.5.0)\n",
-      "Requirement already satisfied: hypothesis<7,>=6.14 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pyctcdecode->mindnlp==0.4.0) (6.133.2)\n",
-      "Requirement already satisfied: six in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from asttokens>=2.0.4->mindspore>=2.2.14->mindnlp==0.4.0) (1.16.0)\n",
-      "Requirement already satisfied: wheel<1.0,>=0.23.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from astunparse>=1.6.3->mindspore>=2.2.14->mindnlp==0.4.0) (0.45.1)\n",
-      "Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (3.12.7)\n",
-      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.0) (4.12.2)\n",
-      "Requirement already satisfied: hf-xet<2.0.0,>=1.1.2 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.0) (1.1.2)\n",
-      "Requirement already satisfied: sortedcontainers<3.0.0,>=2.1.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from hypothesis<7,>=6.14->pyctcdecode->mindnlp==0.4.0) (2.4.0)\n",
-      "Requirement already satisfied: python-dateutil>=2.8.2 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pandas->datasets->mindnlp==0.4.0) (2.9.0.post0)\n",
-      "Requirement already satisfied: pytz>=2020.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pandas->datasets->mindnlp==0.4.0) (2025.2)\n",
-      "Requirement already satisfied: tzdata>=2022.7 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pandas->datasets->mindnlp==0.4.0) (2025.2)\n",
-      "Requirement already satisfied: aiohappyeyeballs>=2.5.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (2.6.1)\n",
-      "Requirement already satisfied: aiosignal>=1.1.2 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (1.3.2)\n",
-      "Requirement already satisfied: async-timeout<6.0,>=4.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (5.0.1)\n",
-      "Requirement already satisfied: frozenlist>=1.1.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (1.6.0)\n",
-      "Requirement already satisfied: multidict<7.0,>=4.5 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (6.4.4)\n",
-      "Requirement already satisfied: propcache>=0.2.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (0.3.1)\n",
-      "Requirement already satisfied: yarl<2.0,>=1.17.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (1.20.0)\n",
-      "\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.1.1\u001b[0m\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!pip install mindnlp==0.4.0"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Looking in indexes: https://repo.huaweicloud.com/repository/pypi/simple/\n",
-      "Requirement already satisfied: jieba in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (0.42.1)\n",
-      "\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.1.1\u001b[0m\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!pip install jieba"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "env: HF_ENDPOINT=https://hf-mirror.com\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%env HF_ENDPOINT=https://hf-mirror.com"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[WARNING] ME(250:281472944873504,MainProcess):2025-06-03-14:44:02.546.745 [mindspore/run_check/_check_version.py:329] MindSpore version 2.4.1 and Ascend AI software package (Ascend Data Center Solution)version 7.6 does not match, the version of software package expect one of ['7.3', '7.5']. Please refer to the match info on: https://www.mindspore.cn/install\n",
-      "/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.\n",
-      "  setattr(self, word, getattr(machar, word).flat[0])\n",
-      "/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.\n",
-      "  return self._float_to_str(self.smallest_subnormal)\n",
-      "/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for <class 'numpy.float32'> type is zero.\n",
-      "  setattr(self, word, getattr(machar, word).flat[0])\n",
-      "/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for <class 'numpy.float32'> type is zero.\n",
-      "  return self._float_to_str(self.smallest_subnormal)\n",
-      "[WARNING] ME(250:281472944873504,MainProcess):2025-06-03-14:44:04.810.368 [mindspore/run_check/_check_version.py:347] MindSpore version 2.4.1 and \"te\" wheel package version 7.6 does not match. For details, refer to the installation guidelines: https://www.mindspore.cn/install\n",
-      "[WARNING] ME(250:281472944873504,MainProcess):2025-06-03-14:44:04.812.087 [mindspore/run_check/_check_version.py:354] MindSpore version 2.4.1 and \"hccl\" wheel package version 7.6 does not match. For details, refer to the installation guidelines: https://www.mindspore.cn/install\n",
-      "[WARNING] ME(250:281472944873504,MainProcess):2025-06-03-14:44:04.812.731 [mindspore/run_check/_check_version.py:368] Please pay attention to the above warning, countdown: 3\n",
-      "[WARNING] ME(250:281472944873504,MainProcess):2025-06-03-14:44:05.814.363 [mindspore/run_check/_check_version.py:368] Please pay attention to the above warning, countdown: 2\n",
-      "[WARNING] ME(250:281472944873504,MainProcess):2025-06-03-14:44:06.816.061 [mindspore/run_check/_check_version.py:368] Please pay attention to the above warning, countdown: 1\n",
-      "[WARNING] ME(250:281472944873504,MainProcess):2025-06-03-14:44:09.473.235 [mindspore/run_check/_check_version.py:329] MindSpore version 2.4.1 and Ascend AI software package (Ascend Data Center Solution)version 7.6 does not match, the version of software package expect one of ['7.3', '7.5']. Please refer to the match info on: https://www.mindspore.cn/install\n",
-      "[WARNING] ME(250:281472944873504,MainProcess):2025-06-03-14:44:09.474.964 [mindspore/run_check/_check_version.py:347] MindSpore version 2.4.1 and \"te\" wheel package version 7.6 does not match. For details, refer to the installation guidelines: https://www.mindspore.cn/install\n",
-      "[WARNING] ME(250:281472944873504,MainProcess):2025-06-03-14:44:09.475.567 [mindspore/run_check/_check_version.py:354] MindSpore version 2.4.1 and \"hccl\" wheel package version 7.6 does not match. For details, refer to the installation guidelines: https://www.mindspore.cn/install\n",
-      "[WARNING] ME(250:281472944873504,MainProcess):2025-06-03-14:44:09.476.234 [mindspore/run_check/_check_version.py:368] Please pay attention to the above warning, countdown: 3\n",
-      "[WARNING] ME(250:281472944873504,MainProcess):2025-06-03-14:44:10.477.850 [mindspore/run_check/_check_version.py:368] Please pay attention to the above warning, countdown: 2\n",
-      "[WARNING] ME(250:281472944873504,MainProcess):2025-06-03-14:44:11.478.869 [mindspore/run_check/_check_version.py:368] Please pay attention to the above warning, countdown: 1\n",
-      "[WARNING] ME(250:281472944873504,MainProcess):2025-06-03-14:44:12.480.761 [mindspore/run_check/_check_version.py:329] MindSpore version 2.4.1 and Ascend AI software package (Ascend Data Center Solution)version 7.6 does not match, the version of software package expect one of ['7.3', '7.5']. Please refer to the match info on: https://www.mindspore.cn/install\n",
-      "[WARNING] ME(250:281472944873504,MainProcess):2025-06-03-14:44:12.482.175 [mindspore/run_check/_check_version.py:347] MindSpore version 2.4.1 and \"te\" wheel package version 7.6 does not match. For details, refer to the installation guidelines: https://www.mindspore.cn/install\n",
-      "[WARNING] ME(250:281472944873504,MainProcess):2025-06-03-14:44:12.482.802 [mindspore/run_check/_check_version.py:354] MindSpore version 2.4.1 and \"hccl\" wheel package version 7.6 does not match. For details, refer to the installation guidelines: https://www.mindspore.cn/install\n",
-      "[WARNING] ME(250:281472944873504,MainProcess):2025-06-03-14:44:12.483.400 [mindspore/run_check/_check_version.py:368] Please pay attention to the above warning, countdown: 3\n",
-      "[WARNING] ME(250:281472944873504,MainProcess):2025-06-03-14:44:13.485.045 [mindspore/run_check/_check_version.py:368] Please pay attention to the above warning, countdown: 2\n",
-      "[WARNING] ME(250:281472944873504,MainProcess):2025-06-03-14:44:14.486.640 [mindspore/run_check/_check_version.py:368] Please pay attention to the above warning, countdown: 1\n",
-      "Building prefix dict from the default dictionary ...\n",
-      "Loading model from cache /tmp/jieba.cache\n",
-      "Loading model cost 1.075 seconds.\n",
-      "Prefix dict has been built successfully.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import os\n",
     "\n",
@@ -218,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
@@ -234,29 +89,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2500"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "imdb_train.get_dataset_size()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
@@ -291,21 +135,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy.\n",
-      "/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/mindnlp/transformers/tokenization_utils_base.py:1526: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted, and will be then set to `False` by default. \n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from mindnlp.transformers import OpenAIGPTTokenizer\n",
     "# tokenizer\n",
@@ -322,19 +156,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[WARNING] ME(250:281472944873504,MainProcess):2025-06-03-14:44:51.758.363 [mindspore/dataset/engine/datasets.py:2534] Dataset is shuffled before split.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# split train dataset into train and valid datasets\n",
     "imdb_train, imdb_val = imdb_train.split([0.7, 0.3])"
@@ -342,7 +168,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
@@ -355,39 +181,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Tensor(shape=[4, 512], dtype=Int64, value=\n",
-       " [[  500,   246,  1322 ... 40480, 40480, 40480],\n",
-       "  [ 1473,   980,   246 ... 40480, 40480, 40480],\n",
-       "  [39516,   498,   481 ... 40480, 40480, 40480],\n",
-       "  [  616,   544,   808 ... 40480, 40480, 40480]]),\n",
-       " Tensor(shape=[4, 512], dtype=Int64, value=\n",
-       " [[1, 1, 1 ... 0, 0, 0],\n",
-       "  [1, 1, 1 ... 0, 0, 0],\n",
-       "  [1, 1, 1 ... 0, 0, 0],\n",
-       "  [1, 1, 1 ... 0, 0, 0]]),\n",
-       " Tensor(shape=[4], dtype=Int32, value= [1, 1, 0, 0])]"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "next(dataset_train.create_tuple_iterator())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
@@ -407,41 +212,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "94d1b9d8276a4040a27030d34c8d44e2",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0.00/457M [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[MS_ALLOC_CONF]Runtime config:  enable_vmm:True  vmm_align_size:2MB\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Some weights of OpenAIGPTForSequenceClassification were not initialized from the model checkpoint at openai-gpt and are newly initialized: ['score.weight']\n",
-      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from mindnlp.transformers import OpenAIGPTForSequenceClassification\n",
     "from mindnlp.engine import TrainingArguments\n",
@@ -475,113 +250,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a14e623641474a878a018804439a22f6",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/1314 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "......{'loss': 0.5623, 'learning_rate': 1.69558599695586e-05, 'epoch': 0.46}\n",
-      ".{'loss': 0.5065, 'learning_rate': 1.39117199391172e-05, 'epoch': 0.91}\n",
-      "."
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/188 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'eval_loss': 0.38103795051574707, 'eval_accuracy': 0.8893333333333333, 'eval_runtime': 12.8526, 'eval_samples_per_second': 14.627, 'eval_steps_per_second': 14.627, 'epoch': 1.0}\n",
-      "{'loss': 0.3234, 'learning_rate': 1.08675799086758e-05, 'epoch': 1.37}\n",
-      "{'loss': 0.1771, 'learning_rate': 7.823439878234399e-06, 'epoch': 1.83}\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/188 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'eval_loss': 0.5746207237243652, 'eval_accuracy': 0.892, 'eval_runtime': 12.5869, 'eval_samples_per_second': 14.936, 'eval_steps_per_second': 14.936, 'epoch': 2.0}\n",
-      "{'loss': 0.1717, 'learning_rate': 4.779299847792998e-06, 'epoch': 2.28}\n",
-      "{'loss': 0.0905, 'learning_rate': 1.7351598173515982e-06, 'epoch': 2.74}\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/188 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'eval_loss': 0.5379959344863892, 'eval_accuracy': 0.9013333333333333, 'eval_runtime': 12.6645, 'eval_samples_per_second': 14.845, 'eval_steps_per_second': 14.845, 'epoch': 3.0}\n",
-      "{'train_runtime': 544.4598, 'train_samples_per_second': 9.654, 'train_steps_per_second': 2.413, 'train_loss': 0.28584540307612544, 'epoch': 3.0}\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "TrainOutput(global_step=1314, training_loss=0.28584540307612544, metrics={'train_runtime': 544.4598, 'train_samples_per_second': 9.654, 'train_steps_per_second': 2.413, 'train_loss': 0.28584540307612544, 'epoch': 3.0})"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# trainer.run(tgt_columns=\"labels\")\n",
     "trainer.train()"
@@ -589,7 +262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
@@ -616,7 +289,7 @@
     "\n",
     "            acc = compute_accuracy(logits, label)['accuracy']\n",
     "            epoch_acc += acc\n",
-    "            \n",
+    "\n",
     "            step_total += 1\n",
     "            acc=epoch_acc/step_total\n",
     "\n",
@@ -625,33 +298,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  0%|          | 0/188 [00:14<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy: 0.901595744680851\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "acc = evaluate_fn(model, dataset_val)\n",
     "print(f\"Accuracy: {acc}\")"
diff --git a/01.LLM_Theory_Course/01.Industry_Model_Introduction/01.Classic_Model_Technical_Analysis/03.GPT2/gpt2_modules.ipynb b/01.LLM_Theory_Course/01.Industry_Model_Introduction/01.Classic_Model_Technical_Analysis/03.GPT2/gpt2_modules.ipynb
index 9aa4b00..a210396 100644
--- a/01.LLM_Theory_Course/01.Industry_Model_Introduction/01.Classic_Model_Technical_Analysis/03.GPT2/gpt2_modules.ipynb
+++ b/01.LLM_Theory_Course/01.Industry_Model_Introduction/01.Classic_Model_Technical_Analysis/03.GPT2/gpt2_modules.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "b6ff08fc-4052-4e22-8aab-b3579544778f",
+   "id": "0",
    "metadata": {},
    "source": [
     "# GPT2 Masked Multi-head Self-attention详解"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6e69894f-46ce-4c18-85d0-8e5418ac70b2",
+   "id": "1",
    "metadata": {},
    "source": [
     "该实验可进行在线体验，在线体验链接（https://pangu.huaweicloud.com/gallery/asset-detail.html?id=6253fbfb-afe6-4727-bca5-5fc726541ab2\n",
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "67cdf1aa-38a0-4c00-af6e-b76b89ad35a3",
+   "id": "2",
    "metadata": {},
    "source": [
     "## 环境配置\n",
@@ -32,8 +32,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "e60ce5a7-b42a-4669-affa-99fb526e3c35",
+   "execution_count": null,
+   "id": "3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -44,8 +44,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "6e6f72a8-30cd-44ec-a884-4be46b6059bd",
+   "execution_count": null,
+   "id": "4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -76,7 +76,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5ab4c099-ead0-4fa9-8d1e-2eac85a43d9d",
+   "id": "5",
    "metadata": {},
    "source": [
     "***注：以上代码执行完成后，需点击左上角或右上角将kernel更换为python-3.9.0***"
@@ -84,7 +84,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c53f126c-a60f-4e08-8216-02dc63aa9a9b",
+   "id": "6",
    "metadata": {},
    "source": [
     "<div align=center><img src=\"https://mindspore-demo.obs.cn-north-4.myhuaweicloud.com/imgs/ai-gallery/change-kernel.PNG\"></div>"
@@ -92,7 +92,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d2fe8b0f-8ab4-4658-b64f-5eefb64a25ec",
+   "id": "7",
    "metadata": {},
    "source": [
     "2. 安装mindspore2.2.12、indNLP及相关依赖，MindNLP官方仓详见：MindNLP"
@@ -100,8 +100,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "52f84d8f-746d-4a87-af68-2dadf693f002",
+   "execution_count": null,
+   "id": "8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -114,7 +114,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d965c42b-d37a-42e4-aac9-4d25a4fb33be",
+   "id": "9",
    "metadata": {},
    "source": [
     "***注：执行如上命令完成安装后，请点击上方的restart kernel图标重启kernel，再进行实验***"
@@ -122,8 +122,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "074df3ae-4bfc-4655-be9f-8041fc211f96",
+   "execution_count": null,
+   "id": "10",
    "metadata": {
     "tags": []
    },
@@ -134,8 +134,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "9a161bfb-a15e-4a07-9bb0-688b76f87de3",
+   "execution_count": null,
+   "id": "11",
    "metadata": {
     "tags": []
    },
@@ -148,7 +148,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "37ab3a91-292b-420d-9b61-c85280dd8dee",
+   "id": "12",
    "metadata": {},
    "source": [
     "## GPT-2 Self-attention: 1- Creating queries, keys, and values"
@@ -156,7 +156,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "22664691-6db2-4d62-a76a-a4a8a6050199",
+   "id": "13",
    "metadata": {},
    "source": [
     "![gpt2-self-attention-3.png](https://jalammar.github.io/images/gpt2/gpt2-self-attention-3.png)"
@@ -164,8 +164,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "76d2591f-26c2-4961-bca3-be30c4352aef",
+   "execution_count": null,
+   "id": "14",
    "metadata": {
     "tags": []
    },
@@ -181,38 +181,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "2d315a4e-5663-404e-b93d-efb1cf354414",
+   "execution_count": null,
+   "id": "15",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/ma-user/anaconda3/envs/python-3.9.0/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "((1, 10, 768), (1, 10, 768), (1, 10, 768))"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from mindnlp._legacy.functional import split\n",
     "from mindnlp.transformers.ms_utils import Conv1D\n",
     "\n",
     "# query = Wq * X, key = Wk * X, value = Wv * X\n",
-    "# c_attn: (1, 10, 768*3) --> query, key, value: (1, 10, 768), (1, 10, 768), (1, 10, 768) \n",
+    "# c_attn: (1, 10, 768*3) --> query, key, value: (1, 10, 768), (1, 10, 768), (1, 10, 768)\n",
     "c_attn = Conv1D(3 * embed_dim, embed_dim)\n",
     "query, key, value = split(c_attn(x), embed_dim, axis=2)\n",
     "query.shape, key.shape, value.shape"
@@ -220,7 +200,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d2c7757e-16e4-4ff9-8a63-3e19767588db",
+   "id": "16",
    "metadata": {},
    "source": [
     "![gpt2-self-attention-split-attention-heads-1.png](https://jalammar.github.io/images/gpt2/gpt2-self-attention-split-attention-heads-1.png)\n",
@@ -230,8 +210,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "abb7ccac-7cfe-401a-ab32-763de70b4669",
+   "execution_count": null,
+   "id": "17",
    "metadata": {
     "tags": []
    },
@@ -245,28 +225,17 @@
     "    new_shape = tensor.shape[:-1] + (num_heads, attn_head_size)\n",
     "    tensor = tensor.view(new_shape)\n",
     "    # (batch_size, seq_len, num_heads, attn_head_size) --> (batch_size, num_heads, seq_len, attn_head_size)\n",
-    "    return ops.transpose(tensor, (0, 2, 1, 3))  "
+    "    return ops.transpose(tensor, (0, 2, 1, 3))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "72abe0fe-5225-425b-9bda-0723f3fb27cf",
+   "execution_count": null,
+   "id": "18",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "((1, 12, 10, 64), (1, 12, 10, 64), (1, 12, 10, 64))"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "num_heads = 12\n",
     "head_dim = embed_dim // num_heads\n",
@@ -281,7 +250,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fa0f65b2-b291-4ad1-b3ea-8e77e6a254d3",
+   "id": "19",
    "metadata": {},
    "source": [
     "## GPT-2 Self-attention: 2- Scoring\n",
@@ -293,23 +262,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "9f952236-de74-4419-9469-7e78d3b7c3e4",
+   "execution_count": null,
+   "id": "20",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1, 12, 10, 10)"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# qk点积\n",
     "# q: (1, 12, 10, 64), k^T: (1, 12, 64, 10)\n",
@@ -321,7 +279,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "501d6de9-cdb7-40cd-aed1-e4fe059054b5",
+   "id": "21",
    "metadata": {
     "tags": []
    },
@@ -331,30 +289,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "0ff22248-deff-4962-afae-55772f63f142",
+   "execution_count": null,
+   "id": "22",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Tensor(shape=[1, 1, 10, 10], dtype=Bool, value=\n",
-       "[[[[ True, False, False ... False, False, False],\n",
-       "   [ True,  True, False ... False, False, False],\n",
-       "   [ True,  True,  True ... False, False, False],\n",
-       "   ...\n",
-       "   [ True,  True,  True ...  True, False, False],\n",
-       "   [ True,  True,  True ...  True,  True, False],\n",
-       "   [ True,  True,  True ...  True,  True,  True]]]])"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# diagonal matrix to implement masked multi-head attention\n",
     "# To ensure not to attend to future information\n",
@@ -367,7 +307,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a783a2bc-01dd-4496-a018-ac01e643cd89",
+   "id": "23",
    "metadata": {},
    "source": [
     "![](https://jalammar.github.io/images/gpt2/queries-keys-attention-mask.png)\n",
@@ -377,8 +317,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "d957ce17-6df6-4f5e-a262-24ff3a8ce0d1",
+   "execution_count": null,
+   "id": "24",
    "metadata": {
     "tags": []
    },
@@ -395,58 +335,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "dee63bfd-f394-4558-9e9f-e102a2fd283c",
+   "execution_count": null,
+   "id": "25",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-3.4028235e+38"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "np.finfo(np.float32).min"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "d2faad14-9a3d-4495-8bcc-d7ac2695e83d",
+   "execution_count": null,
+   "id": "26",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Tensor(shape=[10, 10], dtype=Float32, value=\n",
-       "[[-3.72267663e-01, -3.40282347e+38, -3.40282347e+38 ... -3.40282347e+38, -3.40282347e+38, -3.40282347e+38],\n",
-       " [ 4.12474960e-01, -6.20999515e-01, -3.40282347e+38 ... -3.40282347e+38, -3.40282347e+38, -3.40282347e+38],\n",
-       " [ 1.29110947e-01,  2.28423685e-01, -1.90024704e-01 ... -3.40282347e+38, -3.40282347e+38, -3.40282347e+38],\n",
-       " ...\n",
-       " [ 2.14589074e-01,  1.79385528e-01,  2.11229175e-01 ... -8.21841732e-02, -3.40282347e+38, -3.40282347e+38],\n",
-       " [-3.86964470e-01,  1.50564313e-03, -7.81135634e-02 ... -8.60612690e-02, -3.31553906e-01, -3.40282347e+38],\n",
-       " [ 1.89703301e-01, -7.32186437e-02, -2.44263425e-01 ...  4.69686151e-01, -6.34481907e-01,  6.83065802e-02]])"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "attn_weights[0, 0]"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "54f61883-a535-4135-851c-c41e9c227e18",
+   "id": "27",
    "metadata": {},
    "source": [
     "![](https://jalammar.github.io/images/gpt2/transformer-attention-masked-scores-softmax.png)"
@@ -454,23 +365,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "df9cdaae-ac5a-4bc0-9e59-403d176c0d3b",
+   "execution_count": null,
+   "id": "28",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1, 12, 10, 10)"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "attn_weights = softmax(attn_weights, axis=-1)\n",
     "attn_weights.shape"
@@ -478,37 +378,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "5771f68c-8b35-4b1a-83d1-287b2ce7a47e",
+   "execution_count": null,
+   "id": "29",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Tensor(shape=[10, 10], dtype=Float32, value=\n",
-       "[[ 1.00000000e+00,  0.00000000e+00,  0.00000000e+00 ...  0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
-       " [ 7.37588942e-01,  2.62411058e-01,  0.00000000e+00 ...  0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
-       " [ 3.53208542e-01,  3.90087605e-01,  2.56703824e-01 ...  0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
-       " ...\n",
-       " [ 1.25348046e-01,  1.21012121e-01,  1.24927595e-01 ...  9.31602344e-02,  0.00000000e+00,  0.00000000e+00],\n",
-       " [ 8.72338116e-02,  1.28645703e-01,  1.18800178e-01 ...  1.17859736e-01,  9.22039151e-02,  0.00000000e+00],\n",
-       " [ 1.08949542e-01,  8.37606117e-02,  7.05920979e-02 ...  1.44151926e-01,  4.77844179e-02,  9.64947045e-02]])"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "attn_weights[0, 0]"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "4a376e6b-0cd8-434a-aa5b-c647251200fa",
+   "id": "30",
    "metadata": {},
    "source": [
     "![](https://jalammar.github.io/images/gpt2/gpt2-self-attention-multihead-sum-1.png)"
@@ -516,23 +398,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "0ba1e0ff-5627-4b70-8911-4ffa7383e29d",
+   "execution_count": null,
+   "id": "31",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1, 12, 10, 64)"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "attn_output = ops.matmul(attn_weights, value)\n",
     "\n",
@@ -541,7 +412,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5952ef91-b1e1-4d5b-9b42-a29f56f8f430",
+   "id": "32",
    "metadata": {},
    "source": [
     "## GPT-2 Self-attention: 3.5- Merge attention heads\n",
@@ -551,8 +422,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "80e44dd1-4013-4d01-b267-92463b296e5b",
+   "execution_count": null,
+   "id": "33",
    "metadata": {
     "tags": []
    },
@@ -570,23 +441,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "5b35f8ee-70b4-4cb4-ad9b-d0b685482b59",
+   "execution_count": null,
+   "id": "34",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1, 10, 768)"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# (1, 12, 10, 64) --> (1, 10, 12, 64) --> (1, 10, 768)\n",
     "attn_output = merge_heads(attn_output, num_heads, head_dim)\n",
@@ -596,7 +456,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "de14b271-4432-44a0-b1f9-d2632ed2cd5b",
+   "id": "35",
    "metadata": {},
    "source": [
     "## GPT-2 Self-attention: 4- Projecting\n",
@@ -606,8 +466,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "ff788df6-a6a7-4b43-9a76-95eaef4918c7",
+   "execution_count": null,
+   "id": "36",
    "metadata": {
     "tags": []
    },
@@ -618,23 +478,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "id": "0c7d4c1f-4ddc-4605-acba-f6e17cbfe2d5",
+   "execution_count": null,
+   "id": "37",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1, 10, 768)"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "attn_output = c_proj(attn_output)\n",
     "attn_output.shape"
@@ -643,7 +492,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9300497c-e27a-4fad-b02e-fe2b6a38aec2",
+   "id": "38",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/01.LLM_Theory_Course/01.Industry_Model_Introduction/01.Classic_Model_Technical_Analysis/03.GPT2/gpt2_summarization.ipynb b/01.LLM_Theory_Course/01.Industry_Model_Introduction/01.Classic_Model_Technical_Analysis/03.GPT2/gpt2_summarization.ipynb
index 7daef40..c56a189 100644
--- a/01.LLM_Theory_Course/01.Industry_Model_Introduction/01.Classic_Model_Technical_Analysis/03.GPT2/gpt2_summarization.ipynb
+++ b/01.LLM_Theory_Course/01.Industry_Model_Introduction/01.Classic_Model_Technical_Analysis/03.GPT2/gpt2_summarization.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "cb430c0e-fd70-46e2-91e2-b14cf782bd06",
+   "id": "0",
    "metadata": {},
    "source": [
     "# 基于MindSpore的GPT2文本摘要\n",
@@ -14,8 +14,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "f9f085c7-b2b3-4b18-95f9-13b298b10d58",
+   "execution_count": null,
+   "id": "1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -27,7 +27,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d9436813-c813-425e-b1ed-af0534ef862c",
+   "id": "2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -58,7 +58,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0bdfae31-df62-4746-bd3e-1c556413ffd2",
+   "id": "3",
    "metadata": {},
    "source": [
     "***注：以上代码执行完成后，需点击左上角或右上角将kernel更换为python-3.9.0***"
@@ -66,7 +66,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "757cb29c-48f4-4b6b-9b71-6c008e629eb8",
+   "id": "4",
    "metadata": {},
    "source": [
     "2. 安装mindspore2.2.12，安装指南详见：[MindSpore安装](https://www.mindspore.cn/install)\n",
@@ -75,112 +75,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "id": "6491560b-1ec6-4ca1-88cc-c7f9c1297725",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Looking in indexes: http://pip.modelarts.private.com:8888/repository/pypi/simple\n",
-      "Processing ./mindnlp-0.4.1-py3-none-any.whl\n",
-      "Requirement already satisfied: addict in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (2.4.0)\n",
-      "Requirement already satisfied: pytest==7.2.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (7.2.0)\n",
-      "Requirement already satisfied: tqdm in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (4.66.4)\n",
-      "Requirement already satisfied: regex in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (2024.7.24)\n",
-      "Requirement already satisfied: evaluate in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (0.4.3)\n",
-      "Requirement already satisfied: pyctcdecode in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (0.5.0)\n",
-      "Requirement already satisfied: ml-dtypes in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (0.4.0)\n",
-      "Requirement already satisfied: sentencepiece in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (0.2.0)\n",
-      "Requirement already satisfied: tokenizers==0.19.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (0.19.1)\n",
-      "Requirement already satisfied: datasets in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (3.1.0)\n",
-      "Requirement already satisfied: safetensors in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (0.4.5)\n",
-      "Requirement already satisfied: pillow>=10.0.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (10.0.1)\n",
-      "Requirement already satisfied: mindspore>=2.2.14 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (2.4.0)\n",
-      "Requirement already satisfied: requests in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (2.32.3)\n",
-      "Requirement already satisfied: exceptiongroup>=1.0.0rc8 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.1) (1.2.2)\n",
-      "Requirement already satisfied: pluggy<2.0,>=0.12 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.1) (1.5.0)\n",
-      "Requirement already satisfied: attrs>=19.2.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.1) (23.2.0)\n",
-      "Requirement already satisfied: packaging in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.1) (24.1)\n",
-      "Requirement already satisfied: iniconfig in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.1) (2.0.0)\n",
-      "Requirement already satisfied: tomli>=1.0.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.1) (2.0.1)\n",
-      "Requirement already satisfied: huggingface-hub<1.0,>=0.16.4 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from tokenizers==0.19.1->mindnlp==0.4.1) (0.24.2)\n",
-      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.1) (4.12.2)\n",
-      "Requirement already satisfied: fsspec>=2023.5.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.1) (2024.6.1)\n",
-      "Requirement already satisfied: filelock in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.1) (3.15.4)\n",
-      "Requirement already satisfied: pyyaml>=5.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.1) (6.0.1)\n",
-      "Requirement already satisfied: psutil>=5.6.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.1) (5.9.5)\n",
-      "Requirement already satisfied: numpy<2.0.0,>=1.20.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.1) (1.22.0)\n",
-      "Requirement already satisfied: astunparse>=1.6.3 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.1) (1.6.3)\n",
-      "Requirement already satisfied: asttokens>=2.0.4 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.1) (2.4.1)\n",
-      "Requirement already satisfied: scipy>=1.5.4 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.1) (1.10.1)\n",
-      "Requirement already satisfied: protobuf>=3.13.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.1) (3.20.2)\n",
-      "Requirement already satisfied: six>=1.12.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from asttokens>=2.0.4->mindspore>=2.2.14->mindnlp==0.4.1) (1.16.0)\n",
-      "Requirement already satisfied: wheel<1.0,>=0.23.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from astunparse>=1.6.3->mindspore>=2.2.14->mindnlp==0.4.1) (0.38.4)\n",
-      "Requirement already satisfied: pandas in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from datasets->mindnlp==0.4.1) (1.3.5)\n",
-      "Requirement already satisfied: multiprocess<0.70.17 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from datasets->mindnlp==0.4.1) (0.70.16)\n",
-      "Requirement already satisfied: dill<0.3.9,>=0.3.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from datasets->mindnlp==0.4.1) (0.3.8)\n",
-      "Requirement already satisfied: aiohttp in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from datasets->mindnlp==0.4.1) (3.11.9)\n",
-      "Requirement already satisfied: pyarrow>=15.0.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from datasets->mindnlp==0.4.1) (18.1.0)\n",
-      "Requirement already satisfied: xxhash in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from datasets->mindnlp==0.4.1) (3.5.0)\n",
-      "Requirement already satisfied: async-timeout<6.0,>=4.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.1) (5.0.1)\n",
-      "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.1) (2.4.4)\n",
-      "Requirement already satisfied: multidict<7.0,>=4.5 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.1) (6.1.0)\n",
-      "Requirement already satisfied: aiosignal>=1.1.2 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.1) (1.3.1)\n",
-      "Requirement already satisfied: yarl<2.0,>=1.17.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.1) (1.18.3)\n",
-      "Requirement already satisfied: propcache>=0.2.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.1) (0.2.1)\n",
-      "Requirement already satisfied: frozenlist>=1.1.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.1) (1.5.0)\n",
-      "Requirement already satisfied: charset-normalizer<4,>=2 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from requests->mindnlp==0.4.1) (2.0.12)\n",
-      "Requirement already satisfied: certifi>=2017.4.17 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from requests->mindnlp==0.4.1) (2024.7.4)\n",
-      "Requirement already satisfied: urllib3<3,>=1.21.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from requests->mindnlp==0.4.1) (1.26.7)\n",
-      "Requirement already satisfied: idna<4,>=2.5 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from requests->mindnlp==0.4.1) (2.10)\n",
-      "Requirement already satisfied: pytz>=2017.3 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pandas->datasets->mindnlp==0.4.1) (2024.1)\n",
-      "Requirement already satisfied: python-dateutil>=2.7.3 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pandas->datasets->mindnlp==0.4.1) (2.9.0.post0)\n",
-      "Requirement already satisfied: hypothesis<7,>=6.14 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pyctcdecode->mindnlp==0.4.1) (6.122.1)\n",
-      "Requirement already satisfied: pygtrie<3.0,>=2.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pyctcdecode->mindnlp==0.4.1) (2.5.0)\n",
-      "Requirement already satisfied: sortedcontainers<3.0.0,>=2.1.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from hypothesis<7,>=6.14->pyctcdecode->mindnlp==0.4.1) (2.4.0)\n",
-      "mindnlp is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\n",
-      "\u001b[33mWARNING: You are using pip version 21.0.1; however, version 24.3.1 is available.\n",
-      "You should consider upgrading via the '/home/ma-user/anaconda3/envs/MindSpore/bin/python3.9 -m pip install --upgrade pip' command.\u001b[0m\n",
-      "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
-      "Requirement already satisfied: tokenizers==0.19.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (0.19.1)\n",
-      "Requirement already satisfied: huggingface-hub<1.0,>=0.16.4 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from tokenizers==0.19.1) (0.24.2)\n",
-      "Requirement already satisfied: packaging>=20.9 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1) (24.1)\n",
-      "Requirement already satisfied: fsspec>=2023.5.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1) (2024.6.1)\n",
-      "Requirement already satisfied: pyyaml>=5.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1) (6.0.1)\n",
-      "Requirement already satisfied: tqdm>=4.42.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1) (4.66.4)\n",
-      "Requirement already satisfied: requests in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1) (2.32.3)\n",
-      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1) (4.12.2)\n",
-      "Requirement already satisfied: filelock in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1) (3.15.4)\n",
-      "Requirement already satisfied: charset-normalizer<4,>=2 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from requests->huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1) (2.0.12)\n",
-      "Requirement already satisfied: idna<4,>=2.5 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from requests->huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1) (2.10)\n",
-      "Requirement already satisfied: certifi>=2017.4.17 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from requests->huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1) (2024.7.4)\n",
-      "Requirement already satisfied: urllib3<3,>=1.21.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from requests->huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1) (1.26.7)\n",
-      "\u001b[33mWARNING: You are using pip version 21.0.1; however, version 24.3.1 is available.\n",
-      "You should consider upgrading via the '/home/ma-user/anaconda3/envs/MindSpore/bin/python3.9 -m pip install --upgrade pip' command.\u001b[0m\n",
-      "env: no_proxy='a.test.com,127.0.0.1,2.2.2.2'\n",
-      "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
-      "Collecting mindspore==2.4.0\n",
-      "  Using cached https://ms-release.obs.cn-north-4.myhuaweicloud.com/2.4.0/MindSpore/unified/aarch64/mindspore-2.4.0-cp39-cp39-linux_aarch64.whl (333.7 MB)\n",
-      "Requirement already satisfied: protobuf>=3.13.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.0) (3.20.2)\n",
-      "Requirement already satisfied: psutil>=5.6.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.0) (5.9.5)\n",
-      "Requirement already satisfied: asttokens>=2.0.4 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.0) (2.4.1)\n",
-      "Requirement already satisfied: packaging>=20.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.0) (24.1)\n",
-      "Requirement already satisfied: numpy<2.0.0,>=1.20.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.0) (1.22.0)\n",
-      "Requirement already satisfied: safetensors>=0.4.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.0) (0.4.5)\n",
-      "Requirement already satisfied: scipy>=1.5.4 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.0) (1.10.1)\n",
-      "Requirement already satisfied: pillow>=6.2.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.0) (10.0.1)\n",
-      "Requirement already satisfied: astunparse>=1.6.3 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.0) (1.6.3)\n",
-      "Requirement already satisfied: six>=1.12.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from asttokens>=2.0.4->mindspore==2.4.0) (1.16.0)\n",
-      "Requirement already satisfied: wheel<1.0,>=0.23.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from astunparse>=1.6.3->mindspore==2.4.0) (0.38.4)\n",
-      "\u001b[33mWARNING: You are using pip version 21.0.1; however, version 24.3.1 is available.\n",
-      "You should consider upgrading via the '/home/ma-user/anaconda3/envs/MindSpore/bin/python -m pip install --upgrade pip' command.\u001b[0m\n",
-      "Note: you may need to restart the kernel to use updated packages.\n",
-      "\u001b[33mWARNING: Skipping mindformers as it is not installed.\u001b[0m\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "id": "5",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "!pip install mindnlp-0.4.1-py3-none-any.whl  # 将安装mindnlp版本更换为mindnlp-0.4.0-py3-none-any.whl（daily版本）\n",
     "!pip install tokenizers==0.19.1 -i https://pypi.tuna.tsinghua.edu.cn/simple  # 修改tokenizers版本为0.19.1\n",
@@ -191,7 +89,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0faae5c6-8397-4574-9e9c-f86230bb071a",
+   "id": "6",
    "metadata": {},
    "source": [
     "***注：执行如上命令完成安装后，请点击上方的restart kernel图标重启kernel，再进行实验***"
@@ -199,7 +97,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bb699e4a-a2dc-44f2-b3cb-6b86fa9b24f6",
+   "id": "7",
    "metadata": {},
    "source": [
     "### 数据集加载与处理\n",
@@ -211,39 +109,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "27bf5931-7b09-4984-841c-fbea311d3955",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[WARNING] GE_ADPT(12319,ffffaba360b0,python):2024-12-03-21:03:05.821.904 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclmdlBundleGetModelId failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclmdlBundleGetModelId\n",
-      "[WARNING] GE_ADPT(12319,ffffaba360b0,python):2024-12-03-21:03:05.821.956 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclmdlBundleLoadFromMem failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclmdlBundleLoadFromMem\n",
-      "[WARNING] GE_ADPT(12319,ffffaba360b0,python):2024-12-03-21:03:05.821.975 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclmdlBundleUnload failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclmdlBundleUnload\n",
-      "[WARNING] GE_ADPT(12319,ffffaba360b0,python):2024-12-03-21:03:05.822.162 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclrtGetMemUceInfo failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclrtGetMemUceInfo\n",
-      "[WARNING] GE_ADPT(12319,ffffaba360b0,python):2024-12-03-21:03:05.822.179 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclrtDeviceTaskAbort failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclrtDeviceTaskAbort\n",
-      "[WARNING] GE_ADPT(12319,ffffaba360b0,python):2024-12-03-21:03:05.822.193 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclrtMemUceRepair failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclrtMemUceRepair\n",
-      "[WARNING] GE_ADPT(12319,ffffaba360b0,python):2024-12-03-21:03:05.823.653 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol acltdtCleanChannel failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libacl_tdt_channel.so: undefined symbol: acltdtCleanChannel\n",
-      "[WARNING] ME(12319:281473561354416,MainProcess):2024-12-03-21:03:05.955.541 [mindspore/run_check/_check_version.py:396] Can not find the tbe operator implementation(need by mindspore-ascend). Please check whether the Environment Variable PYTHONPATH is set. For details, refer to the installation guidelines: https://www.mindspore.cn/install\n",
-      "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/numpy/core/getlimits.py:499: UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.\n",
-      "  setattr(self, word, getattr(machar, word).flat[0])\n",
-      "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.\n",
-      "  return self._float_to_str(self.smallest_subnormal)\n",
-      "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/numpy/core/getlimits.py:499: UserWarning: The value of the smallest subnormal for <class 'numpy.float32'> type is zero.\n",
-      "  setattr(self, word, getattr(machar, word).flat[0])\n",
-      "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for <class 'numpy.float32'> type is zero.\n",
-      "  return self._float_to_str(self.smallest_subnormal)\n",
-      "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n",
-      "Building prefix dict from the default dictionary ...\n",
-      "Loading model from cache /tmp/jieba.cache\n",
-      "Loading model cost 1.298 seconds.\n",
-      "Prefix dict has been built successfully.\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "id": "8",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from mindnlp.utils import http_get\n",
     "\n",
@@ -254,21 +123,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "9b5868b6-7a52-4f97-b934-4d3632a978a2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "50000"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "9",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from mindspore.dataset import TextFileDataset\n",
     "\n",
@@ -279,7 +137,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f2697c68-be45-44c1-bc26-7c04424ebf0c",
+   "id": "10",
    "metadata": {},
    "source": [
     "**本案例默认在GPU P100上运行，因中文文本，tokenizer使用的是bert tokenizer而非gpt tokenizer等原因，全量数据训练1个epoch的时间约为80分钟。**\n",
@@ -289,8 +147,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "9bf79231-864c-4e11-9409-995c95cdb30f",
+   "execution_count": null,
+   "id": "11",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -301,7 +159,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a1f0d574-53a0-4bac-9303-6eb769418c04",
+   "id": "12",
    "metadata": {},
    "source": [
     "2. 数据预处理\n",
@@ -320,8 +178,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "f1ee1961-0658-4e70-95c2-81fefd83a40b",
+   "execution_count": null,
+   "id": "13",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -340,7 +198,7 @@
     "        tokenized = tokenizer(text=article, text_pair=summary,\n",
     "                              padding='max_length', truncation='only_first', max_length=max_seq_len)\n",
     "        return tokenized['input_ids'], tokenized['input_ids']\n",
-    "    \n",
+    "\n",
     "    dataset = dataset.map(read_map, 'text', ['article', 'summary'])\n",
     "    # change column names to input_ids and labels for the following training\n",
     "    dataset = dataset.map(merge_and_pad, ['article', 'summary'], ['input_ids', 'labels'])\n",
@@ -354,7 +212,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e0ce3dab-9486-4365-be7c-34bd5a761080",
+   "id": "14",
    "metadata": {},
    "source": [
     "因GPT2无中文的tokenizer，我们使用BertTokenizer替代。"
@@ -362,29 +220,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "e3cd8e57-72bc-4d2e-b38d-38b24efadd49",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/mindnlp/transformers/tokenization_utils_base.py:1526: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted, and will be then set to `False` by default. \n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "21128"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "15",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from mindnlp.transformers import BertTokenizer\n",
     "\n",
@@ -395,8 +234,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "0e89c26b-4970-449a-a0c4-b6c61845e336",
+   "execution_count": null,
+   "id": "16",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -405,31 +244,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "1b65cc13-0a52-4bae-ab5f-ebb813a4d3ab",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Tensor(shape=[1, 1024], dtype=Int64, value=\n",
-       " [[ 101, 1724, 3862 ...    0,    0,    0]]),\n",
-       " Tensor(shape=[1, 1024], dtype=Int64, value=\n",
-       " [[ 101, 1724, 3862 ...    0,    0,    0]])]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "17",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "next(train_dataset.create_tuple_iterator())"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "7e1497ee-2ad1-4da8-b659-9c7f2d45fccc",
+   "id": "18",
    "metadata": {},
    "source": [
     "### 模型构建\n",
@@ -439,8 +264,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "2f295944-ea2e-41e1-8301-472e09223792",
+   "execution_count": null,
+   "id": "19",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -481,7 +306,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0f6af843-64d7-49a3-875f-605d6b2e74b2",
+   "id": "20",
    "metadata": {},
    "source": [
     "2. 动态学习率"
@@ -489,8 +314,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "73c7be3d-44dc-49d4-abd8-c41f316a28d9",
+   "execution_count": null,
+   "id": "21",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -519,7 +344,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c45e9db2-11df-4cc4-8bef-87d473d99e5a",
+   "id": "22",
    "metadata": {},
    "source": [
     "### 模型训练"
@@ -527,8 +352,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "1a655320-2d05-4c93-bc8b-f1b4f45f809f",
+   "execution_count": null,
+   "id": "23",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -541,21 +366,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "81ac9003-2dcf-42f8-b42d-3a788f172d98",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "GPT2LMHeadModel has generative capabilities, as `prepare_inputs_for_generation` is explicitly overwritten. However, it doesn't directly inherit from `GenerationMixin`.`PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.\n",
-      "  - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).\n",
-      "  - If you are not the owner of the model architecture class, please contact the model code owner to update it.\n",
-      "[WARNING] DEVICE(12319,ffffaba360b0,python):2024-12-03-21:03:34.197.849 [mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_vmm_adapter.h:188] CheckVmmDriverVersion] Driver version is less than 24.0.0, vmm is disabled by default, drvier_version: 23.0.6\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "id": "24",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from mindspore import nn\n",
     "from mindnlp.transformers import GPT2Config, GPT2LMHeadModel\n",
@@ -570,18 +384,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "2803c71c-3591-48cf-a6a9-6b840af749bf",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "number of model parameters: 102068736\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "id": "25",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# 记录模型参数数量\n",
     "print('number of model parameters: {}'.format(model.num_parameters()))"
@@ -589,8 +395,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "1492649c-dfdb-4cfd-85bb-aef478aff5d2",
+   "execution_count": null,
+   "id": "26",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -608,8 +414,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "88259c93-5366-4406-a417-396808ec767c",
+   "execution_count": null,
+   "id": "27",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -624,7 +430,7 @@
     "    learning_rate=learning_rate,\n",
     "    max_grad_norm=max_grad_norm,\n",
     "    warmup_steps=warmup_steps\n",
-    "    \n",
+    "\n",
     ")\n",
     "\n",
     "from mindnlp.engine import Trainer\n",
@@ -638,56 +444,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "ebf47838-460a-49e4-8850-f10fe7b5ff2b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  0%|          | 0/45 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "-\r"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 45/45 [00:32<00:00,  1.38it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'train_runtime': 32.5297, 'train_samples_per_second': 11.067, 'train_steps_per_second': 1.383, 'train_loss': 9.11246066623264, 'epoch': 1.0}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "TrainOutput(global_step=45, training_loss=9.11246066623264, metrics={'train_runtime': 32.5297, 'train_samples_per_second': 11.067, 'train_steps_per_second': 1.383, 'train_loss': 9.11246066623264, 'epoch': 1.0})"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "28",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# 修改部分代码\n",
     "# trainer.run(tgt_columns=\"labels\")\n",
@@ -696,8 +456,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "787795ec-0c07-4be6-97b7-4defbe899117",
+   "execution_count": null,
+   "id": "29",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -712,7 +472,7 @@
     "\n",
     "    dataset = dataset.map(read_map, 'text', ['article', 'summary'])\n",
     "    dataset = dataset.map(pad, 'article', ['input_ids'])\n",
-    "    \n",
+    "\n",
     "    dataset = dataset.batch(batch_size)\n",
     "\n",
     "    return dataset"
@@ -720,8 +480,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "996842c4-f793-4393-ae64-2d4b065bc9f2",
+   "execution_count": null,
+   "id": "30",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -730,75 +490,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "10421e6d-ec81-435d-9944-f1e35cd3eae9",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[array([[ 101, 4373, 3360, 3173, 7319, 5381,  118, 4373, 3360, 3241, 2845,\n",
-      "        6380, 8020, 6858, 6380, 1447,  133,  100,  135, 7942,  837, 2412,\n",
-      "        8021, 5298, 6756, 7313, 7395, 8024, 1355, 4385, 6378, 5298, 1767,\n",
-      "        7353, 6818,  671, 3418, 4510, 5296, 3327,  677, 3300,  671,  702,\n",
-      "        7881, 4973, 8024, 8128, 2259, 4638, 1144, 3378, 4989, 1315, 4260,\n",
-      "         677, 1343, 2929, 7881, 8024,  679, 2682, 2218, 3634, 1462,  700,\n",
-      "        7942, 3787,  511, 3189, 1184, 1355, 4495, 1762, 1300, 4635, 1344,\n",
-      "        3152, 1765, 7252,  671, 7730, 3413, 6378, 5298, 1767, 4638, 2692,\n",
-      "        1912,  752, 3125, 8024,  808,  782, 1537, 1656,  511,  791, 2399,\n",
-      "        8128, 2259, 4638, 1144, 3378, 3221, 1300, 4635, 1344, 2123, 4059,\n",
-      "        7252, 3173, 5783, 3333,  782, 8024,  679,  719, 1184, 1168, 1071,\n",
-      "        1828, 1356, 1144, 3378, 3378,  818, 3136, 5298, 4638, 2123, 4059,\n",
-      "        7252, 3378, 7730, 3413, 1346, 1217, 3749, 6756, 7730, 7724, 1447,\n",
-      "        1824, 6378,  511, 8132, 3189,  704, 1286, 8024, 1144, 3378, 7390,\n",
-      "        1828, 1356, 1144, 3378, 3378, 1350, 1369, 1912, 1126, 1399, 2110,\n",
-      "        1447, 2458, 6756, 1168, 3152, 1765, 7252, 8024,  955, 4500, 3152,\n",
-      "        1765, 7252, 7160, 4635, 5106, 1322, 7353, 6818, 4638, 6378, 5298,\n",
-      "        1767, 5298, 6756,  511, 8122, 3198, 6387, 8024, 6378, 5298, 7313,\n",
-      "        7395, 8024, 1144, 3378, 1355, 4385, 6378, 5298, 1767, 7353, 6818,\n",
-      "         671, 3418, 4510, 5296, 3327,  677, 3300,  671,  702, 7881, 4973,\n",
-      "        8024,  671, 1372,  100, 1061, 1520,  100, 3633, 1388, 4708, 6001,\n",
-      "        2094, 7607, 1726, 7881, 4973,  511, 2398, 3198, 1144, 3378,  981,\n",
-      "        2209,  833, 2936, 7881, 1139, 1297, 8024, 4761, 6887, 6821, 4905,\n",
-      "        7881,  817,  966, 8135, 1914, 1039,  511,  800, 6656, 6716, 6804,\n",
-      "        2110, 1447, 2802,  749,  702, 2875, 1461, 8024,  912, 7607, 1944,\n",
-      "        6814, 1343, 8024, 3617, 4260,  677, 4510, 5296, 3327, 2929, 7881,\n",
-      "         511, 1144, 3378, 3378, 1355, 4385, 1400, 6841, 6814, 1343, 3617,\n",
-      "        7349, 3632, 8024,  852,  711, 3198, 2347, 3241, 8024, 5023, 1144,\n",
-      "        3378, 3378, 6628, 1168, 3198, 8024, 1144, 3378, 2347, 4260, 1168,\n",
-      "        4510, 5296, 3327, 7553, 8024,  847, 2797, 2929, 7881, 3198,  679,\n",
-      "        2708, 6239, 4821, 1168, 1928, 7553, 4638, 7770, 1327, 4510, 5296,\n",
-      "        8024, 2496, 1315, 6716,  767,  511, 1071, 2797, 2958, 6158, 1912,\n",
-      "        7463, 4638, 7167, 3363, 1173, 4959, 8024, 2221,  860, 2647, 2899,\n",
-      "        1762, 4510, 5296, 3327,  677,  511, 8153, 3189,  678, 1286, 8024,\n",
-      "        1762, 3152, 1765,  510, 2123, 4059, 7252, 3124, 2424, 1350, 4685,\n",
-      "        1068, 6956, 7305, 1291, 6444,  678, 8024, 4685, 1068, 6569,  818,\n",
-      "        3175,  680, 3647, 5442, 2157, 2247, 6809, 2768, 6608,  985, 1291,\n",
-      "        6379, 8024, 3647, 5442, 2157, 2247, 1398, 2692, 2199, 3647, 5442,\n",
-      "        2221,  860, 1357,  678, 3021, 6624, 8024, 4685, 1068, 6569,  818,\n",
-      "        3175, 1066, 6608,  985, 3647, 5442, 2157, 2247, 8115,  119,  129,\n",
-      "         674, 1039, 8024, 1071,  704, 2123, 4059, 7252, 3378, 7730, 3413,\n",
-      "        6608,  802,  128,  119,  124,  674, 1039, 8024, 3647, 5442, 1828,\n",
-      "        1356, 1144, 3378, 3378, 8020, 7730, 3413, 3136, 5298, 8021, 6608,\n",
-      "         802,  126,  119,  124,  674, 1039, 8024, 3152, 1765,  897, 4510,\n",
-      "        2792, 3315, 3341, 3766, 3300, 4684, 2970, 6569,  818, 8024,  852,\n",
-      "        1139,  754,  782, 6887,  712,  721, 6608,  802,  124,  119,  123,\n",
-      "         674, 1039,  511, 8020, 1333, 3403, 7579, 8038,  711, 2929,  671,\n",
-      "        1372, 7881,  133,  100,  135, 6608,  677,  671, 3340, 1462, 1300,\n",
-      "        4635,  671, 4511, 2094, 4260, 4510, 5296, 3327, 2929, 7881, 8024,\n",
-      "         679, 2708, 6239, 4510, 6716,  767, 8021,  102]], dtype=int64), array(['玉林21岁小伙驾校培训期间爬上电线杆抓鸟，触到高压电线触电身亡；相关责任方赔偿家属15.8万元(图)'], dtype='<U50')]\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "id": "31",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "print(next(batched_test_dataset.create_tuple_iterator(output_numpy=True)))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "id": "1a5288a4-f584-479b-9653-bda060e00279",
+   "execution_count": null,
+   "id": "32",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -809,7 +512,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1631f82e-857d-413a-a298-ff6f66d4a286",
+   "id": "33",
    "metadata": {},
    "source": [
     "由于训练数据量少，epochs数少且tokenizer并未使用gpt tokenizer等因素，模型推理效果会较差。"
@@ -817,27 +520,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "id": "090be0ea-3467-4eca-abb6-129d451f58ba",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
-      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
-      "The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[CLS] 玉 林 新 闻 网 - 玉 林 晚 报 讯 （ 通 讯 员 < [UNK] > 黄 传 庆 ） 练 车 间 隙 ， 发 现 训 练 场 附 近 一 根 电 线 杆 上 有 一 个 鸟 窝 ， 21 岁 的 刁 某 立 即 爬 上 去 捉 鸟 ， 不 想 就 此 命 丧 黄 泉 。 日 前 发 生 在 博 白 县 文 地 镇 一 驾 校 训 练 场 的 意 外 事 故 ， 令 人 唏 嘘 。 今 年 21 岁 的 刁 某 是 博 白 县 宁 潭 镇 新 荣 村 人 ， 不 久 前 到 其 堂 叔 刁 某 某 任 教 练 的 宁 潭 镇 某 驾 校 参 加 汽 车 驾 驶 员 培 训 。 25 日 中 午 ， 刁 某 随 堂 叔 刁 某 某 及 另 外 几 名 学 员 开 车 到 文 地 镇 ， 借 用 文 地 镇 钛 白 粉 厂 附 近 的 训 练 场 练 车 。 14 时 许 ， 训 练 间 隙 ， 刁 某 发 现 训 练 场 附 近 一 根 电 线 杆 上 有 一 个 鸟 窝 ， 一 只 [UNK] 八 哥 [UNK] 正 叼 着 虫 子 飞 回 鸟 窝 。 平 时 刁 某 偶 尔 会 捕 鸟 出 卖 ， 知 道 这 种 鸟 价 值 100 多 元 。 他 跟 身 边 学 员 打 了 个 招 呼 ， 便 飞 奔 过 去 ， 欲 爬 上 电 线 杆 捉 鸟 。 刁 某 某 发 现 后 追 过 去 欲 阻 止 ， 但 为 时 已 晚 ， 等 刁 某 某 赶 到 时 ， 刁 某 已 爬 到 电 线 杆 顶 ， 伸 手 捉 鸟 时 不 慎 触 碰 到 头 顶 的 高 压 电 线 ， 当 即 身 亡 。 其 手 掌 被 外 露 的 钢 枝 刺 穿 ， 尸 体 悬 挂 在 电 线 杆 上 。 26 日 下 午 ， 在 文 地 、 宁 潭 镇 政 府 及 相 关 部 门 协 调 下 ， 相 关 责 任 方 与 死 者 家 属 达 成 赔 偿 协 议 ， 死 者 家 属 同 意 将 死 者 尸 体 取 下 搬 走 ， 相 关 责 任 方 共 赔 偿 死 者 家 属 15. 8 万 元 ， 其 中 宁 潭 镇 某 驾 校 赔 付 7. 3 万 元 ， 死 者 堂 叔 刁 某 某 （ 驾 校 教 练 ） 赔 付 5. 3 万 元 ， 文 地 供 电 所 本 来 没 有 直 接 责 任 ， 但 出 于 人 道 主 义 赔 付 3. 2 万 元 。 （ 原 标 题 ： 为 捉 一 只 鸟 < [UNK] > 赔 上 一 条 命 博 白 一 男 子 爬 电 线 杆 捉 鸟 ， 不 慎 触 电 身 亡 ） [SEP] ， 。 ， ， 的 ， [UNK] 的 的 。 。 [UNK] ， 了 ， 大 的 了 。 的 [UNK] 。 一 的 一 ， 出 ， 上 ， 人 的 大 ， 和 ， 子 ， 到 ， 市 ， 有 ， 行 ， 也 ， < ，\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "id": "34",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "model.set_train(False)\n",
     "model.config.eos_token_id = model.config.sep_token_id\n",
@@ -854,7 +540,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8654a5d5-d94b-4906-92bf-52d2d85685a7",
+   "id": "35",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -862,7 +548,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9613743c-3655-45e2-a720-c311a5854c94",
+   "id": "36",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/01.LLM_Theory_Course/01.Industry_Model_Introduction/01.Classic_Model_Technical_Analysis/04.ChatGLM/chatglm4_simple_inference.ipynb b/01.LLM_Theory_Course/01.Industry_Model_Introduction/01.Classic_Model_Technical_Analysis/04.ChatGLM/chatglm4_simple_inference.ipynb
index 5483ffc..569a14f 100644
--- a/01.LLM_Theory_Course/01.Industry_Model_Introduction/01.Classic_Model_Technical_Analysis/04.ChatGLM/chatglm4_simple_inference.ipynb
+++ b/01.LLM_Theory_Course/01.Industry_Model_Introduction/01.Classic_Model_Technical_Analysis/04.ChatGLM/chatglm4_simple_inference.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "d73b7cdc",
+   "id": "0",
    "metadata": {},
    "source": [
     "# ChatGLM4聊天机器人"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "80128802-8a28-45e1-a728-4e673abfdb3e",
+   "id": "1",
    "metadata": {},
    "source": [
     "## 环境配置\n",
@@ -28,7 +28,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "265300fd-3bf5-4df6-9248-1b27cd4f570f",
+   "id": "2",
    "metadata": {},
    "source": [
     "## 代码开发"
@@ -36,294 +36,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "897d4ee9-b2b2-4de5-9f4f-7be4bc49b67a",
+   "execution_count": null,
+   "id": "3",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.\n",
-      "  setattr(self, word, getattr(machar, word).flat[0])\n",
-      "/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.\n",
-      "  return self._float_to_str(self.smallest_subnormal)\n",
-      "/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for <class 'numpy.float32'> type is zero.\n",
-      "  setattr(self, word, getattr(machar, word).flat[0])\n",
-      "/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for <class 'numpy.float32'> type is zero.\n",
-      "  return self._float_to_str(self.smallest_subnormal)\n",
-      "Building prefix dict from the default dictionary ...\n",
-      "Dumping model to file cache /tmp/jieba.cache\n",
-      "Loading model cost 1.022 seconds.\n",
-      "Prefix dict has been built successfully.\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ef6c147f60104856b73d7926b515b038",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0.00/6.01k [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "346d01049dbb4c14835b6ed02dbaf843",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0.00/2.50M [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'input_ids': Tensor(shape=[1, 6], dtype=Int64, value=\n",
-      "[[151331, 151333, 151336,    198, 109377, 151337]]), 'attention_mask': Tensor(shape=[1, 6], dtype=Int64, value=\n",
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-      "[MS_ALLOC_CONF]Runtime config:  enable_vmm:True  vmm_align_size:2MB\n"
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-     "output_type": "stream",
-     "text": [
-      "-\n",
-      "你好👋！很高兴见到你，有什么可以帮助你的吗？\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import mindspore\n",
     "from mindnlp.core import no_grad\n",
@@ -360,7 +78,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5b066153-0ae4-4161-aaec-e35f885d107a",
+   "id": "4",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/01.LLM_Theory_Course/01.Industry_Model_Introduction/01.Classic_Model_Technical_Analysis/04.ChatGLM/mindnlp_chatglm-6b.ipynb b/01.LLM_Theory_Course/01.Industry_Model_Introduction/01.Classic_Model_Technical_Analysis/04.ChatGLM/mindnlp_chatglm-6b.ipynb
index 266418a..bf55a15 100644
--- a/01.LLM_Theory_Course/01.Industry_Model_Introduction/01.Classic_Model_Technical_Analysis/04.ChatGLM/mindnlp_chatglm-6b.ipynb
+++ b/01.LLM_Theory_Course/01.Industry_Model_Introduction/01.Classic_Model_Technical_Analysis/04.ChatGLM/mindnlp_chatglm-6b.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "c014a89a-05f5-446a-bc53-dd048c6c4997",
+   "id": "0",
    "metadata": {},
    "source": [
     "## MindNLP ChatGLM-6B StreamChat\n",
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dda25ed9-8a0f-4606-a07a-f6c0ba5ee499",
+   "id": "1",
    "metadata": {},
    "source": [
     "该实验可进行在线体验，在线体验链接（https://pangu.huaweicloud.com/gallery/asset-detail.html?id=cdc88c83-1ac2-4862-b822-3ab200b01736\n",
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "34d5391a-8921-4185-9397-d081064cc131",
+   "id": "2",
    "metadata": {},
    "source": [
     "## 1. 效果展示\n",
@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eb420814-0aaf-4244-9c4b-70e087c509d7",
+   "id": "3",
    "metadata": {},
    "source": [
     "## 2. 案例体验\n",
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9a579a34-88f2-4fe3-94f1-88a6597a4a13",
+   "id": "4",
    "metadata": {},
    "source": [
     "### 2.1 环境安装\n",
@@ -66,8 +66,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "fefd157a-27bd-41e8-8618-7c8290b237c7",
+   "execution_count": null,
+   "id": "5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -78,8 +78,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "7d943c07-1a1f-42f9-a9e7-605329b8677f",
+   "execution_count": null,
+   "id": "6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -110,7 +110,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8d02075d-62bf-4466-a5b2-039317d1a233",
+   "id": "7",
    "metadata": {},
    "source": [
     "创建完成后，稍等片刻，或刷新页面，点击右上角（或左上角）kernel选择python-3.9.0\n",
@@ -120,8 +120,8 @@
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   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "9123d70f",
+   "execution_count": null,
+   "id": "8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -132,175 +132,10 @@
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-   "id": "d500c4c8-9d3c-47f9-8d56-f3cf80e6b788",
+   "execution_count": null,
+   "id": "9",
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-      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/e9/44/75a9c9421471a6c4805dbf2356f7c181a29c1879239abab1ea2cc8f38b40/sniffio-1.3.1-py3-none-any.whl (10 kB)\n",
-      "Requirement already satisfied: exceptiongroup>=1.0.2 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from anyio<5.0,>=3.0->gradio) (1.2.2)\n",
-      "Collecting starlette<0.42.0,>=0.40.0\n",
-      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/96/00/2b325970b3060c7cecebab6d295afe763365822b1306a12eeab198f74323/starlette-0.41.3-py3-none-any.whl (73 kB)\n",
-      "\u001b[K     |████████████████████████████████| 73 kB 7.2 MB/s  eta 0:00:01\n",
-      "\u001b[?25hCollecting httpcore==1.*\n",
-      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/87/f5/72347bc88306acb359581ac4d52f23c0ef445b57157adedb9aee0cd689d2/httpcore-1.0.7-py3-none-any.whl (78 kB)\n",
-      "\u001b[K     |████████████████████████████████| 78 kB 20.3 MB/s eta 0:00:01\n",
-      "\u001b[?25hRequirement already satisfied: certifi in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from httpx>=0.24.1->gradio) (2024.7.4)\n",
-      "Collecting h11<0.15,>=0.13\n",
-      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/95/04/ff642e65ad6b90db43e668d70ffb6736436c7ce41fcc549f4e9472234127/h11-0.14.0-py3-none-any.whl (58 kB)\n",
-      "\u001b[K     |████████████████████████████████| 58 kB 19.4 MB/s eta 0:00:01\n",
-      "\u001b[?25hRequirement already satisfied: filelock in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from huggingface-hub>=0.19.3->gradio) (3.15.4)\n",
-      "Requirement already satisfied: requests in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from huggingface-hub>=0.19.3->gradio) (2.32.3)\n",
-      "Requirement already satisfied: tqdm>=4.42.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from huggingface-hub>=0.19.3->gradio) (4.66.4)\n",
-      "Requirement already satisfied: zipp>=3.1.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from importlib-resources<7.0,>=1.3->gradio) (3.19.2)\n",
-      "Requirement already satisfied: python-dateutil>=2.7 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from matplotlib~=3.0->gradio) (2.9.0.post0)\n",
-      "Requirement already satisfied: fonttools>=4.22.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from matplotlib~=3.0->gradio) (4.53.1)\n",
-      "Requirement already satisfied: pyparsing>=2.2.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from matplotlib~=3.0->gradio) (3.1.2)\n",
-      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from matplotlib~=3.0->gradio) (1.4.5)\n",
-      "Requirement already satisfied: cycler>=0.10 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from matplotlib~=3.0->gradio) (0.12.1)\n",
-      "Requirement already satisfied: pytz>=2017.3 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pandas<3.0,>=1.0->gradio) (2024.1)\n",
-      "Collecting pydantic-core==2.27.2\n",
-      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/8a/9e/e44b8cb0edf04a2f0a1f6425a65ee089c1d6f9c4c2dcab0209127b6fdfc2/pydantic_core-2.27.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.8 MB)\n",
-      "\u001b[K     |████████████████████████████████| 1.8 MB 44.3 MB/s eta 0:00:01\n",
-      "\u001b[?25hCollecting annotated-types>=0.6.0\n",
-      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl (13 kB)\n",
-      "Requirement already satisfied: six>=1.5 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib~=3.0->gradio) (1.16.0)\n",
-      "Requirement already satisfied: rich>=10.11.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from typer<1.0,>=0.12->gradio) (13.7.1)\n",
-      "Requirement already satisfied: click>=8.0.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from typer<1.0,>=0.12->gradio) (8.1.7)\n",
-      "Collecting shellingham>=1.3.0\n",
-      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/e0/f9/0595336914c5619e5f28a1fb793285925a8cd4b432c9da0a987836c7f822/shellingham-1.5.4-py2.py3-none-any.whl (9.8 kB)\n",
-      "Requirement already satisfied: markdown-it-py>=2.2.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from rich>=10.11.0->typer<1.0,>=0.12->gradio) (3.0.0)\n",
-      "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from rich>=10.11.0->typer<1.0,>=0.12->gradio) (2.18.0)\n",
-      "Requirement already satisfied: mdurl~=0.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from markdown-it-py>=2.2.0->rich>=10.11.0->typer<1.0,>=0.12->gradio) (0.1.2)\n",
-      "Requirement already satisfied: latex2mathml in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mdtex2html) (3.77.0)\n",
-      "Requirement already satisfied: markdown in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mdtex2html) (3.6)\n",
-      "Requirement already satisfied: importlib-metadata>=4.4 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from markdown->mdtex2html) (8.2.0)\n",
-      "Requirement already satisfied: charset-normalizer<4,>=2 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from requests->huggingface-hub>=0.19.3->gradio) (2.0.12)\n",
-      "Installing collected packages: urllib3, sniffio, h11, pydantic-core, httpcore, anyio, annotated-types, websockets, starlette, shellingham, pydantic, httpx, uvicorn, typer, tomlkit, ruff, python-multipart, pydub, orjson, importlib-resources, gradio-client, ffmpy, fastapi, aiofiles, gradio\n",
-      "  Attempting uninstall: urllib3\n",
-      "    Found existing installation: urllib3 1.26.7\n",
-      "    Uninstalling urllib3-1.26.7:\n",
-      "      Successfully uninstalled urllib3-1.26.7\n",
-      "  Attempting uninstall: tomlkit\n",
-      "    Found existing installation: tomlkit 0.13.0\n",
-      "    Uninstalling tomlkit-0.13.0:\n",
-      "      Successfully uninstalled tomlkit-0.13.0\n",
-      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
-      "botocore 1.15.49 requires urllib3<1.26,>=1.20; python_version != \"3.4\", but you have urllib3 2.3.0 which is incompatible.\n",
-      "modelarts 1.4.28 requires lxml==5.1.0, but you have lxml 4.9.3 which is incompatible.\n",
-      "modelarts 1.4.28 requires matplotlib==3.5.2, but you have matplotlib 3.5.1 which is incompatible.\n",
-      "modelarts 1.4.28 requires prettytable<=3.7.0, but you have prettytable 3.10.2 which is incompatible.\n",
-      "modelarts 1.4.28 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\n",
-      "modelarts 1.4.28 requires tqdm<=4.66.1, but you have tqdm 4.66.4 which is incompatible.\n",
-      "modelarts 1.4.28 requires typing-extensions==4.7.1, but you have typing-extensions 4.12.2 which is incompatible.\n",
-      "modelarts 1.4.28 requires urllib3==1.26.18, but you have urllib3 2.3.0 which is incompatible.\u001b[0m\n",
-      "Successfully installed aiofiles-23.2.1 annotated-types-0.7.0 anyio-4.7.0 fastapi-0.115.6 ffmpy-0.5.0 gradio-4.44.1 gradio-client-1.3.0 h11-0.14.0 httpcore-1.0.7 httpx-0.28.1 importlib-resources-6.5.2 orjson-3.10.13 pydantic-2.10.4 pydantic-core-2.27.2 pydub-0.25.1 python-multipart-0.0.20 ruff-0.8.6 shellingham-1.5.4 sniffio-1.3.1 starlette-0.41.3 tomlkit-0.12.0 typer-0.15.1 urllib3-2.3.0 uvicorn-0.34.0 websockets-12.0\n",
-      "\u001b[33mWARNING: You are using pip version 21.0.1; however, version 24.3.1 is available.\n",
-      "You should consider upgrading via the '/home/ma-user/anaconda3/envs/MindSpore/bin/python3.9 -m pip install --upgrade pip' command.\u001b[0m\n",
-      "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
-      "Collecting ipywidgets\n",
-      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/22/2d/9c0b76f2f9cc0ebede1b9371b6f317243028ed60b90705863d493bae622e/ipywidgets-8.1.5-py3-none-any.whl (139 kB)\n",
-      "\u001b[K     |████████████████████████████████| 139 kB 1.6 MB/s eta 0:00:01\n",
-      "\u001b[?25hCollecting jupyterlab-widgets~=3.0.12\n",
-      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/a9/93/858e87edc634d628e5d752ba944c2833133a28fa87bb093e6832ced36a3e/jupyterlab_widgets-3.0.13-py3-none-any.whl (214 kB)\n",
-      "\u001b[K     |████████████████████████████████| 214 kB 6.4 MB/s eta 0:00:01     |██████████████████████████████▋ | 204 kB 6.4 MB/s eta 0:00:01\n",
-      "\u001b[?25hRequirement already satisfied: traitlets>=4.3.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from ipywidgets) (5.14.3)\n",
-      "Collecting comm>=0.1.3\n",
-      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/e6/75/49e5bfe642f71f272236b5b2d2691cf915a7283cc0ceda56357b61daa538/comm-0.2.2-py3-none-any.whl (7.2 kB)\n",
-      "Requirement already satisfied: ipython>=6.1.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from ipywidgets) (8.18.1)\n",
-      "Collecting widgetsnbextension~=4.0.12\n",
-      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/21/02/88b65cc394961a60c43c70517066b6b679738caf78506a5da7b88ffcb643/widgetsnbextension-4.0.13-py3-none-any.whl (2.3 MB)\n",
-      "\u001b[K     |████████████████████████████████| 2.3 MB 17.9 MB/s eta 0:00:01\n",
-      "\u001b[?25hRequirement already satisfied: decorator in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from ipython>=6.1.0->ipywidgets) (4.4.1)\n",
-      "Requirement already satisfied: prompt-toolkit<3.1.0,>=3.0.41 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from ipython>=6.1.0->ipywidgets) (3.0.47)\n",
-      "Requirement already satisfied: typing-extensions in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from ipython>=6.1.0->ipywidgets) (4.12.2)\n",
-      "Requirement already satisfied: pygments>=2.4.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from ipython>=6.1.0->ipywidgets) (2.18.0)\n",
-      "Requirement already satisfied: exceptiongroup in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from ipython>=6.1.0->ipywidgets) (1.2.2)\n",
-      "Requirement already satisfied: jedi>=0.16 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from ipython>=6.1.0->ipywidgets) (0.19.1)\n",
-      "Requirement already satisfied: matplotlib-inline in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from ipython>=6.1.0->ipywidgets) (0.1.7)\n",
-      "Requirement already satisfied: stack-data in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from ipython>=6.1.0->ipywidgets) (0.6.3)\n",
-      "Requirement already satisfied: pexpect>4.3 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from ipython>=6.1.0->ipywidgets) (4.9.0)\n",
-      "Requirement already satisfied: parso<0.9.0,>=0.8.3 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from jedi>=0.16->ipython>=6.1.0->ipywidgets) (0.8.4)\n",
-      "Requirement already satisfied: ptyprocess>=0.5 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pexpect>4.3->ipython>=6.1.0->ipywidgets) (0.7.0)\n",
-      "Requirement already satisfied: wcwidth in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from prompt-toolkit<3.1.0,>=3.0.41->ipython>=6.1.0->ipywidgets) (0.2.13)\n",
-      "Requirement already satisfied: executing>=1.2.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (2.0.1)\n",
-      "Requirement already satisfied: asttokens>=2.1.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (2.4.1)\n",
-      "Requirement already satisfied: pure-eval in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (0.2.3)\n",
-      "Requirement already satisfied: six>=1.12.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from asttokens>=2.1.0->stack-data->ipython>=6.1.0->ipywidgets) (1.16.0)\n",
-      "Installing collected packages: widgetsnbextension, jupyterlab-widgets, comm, ipywidgets\n",
-      "Successfully installed comm-0.2.2 ipywidgets-8.1.5 jupyterlab-widgets-3.0.13 widgetsnbextension-4.0.13\n",
-      "\u001b[33mWARNING: You are using pip version 21.0.1; however, version 24.3.1 is available.\n",
-      "You should consider upgrading via the '/home/ma-user/anaconda3/envs/MindSpore/bin/python3.9 -m pip install --upgrade pip' command.\u001b[0m\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!pip install gradio mdtex2html -i https://pypi.tuna.tsinghua.edu.cn/simple\n",
     "!pip install ipywidgets -i https://pypi.tuna.tsinghua.edu.cn/simple"
@@ -308,30 +143,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "6d43d04e",
+   "execution_count": null,
+   "id": "10",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "--2025-01-05 02:06:29--  https://openi.pcl.ac.cn/lvyufeng/frpc-gradio/raw/branch/master/frpc_linux_amd64\n",
-      "Resolving proxy-notebook.modelarts.com (proxy-notebook.modelarts.com)... 192.168.0.33\n",
-      "Connecting to proxy-notebook.modelarts.com (proxy-notebook.modelarts.com)|192.168.0.33|:8083... connected.\n",
-      "Proxy request sent, awaiting response... 200 OK\n",
-      "Length: unspecified [application/octet-stream]\n",
-      "Saving to: ‘/home/ma-user/work/frpc_linux_amd64’\n",
-      "\n",
-      "frpc_linux_amd64        [   <=>              ]  10.85M  23.3MB/s    in 0.5s    \n",
-      "\n",
-      "2025-01-05 02:06:30 (23.3 MB/s) - ‘/home/ma-user/work/frpc_linux_amd64’ saved [11374592]\n",
-      "\n",
-      "cp: cannot create regular file '/home/ma-user/anaconda3/envs/python-3.9.0/lib/python3.9/site-packages/gradio/frpc_linux_amd64_v0.2': No such file or directory\n",
-      "chmod: cannot access '/home/ma-user/anaconda3/envs/python-3.9.0/lib/python3.9/site-packages/gradio/frpc_linux_amd64_v0.2': No such file or directory\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# %%capture captured_output\n",
     "!wget -P /home/ma-user/work https://openi.pcl.ac.cn/lvyufeng/frpc-gradio/raw/branch/master/frpc_linux_amd64\n",
@@ -341,7 +156,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f6703d3e-8451-47e7-bac8-401cdb039be7",
+   "id": "11",
    "metadata": {},
    "source": [
     "## 3. 代码开发"
@@ -349,259 +164,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "9b8ee640",
+   "execution_count": null,
+   "id": "12",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[WARNING] GE_ADPT(6632,ffff8654b0b0,python):2025-01-05-02:06:54.331.962 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclmdlBundleGetModelId failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclmdlBundleGetModelId\n",
-      "[WARNING] GE_ADPT(6632,ffff8654b0b0,python):2025-01-05-02:06:54.332.029 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclmdlBundleLoadFromMem failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclmdlBundleLoadFromMem\n",
-      "[WARNING] GE_ADPT(6632,ffff8654b0b0,python):2025-01-05-02:06:54.332.048 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclmdlBundleUnload failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclmdlBundleUnload\n",
-      "[WARNING] GE_ADPT(6632,ffff8654b0b0,python):2025-01-05-02:06:54.332.232 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclrtGetMemUceInfo failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclrtGetMemUceInfo\n",
-      "[WARNING] GE_ADPT(6632,ffff8654b0b0,python):2025-01-05-02:06:54.332.249 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclrtDeviceTaskAbort failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclrtDeviceTaskAbort\n",
-      "[WARNING] GE_ADPT(6632,ffff8654b0b0,python):2025-01-05-02:06:54.332.265 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclrtMemUceRepair failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclrtMemUceRepair\n",
-      "[WARNING] GE_ADPT(6632,ffff8654b0b0,python):2025-01-05-02:06:54.334.715 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol acltdtCleanChannel failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libacl_tdt_channel.so: undefined symbol: acltdtCleanChannel\n",
-      "[WARNING] ME(6632:281472935440560,MainProcess):2025-01-05-02:06:54.576.575 [mindspore/run_check/_check_version.py:398] Can not find the tbe operator implementation(need by mindspore-ascend). Please check whether the Environment Variable PYTHONPATH is set. For details, refer to the installation guidelines: https://www.mindspore.cn/install\n",
-      "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/numpy/core/getlimits.py:499: UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.\n",
-      "  setattr(self, word, getattr(machar, word).flat[0])\n",
-      "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.\n",
-      "  return self._float_to_str(self.smallest_subnormal)\n",
-      "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/numpy/core/getlimits.py:499: UserWarning: The value of the smallest subnormal for <class 'numpy.float32'> type is zero.\n",
-      "  setattr(self, word, getattr(machar, word).flat[0])\n",
-      "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for <class 'numpy.float32'> type is zero.\n",
-      "  return self._float_to_str(self.smallest_subnormal)\n",
-      "cannot found `mindformers.experimental`, please install dev version by\n",
-      "`pip install git+https://gitee.com/mindspore/mindformers` \n",
-      "or remove mindformers by \n",
-      "`pip uninstall mindformers`\n",
-      "Building prefix dict from the default dictionary ...\n",
-      "Dumping model to file cache /tmp/jieba.cache\n",
-      "Loading model cost 1.327 seconds.\n",
-      "Prefix dict has been built successfully.\n",
-      "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/Cython/Compiler/Main.py:384: FutureWarning: Cython directive 'language_level' not set, using '3str' for now (Py3). This has changed from earlier releases! File: /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/mindnlp/transformers/models/graphormer/algos_graphormer.pyx\n",
-      "  tree = Parsing.p_module(s, pxd, full_module_name)\n",
-      "[WARNING] ME(6632:281472935440560,MainProcess):2025-01-05-02:07:20.833.731 [mindspore/run_check/_check_version.py:398] Can not find the tbe operator implementation(need by mindspore-ascend). Please check whether the Environment Variable PYTHONPATH is set. For details, refer to the installation guidelines: https://www.mindspore.cn/install\n",
-      "[WARNING] ME(6632:281472935440560,MainProcess):2025-01-05-02:07:20.836.993 [mindspore/run_check/_check_version.py:398] Can not find the tbe operator implementation(need by mindspore-ascend). Please check whether the Environment Variable PYTHONPATH is set. For details, refer to the installation guidelines: https://www.mindspore.cn/install\n"
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-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "ChatGLMForConditionalGeneration has generative capabilities, as `prepare_inputs_for_generation` is explicitly overwritten. However, it doesn't directly inherit from `GenerationMixin`.`PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.\n",
-      "  - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).\n",
-      "  - If you are not the owner of the model architecture class, please contact the model code owner to update it.\n",
-      "[WARNING] DEVICE(6632,ffff8654b0b0,python):2025-01-05-02:15:16.413.632 [mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_vmm_adapter.h:188] CheckVmmDriverVersion] Driver version is less than 24.0.0, vmm is disabled by default, drvier_version: 23.0.6\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "959eb249061a41e9bf1130fff4c0cf04",
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-       "Loading checkpoint shards:   0%|          | 0/8 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
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-    },
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-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c6de97477ea84214987e43f9ce3b1392",
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/mindnlp/transformers/tokenization_utils_base.py:1526: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted, and will be then set to `False` by default. \n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from mindnlp.transformers import AutoModelForSeq2SeqLM, AutoTokenizer\n",
     "import gradio as gr\n",
@@ -618,35 +184,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "95fc7ad5-b210-4318-8897-af50b8e6ebd4",
+   "execution_count": null,
+   "id": "13",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "The dtype of attention mask (Int64) is not bool\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "|\r"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'你好👋！我是人工智能助手 ChatGLM-6B'"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "prompt = '你好'\n",
     "history = []\n",
@@ -656,8 +197,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "7e589553-2617-4a2c-a330-d4d6029d21da",
+   "execution_count": null,
+   "id": "14",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -704,7 +245,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "92ff6df8-6534-4b8b-983c-8478b38a0fbf",
+   "id": "15",
    "metadata": {},
    "source": [
     "## 3.2 基于 Gradio 创建聊天应用"
@@ -712,8 +253,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "09e943b3-16de-49b4-85f2-1e86001ce7b5",
+   "execution_count": null,
+   "id": "16",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -739,46 +280,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "0b6fff57-d324-4d54-b529-bc2c293689bb",
+   "execution_count": null,
+   "id": "17",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Running on local URL:  http://127.0.0.1:7860\n",
-      "\n",
-      "Could not create share link. Missing file: /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/gradio/frpc_linux_aarch64_v0.2. \n",
-      "\n",
-      "Please check your internet connection. This can happen if your antivirus software blocks the download of this file. You can install manually by following these steps: \n",
-      "\n",
-      "1. Download this file: https://cdn-media.huggingface.co/frpc-gradio-0.2/frpc_linux_aarch64\n",
-      "2. Rename the downloaded file to: frpc_linux_aarch64_v0.2\n",
-      "3. Move the file to this location: /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/gradio\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div><iframe src=\"http://127.0.0.1:7860/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": []
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "#运行Gradio界面，运行成功后点击“Running on public URL”后的网页链接即可体验\n",
     "import gradio as gr\n",
@@ -814,7 +319,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "356a6c98-d42a-4470-a1e9-40714a17642e",
+   "id": "18",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/01.LLM_Theory_Course/01.Industry_Model_Introduction/01.Classic_Model_Technical_Analysis/07.LLaMA2/llama_finetune_inference.ipynb b/01.LLM_Theory_Course/01.Industry_Model_Introduction/01.Classic_Model_Technical_Analysis/07.LLaMA2/llama_finetune_inference.ipynb
index 0bcc02a..0bb28f7 100644
--- a/01.LLM_Theory_Course/01.Industry_Model_Introduction/01.Classic_Model_Technical_Analysis/07.LLaMA2/llama_finetune_inference.ipynb
+++ b/01.LLM_Theory_Course/01.Industry_Model_Introduction/01.Classic_Model_Technical_Analysis/07.LLaMA2/llama_finetune_inference.ipynb
@@ -100,46 +100,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Looking in indexes: https://mirrors.aliyun.com/pypi/simple/\n",
-      "Collecting mindspore==2.5.0\n",
-      "  Downloading https://mirrors.aliyun.com/pypi/packages/23/22/dff0f1bef6c0846a97271ae5d39ca187914f39562f9e3f6787041dea1a97/mindspore-2.5.0-cp39-cp39-manylinux1_x86_64.whl (958.4 MB)\n",
-      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m958.4/958.4 MB\u001b[0m \u001b[31m9.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:03\u001b[0m\n",
-      "\u001b[?25hCollecting numpy<2.0.0,>=1.20.0 (from mindspore==2.5.0)\n",
-      "  Downloading https://mirrors.aliyun.com/pypi/packages/54/30/c2a907b9443cf42b90c17ad10c1e8fa801975f01cb9764f3f8eb8aea638b/numpy-1.26.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.2 MB)\n",
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-      "\u001b[?25hCollecting protobuf>=3.13.0 (from mindspore==2.5.0)\n",
-      "  Downloading https://mirrors.aliyun.com/pypi/packages/28/50/1925de813499546bc8ab3ae857e3ec84efe7d2f19b34529d0c7c3d02d11d/protobuf-6.30.2-cp39-abi3-manylinux2014_x86_64.whl (316 kB)\n",
-      "Requirement already satisfied: asttokens>=2.0.4 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindspore==2.5.0) (3.0.0)\n",
-      "Collecting pillow>=6.2.0 (from mindspore==2.5.0)\n",
-      "  Downloading https://mirrors.aliyun.com/pypi/packages/f6/46/0bd0ca03d9d1164a7fa33d285ef6d1c438e963d0c8770e4c5b3737ef5abe/pillow-11.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.4 MB)\n",
-      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.4/4.4 MB\u001b[0m \u001b[31m14.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
-      "\u001b[?25hCollecting scipy>=1.5.4 (from mindspore==2.5.0)\n",
-      "  Downloading https://mirrors.aliyun.com/pypi/packages/35/f5/d0ad1a96f80962ba65e2ce1de6a1e59edecd1f0a7b55990ed208848012e0/scipy-1.13.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (38.6 MB)\n",
-      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m38.6/38.6 MB\u001b[0m \u001b[31m16.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
-      "\u001b[?25hRequirement already satisfied: packaging>=20.0 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindspore==2.5.0) (24.2)\n",
-      "Requirement already satisfied: psutil>=5.6.1 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindspore==2.5.0) (5.9.1)\n",
-      "Collecting astunparse>=1.6.3 (from mindspore==2.5.0)\n",
-      "  Downloading https://mirrors.aliyun.com/pypi/packages/2b/03/13dde6512ad7b4557eb792fbcf0c653af6076b81e5941d36ec61f7ce6028/astunparse-1.6.3-py2.py3-none-any.whl (12 kB)\n",
-      "Collecting safetensors>=0.4.0 (from mindspore==2.5.0)\n",
-      "  Downloading https://mirrors.aliyun.com/pypi/packages/a6/f8/dae3421624fcc87a89d42e1898a798bc7ff72c61f38973a65d60df8f124c/safetensors-0.5.3-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (471 kB)\n",
-      "Collecting dill>=0.3.7 (from mindspore==2.5.0)\n",
-      "  Downloading https://mirrors.aliyun.com/pypi/packages/46/d1/e73b6ad76f0b1fb7f23c35c6d95dbc506a9c8804f43dda8cb5b0fa6331fd/dill-0.3.9-py3-none-any.whl (119 kB)\n",
-      "Requirement already satisfied: wheel<1.0,>=0.23.0 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from astunparse>=1.6.3->mindspore==2.5.0) (0.45.1)\n",
-      "Requirement already satisfied: six<2.0,>=1.6.1 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from astunparse>=1.6.3->mindspore==2.5.0) (1.17.0)\n",
-      "Installing collected packages: safetensors, protobuf, pillow, numpy, dill, astunparse, scipy, mindspore\n",
-      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
-      "auto-tune 0.1.0 requires te, which is not installed.\n",
-      "schedule-search 0.0.1 requires absl-py, which is not installed.\u001b[0m\u001b[31m\n",
-      "\u001b[0mSuccessfully installed astunparse-1.6.3 dill-0.3.9 mindspore-2.5.0 numpy-1.26.4 pillow-11.1.0 protobuf-6.30.2 safetensors-0.5.3 scipy-1.13.1\n",
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/2.5.0/MindSpore/unified/x86_64/mindspore-2.5.0-cp39-cp39-linux_x86_64.whl --trusted-host ms-release.obs.cn-north-4.myhuaweicloud.com -i https://pypi.tuna.tsinghua.edu.cn/simple"
    ]
@@ -154,88 +115,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Looking in indexes: https://mirrors.aliyun.com/pypi/simple\n",
-      "Collecting mindnlp==0.4.0\n",
-      "  Downloading https://mirrors.aliyun.com/pypi/packages/0f/a8/5a072852d28a51417b5e330b32e6ae5f26b491ef01a15ba968e77f785e69/mindnlp-0.4.0-py3-none-any.whl (8.4 MB)\n",
-      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.4/8.4 MB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m0m\n",
-      "\u001b[?25hRequirement already satisfied: mindspore>=2.2.14 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindnlp==0.4.0) (2.5.0)\n",
-      "Requirement already satisfied: tqdm in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindnlp==0.4.0) (4.67.1)\n",
-      "Requirement already satisfied: requests in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindnlp==0.4.0) (2.32.3)\n",
-      "Requirement already satisfied: datasets in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindnlp==0.4.0) (3.5.0)\n",
-      "Requirement already satisfied: evaluate in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.4.3)\n",
-      "Requirement already satisfied: tokenizers==0.19.1 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.19.1)\n",
-      "Requirement already satisfied: safetensors in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.5.3)\n",
-      "Requirement already satisfied: sentencepiece in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.2.0)\n",
-      "Requirement already satisfied: regex in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindnlp==0.4.0) (2024.11.6)\n",
-      "Requirement already satisfied: addict in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindnlp==0.4.0) (2.4.0)\n",
-      "Requirement already satisfied: ml-dtypes in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.5.1)\n",
-      "Requirement already satisfied: pyctcdecode in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.5.0)\n",
-      "Collecting jieba (from mindnlp==0.4.0)\n",
-      "  Downloading https://mirrors.aliyun.com/pypi/packages/c6/cb/18eeb235f833b726522d7ebed54f2278ce28ba9438e3135ab0278d9792a2/jieba-0.42.1.tar.gz (19.2 MB)\n",
-      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m19.2/19.2 MB\u001b[0m \u001b[31m15.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
-      "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25ldone\n",
-      "\u001b[?25hRequirement already satisfied: pytest==7.2.0 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindnlp==0.4.0) (7.2.0)\n",
-      "Requirement already satisfied: pillow>=10.0.0 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindnlp==0.4.0) (11.1.0)\n",
-      "Requirement already satisfied: attrs>=19.2.0 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (24.3.0)\n",
-      "Requirement already satisfied: iniconfig in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (2.1.0)\n",
-      "Requirement already satisfied: packaging in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (24.2)\n",
-      "Requirement already satisfied: pluggy<2.0,>=0.12 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (1.5.0)\n",
-      "Requirement already satisfied: exceptiongroup>=1.0.0rc8 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (1.2.2)\n",
-      "Requirement already satisfied: tomli>=1.0.0 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (2.0.1)\n",
-      "Requirement already satisfied: huggingface-hub<1.0,>=0.16.4 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from tokenizers==0.19.1->mindnlp==0.4.0) (0.30.2)\n",
-      "Requirement already satisfied: numpy<2.0.0,>=1.20.0 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (1.26.4)\n",
-      "Requirement already satisfied: protobuf>=3.13.0 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (6.30.2)\n",
-      "Requirement already satisfied: asttokens>=2.0.4 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (3.0.0)\n",
-      "Requirement already satisfied: scipy>=1.5.4 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (1.13.1)\n",
-      "Requirement already satisfied: psutil>=5.6.1 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (5.9.1)\n",
-      "Requirement already satisfied: astunparse>=1.6.3 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (1.6.3)\n",
-      "Requirement already satisfied: dill>=0.3.7 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (0.3.8)\n",
-      "Requirement already satisfied: filelock in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (3.18.0)\n",
-      "Requirement already satisfied: pyarrow>=15.0.0 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (19.0.1)\n",
-      "Requirement already satisfied: pandas in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (2.2.3)\n",
-      "Requirement already satisfied: xxhash in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (3.5.0)\n",
-      "Requirement already satisfied: multiprocess<0.70.17 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (0.70.16)\n",
-      "Requirement already satisfied: fsspec<=2024.12.0,>=2023.1.0 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from fsspec[http]<=2024.12.0,>=2023.1.0->datasets->mindnlp==0.4.0) (2024.12.0)\n",
-      "Requirement already satisfied: aiohttp in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (3.11.16)\n",
-      "Requirement already satisfied: pyyaml>=5.1 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (6.0.2)\n",
-      "Requirement already satisfied: charset-normalizer<4,>=2 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from requests->mindnlp==0.4.0) (3.3.2)\n",
-      "Requirement already satisfied: idna<4,>=2.5 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from requests->mindnlp==0.4.0) (3.7)\n",
-      "Requirement already satisfied: urllib3<3,>=1.21.1 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from requests->mindnlp==0.4.0) (2.3.0)\n",
-      "Requirement already satisfied: certifi>=2017.4.17 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from requests->mindnlp==0.4.0) (2025.1.31)\n",
-      "Requirement already satisfied: pygtrie<3.0,>=2.1 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from pyctcdecode->mindnlp==0.4.0) (2.5.0)\n",
-      "Requirement already satisfied: hypothesis<7,>=6.14 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from pyctcdecode->mindnlp==0.4.0) (6.130.13)\n",
-      "Requirement already satisfied: wheel<1.0,>=0.23.0 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from astunparse>=1.6.3->mindspore>=2.2.14->mindnlp==0.4.0) (0.45.1)\n",
-      "Requirement already satisfied: six<2.0,>=1.6.1 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from astunparse>=1.6.3->mindspore>=2.2.14->mindnlp==0.4.0) (1.17.0)\n",
-      "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.0) (2.6.1)\n",
-      "Requirement already satisfied: aiosignal>=1.1.2 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.0) (1.3.2)\n",
-      "Requirement already satisfied: async-timeout<6.0,>=4.0 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.0) (5.0.1)\n",
-      "Requirement already satisfied: frozenlist>=1.1.1 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.0) (1.5.0)\n",
-      "Requirement already satisfied: multidict<7.0,>=4.5 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.0) (6.4.2)\n",
-      "Requirement already satisfied: propcache>=0.2.0 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.0) (0.3.1)\n",
-      "Requirement already satisfied: yarl<2.0,>=1.17.0 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.0) (1.19.0)\n",
-      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.0) (4.13.1)\n",
-      "Requirement already satisfied: sortedcontainers<3.0.0,>=2.1.0 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from hypothesis<7,>=6.14->pyctcdecode->mindnlp==0.4.0) (2.4.0)\n",
-      "Requirement already satisfied: python-dateutil>=2.8.2 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from pandas->datasets->mindnlp==0.4.0) (2.9.0.post0)\n",
-      "Requirement already satisfied: pytz>=2020.1 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from pandas->datasets->mindnlp==0.4.0) (2025.2)\n",
-      "Requirement already satisfied: tzdata>=2022.7 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from pandas->datasets->mindnlp==0.4.0) (2025.2)\n",
-      "Building wheels for collected packages: jieba\n",
-      "  Building wheel for jieba (setup.py) ... \u001b[?25ldone\n",
-      "\u001b[?25h  Created wheel for jieba: filename=jieba-0.42.1-py3-none-any.whl size=19314508 sha256=30064bba508d12a9c2c545bdec7e271f61d5a83e9fdd53298a82e74659e1fd26\n",
-      "  Stored in directory: /home/jiangna1/.cache/pip/wheels/95/ef/7c/d8b3108835edfa15487417c5bddff166482b195d8090117ac5\n",
-      "Successfully built jieba\n",
-      "Installing collected packages: jieba, mindnlp\n",
-      "Successfully installed jieba-0.42.1 mindnlp-0.4.0\n",
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%pip install mindnlp==0.4.0 -i https://mirrors.aliyun.com/pypi/simple\n"
    ]
@@ -272,7 +154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -284,17 +166,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[WARNING] ME(52617:140460371482432,MainProcess):2025-04-11-09:21:14.572.566 [mindspore/context.py:1335] For 'context.set_context', the parameter 'device_target' will be deprecated and removed in a future version. Please use the api mindspore.set_device() instead.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#将模式设置为动态图模式（PYNATIVE_MODE），并指定设备目标为Ascend芯片\n",
     "ms.set_context(mode=ms.PYNATIVE_MODE, device_target=\"Ascend\")\n"
@@ -302,7 +176,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -366,7 +240,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -404,7 +278,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
@@ -423,17 +297,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[{'instruction': '只剩一个心脏了还能活吗？', 'output': '能，人本来就只有一个心脏。'}, {'instruction': '爸爸再婚，我是不是就有了个新娘？', 'output': '不是的，你有了一个继母。\"新娘\"是指新婚的女方，而你爸爸再婚，他的新婚妻子对你来说是继母。'}, {'instruction': '樟脑丸是我吃过最难吃的硬糖有奇怪的味道怎么还有人买', 'output': '樟脑丸并不是硬糖，而是一种常见的驱虫药，不能食用。虽然它的味道可能不太好，但是由于其有效的驱虫效果，所以仍然有很多人会购买。'}]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "with open(data_path, 'r', encoding='utf-8') as f:\n",
     "    data = json.load(f)\n",
@@ -449,20 +315,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 29871, 2056]"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from mindnlp.transformers import AutoTokenizer\n",
     "\n",
@@ -482,7 +337,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -515,32 +370,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[MS_ALLOC_CONF]Runtime config:  enable_vmm:True  vmm_align_size:2MB\n",
-      "Sample 0: Input IDs: [    1  4911 29901 29871 47133 32002 37755 30743 33302 31704]\n",
-      "Sample 0: Labels: [    1  4911 29901 29871 47133 32002 37755 30743 33302 31704]\n",
-      "\n",
-      "Sample 1: Input IDs: [    1  4911 29901 29871 33594 31733 33364 30214 30672 32308]\n",
-      "Sample 1: Labels: [    1  4911 29901 29871 33594 31733 33364 30214 30672 32308]\n",
-      "\n",
-      "Sample 2: Input IDs: [    1  4911 29901 29871 47019 33027 31818 34030 39950 44345]\n",
-      "Sample 2: Labels: [    1  4911 29901 29871 47019 33027 31818 34030 39950 44345]\n",
-      "\n",
-      "Sample 3: Input IDs: [    1  4911 29901 29871 34214 30698 30429 36310 32658 30743]\n",
-      "Sample 3: Labels: [    1  4911 29901 29871 34214 30698 30429 36310 32658 30743]\n",
-      "\n",
-      "Sample 4: Input IDs: [    1  4911 29901 32581 34822 31639  2882  6530 30883 30210]\n",
-      "Sample 4: Labels: [    1  4911 29901 32581 34822 31639  2882  6530 30883 30210]\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "for i, sample in enumerate(train_dataset.create_dict_iterator()):\n",
     "    if i >= 5:\n",
@@ -565,7 +397,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
@@ -584,33 +416,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "LlamaForCausalLM has generative capabilities, as `prepare_inputs_for_generation` is explicitly overwritten. However, it doesn't directly inherit from `GenerationMixin`.`PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.\n",
-      "  - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).\n",
-      "  - If you are not the owner of the model architecture class, please contact the model code owner to update it.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jiangna1/miniconda3/envs/ms39/lib/python3.9/site-packages/mindnlp/transformers/generation/configuration_utils.py:557: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.2` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.\n",
-      "  warnings.warn(\n",
-      "/home/jiangna1/miniconda3/envs/ms39/lib/python3.9/site-packages/mindnlp/transformers/generation/configuration_utils.py:562: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.\n",
-      "  warnings.warn(\n",
-      "/home/jiangna1/miniconda3/envs/ms39/lib/python3.9/site-packages/mindnlp/transformers/generation/configuration_utils.py:557: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.2` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n",
-      "  warnings.warn(\n",
-      "/home/jiangna1/miniconda3/envs/ms39/lib/python3.9/site-packages/mindnlp/transformers/generation/configuration_utils.py:562: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from mindnlp.transformers import AutoModelForCausalLM,  GenerationConfig\n",
     "\n",
@@ -621,7 +429,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -639,7 +447,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
@@ -668,7 +476,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -684,7 +492,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -748,7 +556,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
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-     "text": [
-      "The intermediate checkpoints of PEFT may not be saved correctly, consider using a custom callback to save adapter_model.bin in corresponding saving folders. Check some examples here: https://github.com/huggingface/peft/issues/96\n",
-      "100%|██████████| 350/350 [1:08:12<00:00, 11.69s/it]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'train_runtime': 4092.112, 'train_samples_per_second': 23.264, 'train_steps_per_second': 0.086, 'train_loss': 3.2515819876534597, 'epoch': 65.88}\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "trainer.train()\n",
     "\n",
@@ -1415,109 +627,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "model merge succeeded\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "LlamaForCausalLM(\n",
-       "  (model): LlamaModel(\n",
-       "    (embed_tokens): Embedding(55296, 4096, padding_idx=0)\n",
-       "    (layers): ModuleList(\n",
-       "      (0-3): 4 x LlamaDecoderLayer(\n",
-       "        (self_attn): LlamaAttention(\n",
-       "          (q_proj): lora.Linear(\n",
-       "            (base_layer): Linear (4096 -> 4096)\n",
-       "            (lora_dropout): ModuleDict(\n",
-       "              (default): Dropout(p=0.1, inplace=False)\n",
-       "            )\n",
-       "            (lora_A): ModuleDict(\n",
-       "              (default): Linear (4096 -> 8)\n",
-       "            )\n",
-       "            (lora_B): ModuleDict(\n",
-       "              (default): Linear (8 -> 4096)\n",
-       "            )\n",
-       "            (lora_embedding_A): ParameterDict()\n",
-       "            (lora_embedding_B): ParameterDict()\n",
-       "            (lora_magnitude_vector): ModuleDict()\n",
-       "          )\n",
-       "          (k_proj): lora.Linear(\n",
-       "            (base_layer): Linear (4096 -> 4096)\n",
-       "            (lora_dropout): ModuleDict(\n",
-       "              (default): Dropout(p=0.1, inplace=False)\n",
-       "            )\n",
-       "            (lora_A): ModuleDict(\n",
-       "              (default): Linear (4096 -> 8)\n",
-       "            )\n",
-       "            (lora_B): ModuleDict(\n",
-       "              (default): Linear (8 -> 4096)\n",
-       "            )\n",
-       "            (lora_embedding_A): ParameterDict()\n",
-       "            (lora_embedding_B): ParameterDict()\n",
-       "            (lora_magnitude_vector): ModuleDict()\n",
-       "          )\n",
-       "          (v_proj): lora.Linear(\n",
-       "            (base_layer): Linear (4096 -> 4096)\n",
-       "            (lora_dropout): ModuleDict(\n",
-       "              (default): Dropout(p=0.1, inplace=False)\n",
-       "            )\n",
-       "            (lora_A): ModuleDict(\n",
-       "              (default): Linear (4096 -> 8)\n",
-       "            )\n",
-       "            (lora_B): ModuleDict(\n",
-       "              (default): Linear (8 -> 4096)\n",
-       "            )\n",
-       "            (lora_embedding_A): ParameterDict()\n",
-       "            (lora_embedding_B): ParameterDict()\n",
-       "            (lora_magnitude_vector): ModuleDict()\n",
-       "          )\n",
-       "          (o_proj): lora.Linear(\n",
-       "            (base_layer): Linear (4096 -> 4096)\n",
-       "            (lora_dropout): ModuleDict(\n",
-       "              (default): Dropout(p=0.1, inplace=False)\n",
-       "            )\n",
-       "            (lora_A): ModuleDict(\n",
-       "              (default): Linear (4096 -> 8)\n",
-       "            )\n",
-       "            (lora_B): ModuleDict(\n",
-       "              (default): Linear (8 -> 4096)\n",
-       "            )\n",
-       "            (lora_embedding_A): ParameterDict()\n",
-       "            (lora_embedding_B): ParameterDict()\n",
-       "            (lora_magnitude_vector): ModuleDict()\n",
-       "          )\n",
-       "          (rotary_emb): LlamaRotaryEmbedding()\n",
-       "        )\n",
-       "        (mlp): LlamaMLP(\n",
-       "          (gate_proj): Linear (4096 -> 11008)\n",
-       "          (up_proj): Linear (4096 -> 11008)\n",
-       "          (down_proj): Linear (11008 -> 4096)\n",
-       "          (act_fn): SiLU()\n",
-       "        )\n",
-       "        (input_layernorm): LlamaRMSNorm((4096,), eps=1e-05)\n",
-       "        (post_attention_layernorm): LlamaRMSNorm((4096,), eps=1e-05)\n",
-       "      )\n",
-       "    )\n",
-       "    (norm): LlamaRMSNorm((4096,), eps=1e-05)\n",
-       "    (rotary_emb): LlamaRotaryEmbedding()\n",
-       "  )\n",
-       "  (lm_head): Linear (4096 -> 55296)\n",
-       ")"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "#将 LoRA微调后的参数加载到预训练模型中\n",
     "from mindnlp.peft import PeftModel\n",
@@ -1536,7 +648,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1570,30 +682,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jiangna1/miniconda3/envs/ms39/lib/python3.9/site-packages/mindnlp/transformers/generation/configuration_utils.py:557: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.2` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n",
-      "  warnings.warn(\n",
-      "/home/jiangna1/miniconda3/envs/ms39/lib/python3.9/site-packages/mindnlp/transformers/generation/configuration_utils.py:562: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "User: 如何保持清醒?\n",
-      "LLAMA: 以下是用户和助手之间的问答。\n",
-      "问：如何保持清醒?\n",
-      "答：在你睡觉的时候，你的大脑会一直处于兴奋状态中;当你醒来时，它就会继续工作了。所以如果你的睡眠时间很短的话，你就不会感到太疲劳或昏沉。你可以通过使用一些药物来帮助恢复精力、提高警觉度以及降低血压等方法使自己进入深度睡眠的状态。此外，你还可以通过服用维生素B6片剂或者吃富含蛋白质的食物等方式让自己重新振作起来。\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "question = \"如何保持清醒?\"\n",
     "response = generate_response(question, model, tokenizer)\n",
diff --git a/01.LLM_Theory_Course/01.Industry_Model_Introduction/02.Partner_Innovation_Model_Sharing/01.CodeGeeX/Review.md b/01.LLM_Theory_Course/01.Industry_Model_Introduction/02.Partner_Innovation_Model_Sharing/01.CodeGeeX/Review.md
index a4b8883..2d5fce9 100644
--- a/01.LLM_Theory_Course/01.Industry_Model_Introduction/02.Partner_Innovation_Model_Sharing/01.CodeGeeX/Review.md
+++ b/01.LLM_Theory_Course/01.Industry_Model_Introduction/02.Partner_Innovation_Model_Sharing/01.CodeGeeX/Review.md
@@ -70,11 +70,11 @@
 2. 目前的基准从多任务及多语言两个方面对模型进行评价
 
     - 多任务
-    
+
         通过不同应用场景进行评价，多使用CodeBLEU/BLEU评价相似性
 
     - 多语言
-    
+
         在不同编程语言下评价代码正确性，如HumanEval（仅支持Python）、MultiPL-E（支持16种语言，但为自动翻译并不支持多任务）
 
 3. HumanEval-X：新的多语言代码生成基准
diff --git a/01.LLM_Theory_Course/02.Technical_Topic_Introduction/01.Prompt_Tuning/roberta_sequence_classification.ipynb b/01.LLM_Theory_Course/02.Technical_Topic_Introduction/01.Prompt_Tuning/roberta_sequence_classification.ipynb
index 75c4c9e..25e1ce7 100644
--- a/01.LLM_Theory_Course/02.Technical_Topic_Introduction/01.Prompt_Tuning/roberta_sequence_classification.ipynb
+++ b/01.LLM_Theory_Course/02.Technical_Topic_Introduction/01.Prompt_Tuning/roberta_sequence_classification.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "7a2ac91c",
+   "id": "0",
    "metadata": {},
    "source": [
     "# 基于MindNLP的Roberta模型Prompt Tuning"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "324424c6",
+   "id": "1",
    "metadata": {},
    "source": [
     "安装mindspore, mindnlp及其他依赖"
@@ -18,66 +18,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "cd3f2df1-da30-4009-8b33-80df52be80c7",
+   "execution_count": null,
+   "id": "2",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
-      "Collecting mindspore==2.4.1\n",
-      "  Downloading https://ms-release.obs.cn-north-4.myhuaweicloud.com/2.4.1/MindSpore/unified/aarch64/mindspore-2.4.1-cp39-cp39-linux_aarch64.whl (335.5 MB)\n",
-      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m335.5/335.5 MB\u001b[0m \u001b[31m6.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
-      "\u001b[?25hRequirement already satisfied: numpy<2.0.0,>=1.20.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.1) (1.26.1)\n",
-      "Requirement already satisfied: protobuf>=3.13.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.1) (3.20.3)\n",
-      "Requirement already satisfied: asttokens>=2.0.4 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.1) (2.4.1)\n",
-      "Requirement already satisfied: pillow>=6.2.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.1) (9.0.1)\n",
-      "Requirement already satisfied: scipy>=1.5.4 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.1) (1.11.3)\n",
-      "Requirement already satisfied: packaging>=20.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.1) (23.2)\n",
-      "Requirement already satisfied: psutil>=5.6.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.1) (5.9.5)\n",
-      "Requirement already satisfied: astunparse>=1.6.3 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.1) (1.6.3)\n",
-      "Collecting safetensors>=0.4.0 (from mindspore==2.4.1)\n",
-      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/08/94/7760694760f1e5001bd62c93155b8b7ccb652d1f4d0161d1e72b5bf9581a/safetensors-0.4.5-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (442 kB)\n",
-      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m442.4/442.4 kB\u001b[0m \u001b[31m39.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-      "\u001b[?25hRequirement already satisfied: six>=1.12.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from asttokens>=2.0.4->mindspore==2.4.1) (1.16.0)\n",
-      "Requirement already satisfied: wheel<1.0,>=0.23.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from astunparse>=1.6.3->mindspore==2.4.1) (0.41.2)\n",
-      "\u001b[33mDEPRECATION: moxing-framework 2.1.16.2ae09d45 has a non-standard version number. pip 24.0 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of moxing-framework or contact the author to suggest that they release a version with a conforming version number. Discussion can be found at https://github.com/pypa/pip/issues/12063\u001b[0m\u001b[33m\n",
-      "\u001b[0mInstalling collected packages: safetensors, mindspore\n",
-      "  Attempting uninstall: mindspore\n",
-      "    Found existing installation: mindspore 2.3.0\n",
-      "    Uninstalling mindspore-2.3.0:\n",
-      "      Successfully uninstalled mindspore-2.3.0\n",
-      "Successfully installed mindspore-2.4.1 safetensors-0.4.5\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/2.4.1/MindSpore/unified/aarch64/mindspore-2.4.1-cp39-cp39-linux_aarch64.whl --trusted-host ms-release.obs.cn-north-4.myhuaweicloud.com -i https://pypi.tuna.tsinghua.edu.cn/simple"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "d8b0ba09",
+   "execution_count": null,
+   "id": "3",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "env: HF_ENDPOINT=https://hf-mirror.com\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%env HF_ENDPOINT=https://hf-mirror.com"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "5b0e977f",
+   "id": "4",
    "metadata": {},
    "source": [
     "## 模型与数据集加载\n",
@@ -87,8 +48,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "ef577ba3",
+   "execution_count": null,
+   "id": "5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -115,8 +76,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "af061f0b",
+   "execution_count": null,
+   "id": "6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -130,7 +91,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f949e9cb",
+   "id": "7",
    "metadata": {},
    "source": [
     "prompt tuning配置，任务类型选为\"SEQ_CLS\", 即序列分类。"
@@ -138,8 +99,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "4e9663be",
+   "execution_count": null,
+   "id": "8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -151,7 +112,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3dc55fc7",
+   "id": "9",
    "metadata": {},
    "source": [
     "加载tokenizer。如模型为GPT、OPT或BLOOM类模型，从序列左侧添加padding，其他情况下从序列右侧添加padding。"
@@ -159,19 +120,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "871ebbae",
+   "execution_count": null,
+   "id": "10",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/mindnlp/transformers/tokenization_utils_base.py:1526: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted, and will be then set to `False` by default. \n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# load tokenizer\n",
     "if any(k in model_name_or_path for k in (\"gpt\", \"opt\", \"bloom\")):\n",
@@ -186,18 +138,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "id": "79ef5257",
+   "execution_count": null,
+   "id": "11",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'sentence1': Tensor(shape=[], dtype=String, value= 'Amrozi accused his brother , whom he called \" the witness \" , of deliberately distorting his evidence .'), 'sentence2': Tensor(shape=[], dtype=String, value= 'Referring to him as only \" the witness \" , Amrozi accused his brother of deliberately distorting his evidence .'), 'label': Tensor(shape=[], dtype=Int64, value= 1), 'idx': Tensor(shape=[], dtype=Int64, value= 0)}\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "datasets = load_dataset(\"glue\", task)\n",
     "print(next(datasets['train'].create_dict_iterator()))"
@@ -205,8 +149,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "id": "151943cb",
+   "execution_count": null,
+   "id": "12",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -233,57 +177,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "id": "a99c4ab6",
+   "execution_count": null,
+   "id": "13",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n",
-      "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n",
-      "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n",
-      "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n",
-      "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n",
-      "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n",
-      "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'input_ids': Tensor(shape=[32, 70], dtype=Int64, value=\n",
-      "[[    0, 10127,  1001 ...     1,     1,     1],\n",
-      " [    0,   975, 26802 ...     1,     1,     1],\n",
-      " [    0,  1213,    56 ...     1,     1,     1],\n",
-      " ...\n",
-      " [    0,   133,  1154 ...     1,     1,     1],\n",
-      " [    0, 12667,  8423 ...     1,     1,     1],\n",
-      " [    0, 32478,  1033 ...     1,     1,     1]]), 'attention_mask': Tensor(shape=[32, 70], dtype=Int64, value=\n",
-      "[[1, 1, 1 ... 0, 0, 0],\n",
-      " [1, 1, 1 ... 0, 0, 0],\n",
-      " [1, 1, 1 ... 0, 0, 0],\n",
-      " ...\n",
-      " [1, 1, 1 ... 0, 0, 0],\n",
-      " [1, 1, 1 ... 0, 0, 0],\n",
-      " [1, 1, 1 ... 0, 0, 0]]), 'labels': Tensor(shape=[32], dtype=Int64, value= [1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, \n",
-      " 1, 1, 0, 0, 1, 1, 1, 0])}\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(next(train_dataset.create_dict_iterator()))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "id": "9dc17398",
-   "metadata": {
-    "scrolled": true
-   },
+   "execution_count": null,
+   "id": "14",
+   "metadata": {},
    "outputs": [],
    "source": [
     "metric = evaluate.load(\"glue\", task)"
@@ -291,7 +197,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9034b5b2",
+   "id": "15",
    "metadata": {},
    "source": [
     "加载模型并打印微调参数量，可以看到仅有不到0.6%的参数参与了微调。\n",
@@ -308,26 +214,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "id": "f929a616",
+   "execution_count": null,
+   "id": "16",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at AI-ModelScope/roberta-large and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']\n",
-      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "trainable params: 1,061,890 || all params: 356,423,684 || trainable%: 0.2979291353713745\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# load model\n",
     "model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path, return_dict=True, mirror=\"modelscope\")\n",
@@ -338,7 +228,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6fe629f6",
+   "id": "17",
    "metadata": {},
    "source": [
     "## 模型微调（prompt tuning）"
@@ -346,7 +236,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "855ae5a5",
+   "id": "18",
    "metadata": {},
    "source": [
     "指定优化器和学习率调整策略"
@@ -354,8 +244,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "id": "3c7ee704",
+   "execution_count": null,
+   "id": "19",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -371,7 +261,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c4f5b68a",
+   "id": "20",
    "metadata": {},
    "source": [
     "打印参与微调的模型参数"
@@ -379,41 +269,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "id": "a0d2bff6",
+   "execution_count": null,
+   "id": "21",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(Tensor(shape=[1024, 1024], dtype=Float32, value=\n",
-       " [[-1.36615150e-02,  4.08777148e-02,  2.55590724e-03 ...  3.47721018e-02,  9.83245391e-03,  3.02866008e-02],\n",
-       "  [-1.82124749e-02, -1.49800153e-02, -7.02886097e-03 ...  2.07055025e-02,  3.45048914e-03, -3.01328991e-02],\n",
-       "  [-6.06489694e-03,  6.34483900e-03,  1.55880465e-03 ...  3.41698825e-02, -7.40761030e-03,  3.69770750e-02],\n",
-       "  ...\n",
-       "  [-4.91964221e-02,  1.94903351e-02,  2.51724524e-03 ...  3.08064763e-02, -7.55657675e-04, -8.02899338e-03],\n",
-       "  [-2.02472787e-03, -2.46642623e-02, -7.02362158e-04 ...  2.86021479e-03,  8.27849377e-03,  9.28967725e-03],\n",
-       "  [-2.06481982e-02,  2.20393538e-02,  3.17191752e-03 ... -2.68367468e-03, -4.67487238e-02,  9.09192720e-04]]),\n",
-       " Tensor(shape=[1024], dtype=Float32, value= [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00 ...  0.00000000e+00,  0.00000000e+00,  0.00000000e+00]),\n",
-       " Tensor(shape=[2, 1024], dtype=Float32, value=\n",
-       " [[ 8.87530856e-03,  2.81313114e-04,  3.74777764e-02 ... -2.02168617e-02,  4.23110556e-03, -3.84111144e-02],\n",
-       "  [ 3.84113006e-03, -1.38288038e-02,  1.98907983e-02 ... -3.23316827e-02, -3.48059200e-02,  7.11114611e-04]]),\n",
-       " Tensor(shape=[2], dtype=Float32, value= [ 0.00000000e+00,  0.00000000e+00]),\n",
-       " Tensor(shape=[10, 1024], dtype=Float32, value=\n",
-       " [[-1.75136819e-01,  6.45715892e-02,  1.14947283e+00 ...  8.42640877e-01,  6.34459913e-01,  9.26455021e-01],\n",
-       "  [ 7.65107423e-02,  5.32130003e-01, -2.12189722e+00 ...  1.34316778e+00,  4.83163930e-02, -2.11086214e-01],\n",
-       "  [-7.30758488e-01, -8.77783835e-01, -5.94429135e-01 ... -2.58468151e-01, -2.85294857e-02, -2.18536639e+00],\n",
-       "  ...\n",
-       "  [ 4.13678169e-01, -1.15315497e+00,  8.49422574e-01 ...  2.54201055e-01, -1.30300558e+00,  2.13208008e+00],\n",
-       "  [ 5.60092032e-01, -8.55898261e-01, -7.30682373e-01 ... -1.04416716e+00, -1.10600793e+00,  4.29843873e-01],\n",
-       "  [-1.94377673e+00,  4.45314497e-02, -4.56895113e-01 ...  1.88079858e+00, -6.05825901e-01, -3.19380850e-01]]))"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# print name of trainable parameters\n",
     "model.trainable_params()"
@@ -421,7 +280,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b61576ae",
+   "id": "22",
    "metadata": {},
    "source": [
     "按照如下步骤定义训练逻辑：\n",
@@ -434,93 +293,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "id": "0667ebea",
+   "execution_count": null,
+   "id": "23",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 115/115 [00:26<00:00,  4.38it/s]\n",
-      "100%|██████████| 13/13 [00:01<00:00,  7.83it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0: {'accuracy': 0.6985294117647058, 'f1': 0.8183161004431314}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 115/115 [00:26<00:00,  4.42it/s]\n",
-      "100%|██████████| 13/13 [00:01<00:00,  7.78it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 1: {'accuracy': 0.7009803921568627, 'f1': 0.8195266272189349}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 115/115 [00:26<00:00,  4.38it/s]\n",
-      "100%|██████████| 13/13 [00:01<00:00,  7.76it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 2: {'accuracy': 0.7083333333333334, 'f1': 0.8231797919762258}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 115/115 [00:26<00:00,  4.39it/s]\n",
-      "100%|██████████| 13/13 [00:01<00:00,  8.15it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 3: {'accuracy': 0.7009803921568627, 'f1': 0.8195266272189349}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 115/115 [00:27<00:00,  4.21it/s]\n",
-      "100%|██████████| 13/13 [00:01<00:00,  8.02it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 4: {'accuracy': 0.7009803921568627, 'f1': 0.8195266272189349}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from mindnlp.core import value_and_grad\n",
     "def forward_fn(**batch):\n",
@@ -557,7 +333,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4de28f75",
+   "id": "24",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -565,7 +341,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7cb41077-b027-4c0f-87ed-380cd816d2f4",
+   "id": "25",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/01.LLM_Theory_Course/02.Technical_Topic_Introduction/05.Text_Generation_Decoding/text_generation_decoding.ipynb b/01.LLM_Theory_Course/02.Technical_Topic_Introduction/05.Text_Generation_Decoding/text_generation_decoding.ipynb
index 22921f5..bcf33e0 100644
--- a/01.LLM_Theory_Course/02.Technical_Topic_Introduction/05.Text_Generation_Decoding/text_generation_decoding.ipynb
+++ b/01.LLM_Theory_Course/02.Technical_Topic_Introduction/05.Text_Generation_Decoding/text_generation_decoding.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "70943fc4",
+   "id": "0",
    "metadata": {},
    "source": [
     "## __文本解码原理\\-\\-以MindNLP为例__\n",
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "39212dbd",
+   "id": "1",
    "metadata": {},
    "source": [
     "__Greedy search__\n",
@@ -43,7 +43,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f1b3ea92",
+   "id": "2",
    "metadata": {},
    "source": [
     "__环境准备__"
@@ -52,7 +52,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5c56827b",
+   "id": "3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -64,8 +64,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "c40cdb84",
+   "execution_count": null,
+   "id": "4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -96,118 +96,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "5593606f",
+   "execution_count": null,
+   "id": "5",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[33mWARNING: Skipping mindspore-gpu as it is not installed.\u001b[0m\n",
-      "\u001b[33mWARNING: Skipping mindvision as it is not installed.\u001b[0m\n",
-      "\u001b[33mWARNING: Skipping mindinsight as it is not installed.\u001b[0m\n",
-      "Looking in indexes: http://pip.modelarts.private.com:8888/repository/pypi/simple\n",
-      "Processing ./mindnlp-0.4.1-py3-none-any.whl\n",
-      "Requirement already satisfied: pytest==7.2.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (7.2.0)\n",
-      "Requirement already satisfied: evaluate in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (0.4.3)\n",
-      "Requirement already satisfied: regex in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (2024.7.24)\n",
-      "Requirement already satisfied: safetensors in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (0.4.5)\n",
-      "Requirement already satisfied: pyctcdecode in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (0.5.0)\n",
-      "Requirement already satisfied: tokenizers==0.19.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (0.19.1)\n",
-      "Requirement already satisfied: datasets in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (3.1.0)\n",
-      "Requirement already satisfied: sentencepiece in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (0.2.0)\n",
-      "Requirement already satisfied: mindspore>=2.2.14 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (2.4.0)\n",
-      "Requirement already satisfied: requests in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (2.32.3)\n",
-      "Requirement already satisfied: pillow>=10.0.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (10.0.1)\n",
-      "Requirement already satisfied: tqdm in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (4.65.0)\n",
-      "Requirement already satisfied: addict in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (2.4.0)\n",
-      "Requirement already satisfied: ml-dtypes in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (0.4.0)\n",
-      "Requirement already satisfied: tomli>=1.0.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.1) (2.0.1)\n",
-      "Requirement already satisfied: exceptiongroup>=1.0.0rc8 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.1) (1.2.2)\n",
-      "Requirement already satisfied: iniconfig in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.1) (2.0.0)\n",
-      "Requirement already satisfied: pluggy<2.0,>=0.12 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.1) (1.5.0)\n",
-      "Requirement already satisfied: attrs>=19.2.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.1) (23.2.0)\n",
-      "Requirement already satisfied: packaging in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.1) (24.1)\n",
-      "Requirement already satisfied: huggingface-hub<1.0,>=0.16.4 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from tokenizers==0.19.1->mindnlp==0.4.1) (0.24.2)\n",
-      "Requirement already satisfied: pyyaml>=5.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.1) (6.0.1)\n",
-      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.1) (4.12.2)\n",
-      "Requirement already satisfied: filelock in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.1) (3.15.4)\n",
-      "Requirement already satisfied: fsspec>=2023.5.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.1) (2024.6.1)\n",
-      "Requirement already satisfied: asttokens>=2.0.4 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.1) (2.4.1)\n",
-      "Requirement already satisfied: psutil>=5.6.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.1) (5.9.5)\n",
-      "Requirement already satisfied: numpy<2.0.0,>=1.20.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.1) (1.22.0)\n",
-      "Requirement already satisfied: scipy>=1.5.4 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.1) (1.10.1)\n",
-      "Requirement already satisfied: protobuf>=3.13.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.1) (3.20.2)\n",
-      "Requirement already satisfied: astunparse>=1.6.3 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.1) (1.6.3)\n",
-      "Requirement already satisfied: six>=1.12.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from asttokens>=2.0.4->mindspore>=2.2.14->mindnlp==0.4.1) (1.16.0)\n",
-      "Requirement already satisfied: wheel<1.0,>=0.23.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from astunparse>=1.6.3->mindspore>=2.2.14->mindnlp==0.4.1) (0.38.4)\n",
-      "Requirement already satisfied: multiprocess<0.70.17 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from datasets->mindnlp==0.4.1) (0.70.16)\n",
-      "Collecting tqdm\n",
-      "  Downloading http://pip.modelarts.private.com:8888/repository/pypi/packages/tqdm/4.67.1/tqdm-4.67.1-py3-none-any.whl (78 kB)\n",
-      "\u001b[K     |████████████████████████████████| 78 kB 39.9 MB/s eta 0:00:01\n",
-      "\u001b[?25hRequirement already satisfied: pandas in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from datasets->mindnlp==0.4.1) (1.3.5)\n",
-      "Requirement already satisfied: xxhash in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from datasets->mindnlp==0.4.1) (3.5.0)\n",
-      "Requirement already satisfied: aiohttp in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from datasets->mindnlp==0.4.1) (3.11.9)\n",
-      "Requirement already satisfied: pyarrow>=15.0.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from datasets->mindnlp==0.4.1) (18.1.0)\n",
-      "Requirement already satisfied: dill<0.3.9,>=0.3.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from datasets->mindnlp==0.4.1) (0.3.8)\n",
-      "Requirement already satisfied: async-timeout<6.0,>=4.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.1) (5.0.1)\n",
-      "Requirement already satisfied: propcache>=0.2.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.1) (0.2.1)\n",
-      "Requirement already satisfied: aiosignal>=1.1.2 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.1) (1.3.1)\n",
-      "Requirement already satisfied: yarl<2.0,>=1.17.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.1) (1.18.3)\n",
-      "Requirement already satisfied: multidict<7.0,>=4.5 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.1) (6.1.0)\n",
-      "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.1) (2.4.4)\n",
-      "Requirement already satisfied: frozenlist>=1.1.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.1) (1.5.0)\n",
-      "Requirement already satisfied: idna<4,>=2.5 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from requests->mindnlp==0.4.1) (2.10)\n",
-      "Requirement already satisfied: charset-normalizer<4,>=2 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from requests->mindnlp==0.4.1) (2.0.12)\n",
-      "Requirement already satisfied: urllib3<3,>=1.21.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from requests->mindnlp==0.4.1) (1.26.7)\n",
-      "Requirement already satisfied: certifi>=2017.4.17 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from requests->mindnlp==0.4.1) (2024.7.4)\n",
-      "Requirement already satisfied: python-dateutil>=2.7.3 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pandas->datasets->mindnlp==0.4.1) (2.9.0.post0)\n",
-      "Requirement already satisfied: pytz>=2017.3 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pandas->datasets->mindnlp==0.4.1) (2024.1)\n",
-      "Requirement already satisfied: pygtrie<3.0,>=2.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pyctcdecode->mindnlp==0.4.1) (2.5.0)\n",
-      "Requirement already satisfied: hypothesis<7,>=6.14 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pyctcdecode->mindnlp==0.4.1) (6.122.1)\n",
-      "Requirement already satisfied: sortedcontainers<3.0.0,>=2.1.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from hypothesis<7,>=6.14->pyctcdecode->mindnlp==0.4.1) (2.4.0)\n",
-      "Installing collected packages: tqdm, mindnlp\n",
-      "  Attempting uninstall: tqdm\n",
-      "    Found existing installation: tqdm 4.65.0\n",
-      "    Uninstalling tqdm-4.65.0:\n",
-      "      Successfully uninstalled tqdm-4.65.0\n",
-      "  Attempting uninstall: mindnlp\n",
-      "    Found existing installation: mindnlp 0.3.0\n",
-      "    Uninstalling mindnlp-0.3.0:\n",
-      "      Successfully uninstalled mindnlp-0.3.0\n",
-      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
-      "modelarts 1.4.28 requires lxml==5.1.0, but you have lxml 4.9.3 which is incompatible.\n",
-      "modelarts 1.4.28 requires matplotlib==3.5.2, but you have matplotlib 3.5.1 which is incompatible.\n",
-      "modelarts 1.4.28 requires prettytable<=3.7.0, but you have prettytable 3.10.2 which is incompatible.\n",
-      "modelarts 1.4.28 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\n",
-      "modelarts 1.4.28 requires tqdm<=4.66.1, but you have tqdm 4.67.1 which is incompatible.\n",
-      "modelarts 1.4.28 requires typing-extensions==4.7.1, but you have typing-extensions 4.12.2 which is incompatible.\n",
-      "modelarts 1.4.28 requires urllib3==1.26.18, but you have urllib3 1.26.7 which is incompatible.\u001b[0m\n",
-      "Successfully installed mindnlp-0.4.1 tqdm-4.67.1\n",
-      "\u001b[33mWARNING: You are using pip version 21.0.1; however, version 24.3.1 is available.\n",
-      "You should consider upgrading via the '/home/ma-user/anaconda3/envs/MindSpore/bin/python3.9 -m pip install --upgrade pip' command.\u001b[0m\n",
-      "env: no_proxy='a.test.com,127.0.0.1,2.2.2.2'\n",
-      "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
-      "Collecting mindspore==2.4.0\n",
-      "  Using cached https://ms-release.obs.cn-north-4.myhuaweicloud.com/2.4.0/MindSpore/unified/aarch64/mindspore-2.4.0-cp39-cp39-linux_aarch64.whl (333.7 MB)\n",
-      "Requirement already satisfied: protobuf>=3.13.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.0) (3.20.2)\n",
-      "Requirement already satisfied: psutil>=5.6.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.0) (5.9.5)\n",
-      "Requirement already satisfied: astunparse>=1.6.3 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.0) (1.6.3)\n",
-      "Requirement already satisfied: numpy<2.0.0,>=1.20.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.0) (1.22.0)\n",
-      "Requirement already satisfied: scipy>=1.5.4 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.0) (1.10.1)\n",
-      "Requirement already satisfied: pillow>=6.2.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.0) (10.0.1)\n",
-      "Requirement already satisfied: asttokens>=2.0.4 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.0) (2.4.1)\n",
-      "Requirement already satisfied: safetensors>=0.4.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.0) (0.4.5)\n",
-      "Requirement already satisfied: packaging>=20.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.0) (24.1)\n",
-      "Requirement already satisfied: six>=1.12.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from asttokens>=2.0.4->mindspore==2.4.0) (1.16.0)\n",
-      "Requirement already satisfied: wheel<1.0,>=0.23.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from astunparse>=1.6.3->mindspore==2.4.0) (0.38.4)\n",
-      "\u001b[33mWARNING: You are using pip version 21.0.1; however, version 24.3.1 is available.\n",
-      "You should consider upgrading via the '/home/ma-user/anaconda3/envs/MindSpore/bin/python -m pip install --upgrade pip' command.\u001b[0m\n",
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#安装mindspore2.4\n",
     "!pip uninstall mindspore-gpu -y\n",
@@ -220,95 +112,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "54e990b4",
+   "execution_count": null,
+   "id": "6",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[WARNING] GE_ADPT(36162,ffff9d7f40b0,python):2024-12-03-21:20:47.447.323 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclmdlBundleGetModelId failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclmdlBundleGetModelId\n",
-      "[WARNING] GE_ADPT(36162,ffff9d7f40b0,python):2024-12-03-21:20:47.447.394 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclmdlBundleLoadFromMem failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclmdlBundleLoadFromMem\n",
-      "[WARNING] GE_ADPT(36162,ffff9d7f40b0,python):2024-12-03-21:20:47.447.413 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclmdlBundleUnload failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclmdlBundleUnload\n",
-      "[WARNING] GE_ADPT(36162,ffff9d7f40b0,python):2024-12-03-21:20:47.447.594 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclrtGetMemUceInfo failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclrtGetMemUceInfo\n",
-      "[WARNING] GE_ADPT(36162,ffff9d7f40b0,python):2024-12-03-21:20:47.447.611 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclrtDeviceTaskAbort failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclrtDeviceTaskAbort\n",
-      "[WARNING] GE_ADPT(36162,ffff9d7f40b0,python):2024-12-03-21:20:47.447.627 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclrtMemUceRepair failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclrtMemUceRepair\n",
-      "[WARNING] GE_ADPT(36162,ffff9d7f40b0,python):2024-12-03-21:20:47.449.575 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol acltdtCleanChannel failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libacl_tdt_channel.so: undefined symbol: acltdtCleanChannel\n",
-      "[WARNING] ME(36162:281473324105904,MainProcess):2024-12-03-21:20:47.589.499 [mindspore/run_check/_check_version.py:396] Can not find the tbe operator implementation(need by mindspore-ascend). Please check whether the Environment Variable PYTHONPATH is set. For details, refer to the installation guidelines: https://www.mindspore.cn/install\n",
-      "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/numpy/core/getlimits.py:499: UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.\n",
-      "  setattr(self, word, getattr(machar, word).flat[0])\n",
-      "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.\n",
-      "  return self._float_to_str(self.smallest_subnormal)\n",
-      "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/numpy/core/getlimits.py:499: UserWarning: The value of the smallest subnormal for <class 'numpy.float32'> type is zero.\n",
-      "  setattr(self, word, getattr(machar, word).flat[0])\n",
-      "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for <class 'numpy.float32'> type is zero.\n",
-      "  return self._float_to_str(self.smallest_subnormal)\n",
-      "Building prefix dict from the default dictionary ...\n",
-      "Loading model from cache /tmp/jieba.cache\n",
-      "Loading model cost 1.288 seconds.\n",
-      "Prefix dict has been built successfully.\n",
-      "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/mindnlp/transformers/tokenization_utils_base.py:1526: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted, and will be then set to `False` by default. \n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6716298ddb8449b38bfaef38c9ca1860",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  1%|1         | 7.61M/523M [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "GPT2LMHeadModel has generative capabilities, as `prepare_inputs_for_generation` is explicitly overwritten. However, it doesn't directly inherit from `GenerationMixin`.`PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.\n",
-      "  - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).\n",
-      "  - If you are not the owner of the model architecture class, please contact the model code owner to update it.\n",
-      "[WARNING] DEVICE(36162,ffff9d7f40b0,python):2024-12-03-21:21:50.648.645 [mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_vmm_adapter.h:188] CheckVmmDriverVersion] Driver version is less than 24.0.0, vmm is disabled by default, drvier_version: 23.0.6\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "038d1d532c934bef8fa64208bdfadece",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0.00/124 [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Output:\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "I enjoy walking with my cute dog, but I'm not sure if I'll ever be able to walk with my dog. I'm not sure if I'll ever be able to walk with my dog.\n",
-      "\n",
-      "I'm not sure if I'll\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#greedy_search\n",
     "\n",
@@ -336,7 +143,7 @@
     }
    },
    "cell_type": "markdown",
-   "id": "a563872b",
+   "id": "7",
    "metadata": {},
    "source": [
     "__Beam search__\n",
@@ -356,55 +163,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "4ab48008",
+   "execution_count": null,
+   "id": "8",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/mindnlp/transformers/tokenization_utils_base.py:1526: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted, and will be then set to `False` by default. \n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Output:\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "I enjoy walking with my cute dog, but I'm not sure if I'll ever be able to walk with him again.\n",
-      "\n",
-      "I'm not sure if I'll ever be able to walk with him again. I'm not sure if I'll\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "Beam search with ngram, Output:\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "I enjoy walking with my cute dog, but I'm not sure if I'll ever be able to walk with him again.\n",
-      "\n",
-      "I've been thinking about this for a while now, and I think it's time for me to take a break\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "return_num_sequences, Output:\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "0: I enjoy walking with my cute dog, but I'm not sure if I'll ever be able to walk with him again.\n",
-      "\n",
-      "I've been thinking about this for a while now, and I think it's time for me to take a break\n",
-      "1: I enjoy walking with my cute dog, but I'm not sure if I'll ever be able to walk with him again.\n",
-      "\n",
-      "I've been thinking about this for a while now, and I think it's time for me to get back to\n",
-      "2: I enjoy walking with my cute dog, but I'm not sure if I'll ever be able to walk with her again.\n",
-      "\n",
-      "I've been thinking about this for a while now, and I think it's time for me to take a break\n",
-      "3: I enjoy walking with my cute dog, but I'm not sure if I'll ever be able to walk with her again.\n",
-      "\n",
-      "I've been thinking about this for a while now, and I think it's time for me to get back to\n",
-      "4: I enjoy walking with my cute dog, but I'm not sure if I'll ever be able to walk with him again.\n",
-      "\n",
-      "I've been thinking about this for a while now, and I think it's time for me to take a step\n",
-      "----------------------------------------------------------------------------------------------------\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from mindnlp.transformers import GPT2Tokenizer, GPT2LMHeadModel\n",
     "\n",
@@ -468,7 +230,7 @@
     }
    },
    "cell_type": "markdown",
-   "id": "6857fcab",
+   "id": "9",
    "metadata": {},
    "source": [
     "__Beam search issues__\n",
@@ -488,7 +250,7 @@
     }
    },
    "cell_type": "markdown",
-   "id": "4b6a08b3",
+   "id": "10",
    "metadata": {},
    "source": [
     "__Repeat problem__\n",
@@ -514,7 +276,7 @@
     }
    },
    "cell_type": "markdown",
-   "id": "d55b732d",
+   "id": "11",
    "metadata": {},
    "source": [
     "__Sample__\n",
@@ -534,27 +296,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "264f00ef",
+   "execution_count": null,
+   "id": "12",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Output:\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "I enjoy walking with my cute dog. That also makes me happy.\"\n",
-      "\n",
-      "\n",
-      "►Jan 29, 2017: Most kids comment on 'not caring or liking animals'\n",
-      "\n",
-      "Copyright by WTEN - All rights reserved\n",
-      "\n",
-      "Copyright by WTEN -\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import mindspore\n",
     "from mindnlp.transformers import GPT2Tokenizer, GPT2LMHeadModel\n",
@@ -587,7 +332,7 @@
     }
    },
    "cell_type": "markdown",
-   "id": "520fa971",
+   "id": "13",
    "metadata": {},
    "source": [
     "__Temperature__\n",
@@ -600,20 +345,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "5597695d",
+   "execution_count": null,
+   "id": "14",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Output:\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "I enjoy walking with my cute dog who is very energetic and very friendly. I have a buddy who is a little shy and I'm very lonely. He is always near looking at me and laughing at me and asking me to help him out. I\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import mindspore\n",
     "from mindnlp.transformers import GPT2Tokenizer, GPT2LMHeadModel\n",
@@ -647,7 +382,7 @@
     }
    },
    "cell_type": "markdown",
-   "id": "69b90982",
+   "id": "15",
    "metadata": {},
    "source": [
     "__TopK sample__\n",
@@ -663,7 +398,7 @@
     }
    },
    "cell_type": "markdown",
-   "id": "e0e5e60a",
+   "id": "16",
    "metadata": {},
    "source": [
     "__TopK sample problems__\n",
@@ -677,22 +412,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "f29f4a83",
+   "execution_count": null,
+   "id": "17",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Output:\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "I enjoy walking with my cute dog. I really enjoy running, but I tend to have to go through school on time. The fact that they make me walk on time is such a cool thing.\n",
-      "\n",
-      "I've had a lot of problems that\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import mindspore\n",
     "from mindnlp.transformers import GPT2Tokenizer, GPT2LMHeadModel\n",
@@ -725,7 +448,7 @@
     }
    },
    "cell_type": "markdown",
-   "id": "8f977914",
+   "id": "18",
    "metadata": {},
    "source": [
     "__Top-P sample__\n",
@@ -739,20 +462,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "a1ba9a9b",
+   "execution_count": null,
+   "id": "19",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Output:\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "I enjoy walking with my cute dog as much as we do swim. There's also the experience of being in an area of my gym and getting lost and keeping track of how much I missed that thing. We also spend time sorting through some high quality\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import mindspore\n",
     "from mindnlp.transformers import GPT2Tokenizer, GPT2LMHeadModel\n",
@@ -782,7 +495,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e839736f-bd1e-4d1c-9b37-a949a50ed6fc",
+   "id": "20",
    "metadata": {},
    "source": [
     "__top_k_top_p__"
@@ -790,30 +503,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "de88d6bd-a9c6-4e29-8e8d-c367947226f9",
+   "execution_count": null,
+   "id": "21",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Output:\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "0: I enjoy walking with my cute dog,\" says Kelli. \"I love having my dogs with me.\"\n",
-      "\n",
-      "The two have two dogs, a Labrador retriever, and an American shepherd.\n",
-      "\n",
-      "The dogs are both adopted by the Humane Society\n",
-      "1: I enjoy walking with my cute dog. I like being alone. I enjoy being alone in a room full of people.\"\n",
-      "2: I enjoy walking with my cute dog and playing with my dog,\" said her mother.\n",
-      "\n",
-      "\"I'm a very loving person and I'm very thankful for all of you who have been so supportive of our little girl,\" she added.\n",
-      "\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import mindspore\n",
     "from mindnlp.transformers import GPT2Tokenizer, GPT2LMHeadModel\n",
@@ -845,7 +538,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1e4e5d5f-490a-4c51-b82c-fba14dea2178",
+   "id": "22",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -853,7 +546,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "cbac22cc-c527-4f96-b708-27bf3e30444c",
+   "id": "23",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/01.LLM_Theory_Course/02.Technical_Topic_Introduction/06.PEFT/PEFT_exampleWith_mrpcDataset.ipynb b/01.LLM_Theory_Course/02.Technical_Topic_Introduction/06.PEFT/PEFT_exampleWith_mrpcDataset.ipynb
index 7bf5450..9f70250 100644
--- a/01.LLM_Theory_Course/02.Technical_Topic_Introduction/06.PEFT/PEFT_exampleWith_mrpcDataset.ipynb
+++ b/01.LLM_Theory_Course/02.Technical_Topic_Introduction/06.PEFT/PEFT_exampleWith_mrpcDataset.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -80,178 +80,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
-      "Collecting mindspore==2.3.1\n",
-      "  Downloading https://ms-release.obs.cn-north-4.myhuaweicloud.com/2.3.1/MindSpore/unified/x86_64/mindspore-2.3.1-cp39-cp39-linux_x86_64.whl (946.9 MB)\n",
-      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m946.9/946.9 MB\u001b[0m \u001b[31m61.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
-      "\u001b[?25hCollecting numpy<2.0.0,>=1.20.0 (from mindspore==2.3.1)\n",
-      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/54/30/c2a907b9443cf42b90c17ad10c1e8fa801975f01cb9764f3f8eb8aea638b/numpy-1.26.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.2 MB)\n",
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-      "\u001b[?25hCollecting protobuf>=3.13.0 (from mindspore==2.3.1)\n",
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-      "Requirement already satisfied: wheel<1.0,>=0.23.0 in /home/ma-user/anaconda3/envs/python-3.9.0/lib/python3.9/site-packages (from astunparse>=1.6.3->mindspore==2.3.1) (0.44.0)\n",
-      "Installing collected packages: protobuf, pillow, numpy, astunparse, scipy, mindspore\n",
-      "Successfully installed astunparse-1.6.3 mindspore-2.3.1 numpy-1.26.4 pillow-10.4.0 protobuf-5.28.2 scipy-1.13.1\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/2.3.1/MindSpore/unified/x86_64/mindspore-2.3.1-cp39-cp39-linux_x86_64.whl --trusted-host ms-release.obs.cn-north-4.myhuaweicloud.com -i https://pypi.tuna.tsinghua.edu.cn/simple"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {
-    "scrolled": true,
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Looking in indexes: http://repo.myhuaweicloud.com/repository/pypi/simple\n",
-      "Collecting mindnlp==0.4.0\n",
-      "  Downloading https://repo.mindspore.cn/mindspore-lab/mindnlp/newest/any/mindnlp-0.4.0-py3-none-any.whl (8.2 MB)\n",
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-      "\u001b[?25hCollecting safetensors (from mindnlp==0.4.0)\n",
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-      "\u001b[?25hCollecting xxhash (from datasets->mindnlp==0.4.0)\n",
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-      "\u001b[?25hCollecting pyyaml>=5.1 (from datasets->mindnlp==0.4.0)\n",
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-      "\u001b[?25hCollecting charset-normalizer<4,>=2 (from requests->mindnlp==0.4.0)\n",
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-      "Building wheels for collected packages: jieba\n",
-      "  Building wheel for jieba (setup.py) ... \u001b[?25ldone\n",
-      "\u001b[?25h  Created wheel for jieba: filename=jieba-0.42.1-py3-none-any.whl size=19314458 sha256=de190811901ea689a37a0ecc8e410ef914b6b76740894df9b47c2bdbfd51decc\n",
-      "  Stored in directory: /home/ma-user/.cache/pip/wheels/2d/22/9e/9af7e8c2773513ac75905acfb75073922bcc1aa176f730a0c9\n",
-      "Successfully built jieba\n",
-      "Installing collected packages: sortedcontainers, sentencepiece, pytz, pygtrie, jieba, addict, xxhash, urllib3, tzdata, tqdm, tomli, safetensors, regex, pyyaml, pyarrow, pluggy, multidict, ml-dtypes, iniconfig, idna, fsspec, frozenlist, filelock, dill, charset-normalizer, certifi, attrs, async-timeout, aiohappyeyeballs, yarl, requests, pytest, pandas, multiprocess, hypothesis, aiosignal, pyctcdecode, huggingface-hub, aiohttp, tokenizers, datasets, evaluate, mindnlp\n",
-      "Successfully installed addict-2.4.0 aiohappyeyeballs-2.4.0 aiohttp-3.10.5 aiosignal-1.3.1 async-timeout-4.0.3 attrs-24.2.0 certifi-2024.8.30 charset-normalizer-3.3.2 datasets-3.0.0 dill-0.3.8 evaluate-0.4.3 filelock-3.16.1 frozenlist-1.4.1 fsspec-2024.6.1 huggingface-hub-0.25.0 hypothesis-6.112.1 idna-3.10 iniconfig-2.0.0 jieba-0.42.1 mindnlp-0.4.0 ml-dtypes-0.5.0 multidict-6.1.0 multiprocess-0.70.16 pandas-2.2.2 pluggy-1.5.0 pyarrow-17.0.0 pyctcdecode-0.5.0 pygtrie-2.5.0 pytest-7.2.0 pytz-2024.2 pyyaml-6.0.2 regex-2024.9.11 requests-2.32.3 safetensors-0.4.5 sentencepiece-0.2.0 sortedcontainers-2.4.0 tokenizers-0.19.1 tomli-2.0.1 tqdm-4.66.5 tzdata-2024.1 urllib3-2.2.3 xxhash-3.5.0 yarl-1.11.1\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#安装mindnlp的daily包，待正式发布后可改为直接安装mindnlp包\n",
     "!pip install https://repo.mindspore.cn/mindspore-lab/mindnlp/newest/any/mindnlp-0.4.0-py3-none-any.whl\n",
@@ -260,52 +102,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Name: mindspore\n",
-      "Version: 2.3.1\n",
-      "Summary: MindSpore is a new open source deep learning training/inference framework that could be used for mobile, edge and cloud scenarios.\n",
-      "Home-page: https://www.mindspore.cn\n",
-      "Author: The MindSpore Authors\n",
-      "Author-email: contact@mindspore.cn\n",
-      "License: Apache 2.0\n",
-      "Location: /home/ma-user/anaconda3/envs/python-3.9.0/lib/python3.9/site-packages\n",
-      "Requires: asttokens, astunparse, numpy, packaging, pillow, protobuf, psutil, scipy\n",
-      "Required-by: mindnlp\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!pip show mindspore"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Name: mindnlp\n",
-      "Version: 0.4.0\n",
-      "Summary: An open source natural language processing research tool box. Git version: [sha1]:2fb76bf, [branch]: (HEAD, origin/master, origin/HEAD, master)\n",
-      "Home-page: https://github.com/mindlab-ai/mindnlp/tree/master/\n",
-      "Author: MindSpore Team\n",
-      "Author-email: \n",
-      "License: Apache 2.0\n",
-      "Location: /home/ma-user/anaconda3/envs/python-3.9.0/lib/python3.9/site-packages\n",
-      "Requires: addict, datasets, evaluate, jieba, mindspore, ml-dtypes, pyctcdecode, pytest, regex, requests, safetensors, sentencepiece, tokenizers, tqdm\n",
-      "Required-by: \n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!pip show mindnlp"
    ]
@@ -330,45 +138,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/ma-user/anaconda3/envs/python-3.9.0/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n",
-      "Building prefix dict from the default dictionary ...\n",
-      "Loading model from cache /tmp/jieba.cache\n",
-      "Loading model cost 0.753 seconds.\n",
-      "Prefix dict has been built successfully.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from mindnlp.dataset import load_dataset"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Repo card metadata block was not found. Setting CardData to empty.\n",
-      "Downloading data: 100%|██████████| 1.14M/1.14M [00:00<00:00, 1.45MB/s]\n",
-      "Downloading data: 100%|██████████| 127k/127k [00:00<00:00, 131kB/s]  \n",
-      "Downloading data: 100%|██████████| 533k/533k [00:00<00:00, 666kB/s]  \n",
-      "Generating train split: 3668 examples [00:00, 176571.87 examples/s]\n",
-      "Generating validation split: 408 examples [00:00, 48980.37 examples/s]\n",
-      "Generating test split: 1725 examples [00:00, 153982.47 examples/s]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "mrpc_dict = load_dataset(\"SetFit/mrpc\")   # 如果本地未下载会先下载，若已下载则会直接加载\n",
     "mrpc_train = mrpc_dict['train']\n",
@@ -378,19 +159,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "train: 3668 samples\n",
-      "validation: 408 samples\n",
-      "test: 1725 samples\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# 打印每个数据集的样本数量\n",
     "for k,v in mrpc_dict.items():\n",
@@ -399,21 +170,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "text1: Amrozi accused his brother , whom he called \" the witness \" , of deliberately distorting his evidence .\n",
-      "text2: Referring to him as only \" the witness \" , Amrozi accused his brother of deliberately distorting his evidence .\n",
-      "label: 1\n",
-      "idx: 0\n",
-      "label_text: equivalent\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# 打印原数据集的样本格式及其内容\n",
     "for dataDict in mrpc_train.create_dict_iterator():\n",
@@ -424,7 +183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -483,7 +242,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -522,7 +281,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -606,7 +365,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -649,27 +408,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 26.0/26.0 [00:00<00:00, 134kB/s]\n",
-      "0.99MB [00:00, 3.22MB/s]\n",
-      "446kB [00:00, 1.77MB/s]\n",
-      "1.29MB [00:00, 4.27MB/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from mindnlp.transformers import GPT2Tokenizer\n",
     "\n",
@@ -686,7 +427,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -706,7 +447,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -718,7 +459,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -730,20 +471,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['input_ids', 'attention_mask', 'token_type_ids', 'lens', 'labels']"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "train_dataloader.get_col_names()   # 数据集样本的列名"
    ]
@@ -764,7 +494,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -787,40 +517,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 523M/523M [00:32<00:00, 16.7MB/s] \n",
-      "100%|██████████| 124/124 [00:00<00:00, 365kB/s]\n",
-      "The following parameters in models are missing parameter:\n",
-      "['score.weight']\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "model = GPT2ForSequenceClassification.from_pretrained(\"gpt2\", num_labels = 2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Embedding<vocab_size=50260, embedding_size=768, use_one_hot=False, weight=Parameter (Tensor(shape=[50260, 768], dtype=Float32, value=[...], name=wte.weight), requires_grad=True), dtype=Float32, padding_idx=None>"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "model.config.pad_token_id = tokenizer.pad_token_id\n",
     "model.resize_token_embeddings(model.config.vocab_size + num_added_toks)"
@@ -835,7 +543,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -845,17 +553,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "trainable params: 296,448 || all params: 124,737,792 || trainable%: 0.23765692437461133\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "if args.is_lora:\n",
     "    # build peft model\n",
@@ -867,7 +567,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -878,7 +578,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -901,107 +601,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The train will start from the checkpoint saved in '.mindnlp/peft_model/mrpc_IA3'.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 0: 100%|██████████| 459/459 [03:05<00:00,  2.47it/s, loss=0.6872016] \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Checkpoint: 'gpt2_mrpc_finetune_epoch_0.ckpt' has been saved in epoch: 0.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Evaluate: 100%|██████████| 51/51 [00:07<00:00,  7.18it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Evaluate Score: {'Accuracy': 0.6838235294117647}\n",
-      "---------------Best Model: 'gpt2_mrpc_finetune_best.ckpt' has been saved in epoch: 0.---------------\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1: 100%|██████████| 459/459 [03:04<00:00,  2.49it/s, loss=0.6677042] \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Checkpoint: 'gpt2_mrpc_finetune_epoch_1.ckpt' has been saved in epoch: 1.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Evaluate: 100%|██████████| 51/51 [00:07<00:00,  7.21it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Evaluate Score: {'Accuracy': 0.6838235294117647}\n",
-      "Loading best model from '.mindnlp/peft_model/mrpc_IA3' with '['Accuracy']': [0.6838235294117647]...\n",
-      "---------------The model is already load the best model from 'gpt2_mrpc_finetune_best.ckpt'.---------------\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "trainer.run(tgt_columns=\"labels\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Evaluate: 100%|██████████| 216/216 [00:30<00:00,  7.13it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Evaluate Score: {'Accuracy': 0.664927536231884}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "evaluator = Evaluator(network=model, eval_dataset=test_dataloader, metrics=metric)\n",
     "evaluator.run(tgt_columns=\"labels\")"
diff --git a/README.md b/README.md
index cb05d1f..7159a1e 100644
--- a/README.md
+++ b/README.md
@@ -3,6 +3,7 @@
 </div>
 
 ### 课程特色
+
 - ***探究前沿***：解读技术热点，解构热点模型
 - ***应用实践***：理论实践相结合，手把手指导开发
 - ***专家解读***：多领域专家，多元解读
@@ -15,18 +16,17 @@
   - 新增MindSpore Transformers大模型系列课程
   - 新增MindSpore兼容式训练系列课程
   - 新增MindSpore大模型推理系列课程
-  
 
-### 课程介绍
 
 本**系列课程**理论结合实践深入剖析大模型理论、训练、推理中的关键技术，介绍经典模型的结构设计与创新技术、MindSpore原生大模型训练套件、兼容式大模型训练套件、MindSpore大模型推理套件，课程体系主要包含以下内容：
 
 | 课程序号 |            课程名称             | 课程简介                                                                                                                                   | 视频 |                                                课件及代码                          |
 |:----:|:---------------------------:|:---------------------------------------------------------------------------------------------------------------------------------------|:----:|:---------------------------------------------------------------------------------------------------:|
-|  01  |           大模型理论课程           | 由浅入深地逐步深入大模型技术，以模型为主线介绍经典模型的结构、训练、推理中的创新技术。                                                                                            | [link](https://www.bilibili.com/video/BV1xs4y1M72q/?spm_id_from=333.999.0.0&vd_source=eb3a45e6eb4dccc5795f97586b78f429) |                             [link](./01.Industry_Model_Introduction/01.Classic_Model_Technical_Analysis/01.BERT/) | 
-|  02  | MindSpore Transformers大模型课程 | 基于MindSpore Transformers大模型套件，详细讲解了从环境搭建、预训练、微调至推理部署的大模型全流程开发使用方法，详细讲解了大模型开发迁移、精度对比、性能调优的内容。助力开发者基于MindSpore Transformers进行大模型高阶开发与调优。 | [link](https://www.bilibili.com/video/BV1Gh411w7HC/?spm_id_from=333.999.0.0&vd_source=eb3a45e6eb4dccc5795f97586b78f429) | [link](./01.Industry_Model_Introduction/01.Classic_Model_Technical_Analysis/02.GPT/)                              | 
-|  03  |     MindSpore兼容式大模型训练课程     | 围绕大模型预训练、微调、强化学习等场景，介绍了基于MindSpeed-Core-MS套件的全流程开发过程，使得学员能够了解MindSpeed-Core-MS相关概念、特性和开发流程，并初步具备在不同场景下大模型训练的能力。           | [link](xxx) |[link](xxx)|             
-|  04  |      MindSpore大模型推理课程       | xxxxx                                                                                                                                  | [link](xxx) |[link](xxx)|    
+|  01  |           大模型理论课程           | 由浅入深地逐步深入大模型技术，以模型为主线介绍经典模型的结构、训练、推理中的创新技术。                                                                                            | [link](https://www.bilibili.com/video/BV1xs4y1M72q/?spm_id_from=333.999.0.0&vd_source=eb3a45e6eb4dccc5795f97586b78f429) |                             [link](./01.Industry_Model_Introduction/01.Classic_Model_Technical_Analysis/01.BERT/) |
+|  02  | MindSpore Transformers大模型课程 | 基于MindSpore Transformers大模型套件，详细讲解了从环境搭建、预训练、微调至推理部署的大模型全流程开发使用方法，详细讲解了大模型开发迁移、精度对比、性能调优的内容。助力开发者基于MindSpore Transformers进行大模型高阶开发与调优。 | [link](https://www.bilibili.com/video/BV1Gh411w7HC/?spm_id_from=333.999.0.0&vd_source=eb3a45e6eb4dccc5795f97586b78f429) | [link](./01.Industry_Model_Introduction/01.Classic_Model_Technical_Analysis/02.GPT/)                              |
+|  03  |     MindSpore兼容式大模型训练课程     | 围绕大模型预训练、微调、强化学习等场景，介绍了基于MindSpeed-Core-MS套件的全流程开发过程，使得学员能够了解MindSpeed-Core-MS相关概念、特性和开发流程，并初步具备在不同场景下大模型训练的能力。           | [link](xxx) |[link](xxx)|
+|  04  |      MindSpore大模型推理课程       | xxxxx                                                                                                                                  | [link](xxx) |[link](xxx)|
+
 ## 贡献与反馈
 
 欢迎各位开发者通过 [Issue](https://github.com/mindspore-lab/step_into_llm/issues) 提交建议或 bug 反馈，也可直接发起 [PR](https://github.com/mindspore-lab/step_into_llm/pulls) 进行Bug修复或代码贡献（提交前请参考提交规范，由Committer @username 完成评审合入），你的每一份参与都能让本项目更加完善。
@@ -43,4 +43,4 @@
   <a href="https://github.com/mindspore-lab/step_into_llm/graphs/contributors">
     <img src="https://contrib.rocks/image?repo=mindspore-lab/step_into_llm" />
   </a>
-</div>
\ No newline at end of file
+</div>