diff --git a/Season1.step_into_chatgpt/7.Prompt/roberta_sequence_classification.ipynb b/Season1.step_into_chatgpt/7.Prompt/roberta_sequence_classification.ipynb
index 6cf7043..72660e5 100644
--- a/Season1.step_into_chatgpt/7.Prompt/roberta_sequence_classification.ipynb
+++ b/Season1.step_into_chatgpt/7.Prompt/roberta_sequence_classification.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "8dabe994-5618-4f4f-9955-c8dc74870076",
+   "id": "d0a8f35f-c4f4-4591-a095-adf38dae8ad7",
    "metadata": {},
    "source": [
     "# 基于MindNLP的Roberta模型Prompt Tuning"
@@ -10,112 +10,35 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ebb7edc0-7482-448f-9dfe-b12cc7e1cab4",
+   "id": "7d18d959-5256-4c72-b3e7-18c306af5f90",
    "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=016991f8-0e0d-44c8-96f7-8b2cad54c592\n",
-    "）"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7e9aa619-6a28-4929-9c6c-2d88db8a1053",
-   "metadata": {},
-   "source": [
-    "## 环境安装\n",
-    "\n",
-    "运行如下两个代码块，创建python-3.9.0 kernel。\n",
-    "\n",
-    "> 注意：\n",
-    "> \n",
-    ">此为在线运行平台配置python3.9的指南，如在其他环境平台运行案例，请根据实际情况修改如下代码\n",
-    "> \n",
-    "> 以下两个代码块仅能运行一次，多次运行会出现kernel报错。\n",
-    ">\n",
-    "> 如出现多次运行导致的kernel报错，请终止实例（点击右上角“停止NoteBook实例”的圆形图标），并重启实例。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "3b0ef736-e7f1-46ac-866c-b6c76ef2c0d5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%capture captured_output\n",
-    "!/home/ma-user/anaconda3/bin/conda create -n python-3.9.0 python=3.9.0 -y --override-channels --channel https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main\n",
-    "!/home/ma-user/anaconda3/envs/python-3.9.0/bin/pip install ipykernel"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "6c55998d-bdfc-45b5-b2e4-fb8d603a3be9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "import os\n",
-    "\n",
-    "data = {\n",
-    "   \"display_name\": \"python-3.9.0\",\n",
-    "   \"env\": {\n",
-    "      \"PATH\": \"/home/ma-user/anaconda3/envs/python-3.9.0/bin:/home/ma-user/anaconda3/envs/python-3.7.10/bin:/modelarts/authoring/notebook-conda/bin:/opt/conda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/home/ma-user/modelarts/ma-cli/bin:/home/ma-user/modelarts/ma-cli/bin\"\n",
-    "   },\n",
-    "   \"language\": \"python\",\n",
-    "   \"argv\": [\n",
-    "      \"/home/ma-user/anaconda3/envs/python-3.9.0/bin/python\",\n",
-    "      \"-m\",\n",
-    "      \"ipykernel\",\n",
-    "      \"-f\",\n",
-    "      \"{connection_file}\"\n",
-    "   ]\n",
-    "}\n",
+    "## 环境配置\n",
     "\n",
-    "if not os.path.exists(\"/home/ma-user/anaconda3/share/jupyter/kernels/python-3.9.0/\"):\n",
-    "    os.mkdir(\"/home/ma-user/anaconda3/share/jupyter/kernels/python-3.9.0/\")\n",
+    "    python =3.9\n",
+    "    mindspore = 2.3.1\n",
+    "    mindnlp = 0.4.0\n",
+    "    jieba\n",
+    "    tiktoken\n",
     "\n",
-    "with open('/home/ma-user/anaconda3/share/jupyter/kernels/python-3.9.0/kernel.json', 'w') as f:\n",
-    "    json.dump(data, f, indent=4)"
+    "**在线运行代码平台链接：**\n",
+    "- 1. [华为云AI Gallery](https://pangu.huaweicloud.com/gallery/asset-detail.html?id=016991f8-0e0d-44c8-96f7-8b2cad54c592)\n",
+    "- 2. [大模型平台AI实验室统一入口](https://xihe.mindspore.cn/projects)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "7b2ee72f-e907-4536-b295-cc2f1c64db3c",
+   "id": "32234a62-3149-47a8-aece-f5175f206bdc",
    "metadata": {},
    "source": [
-    "创建完成后，稍等片刻，或刷新页面。如下图所示，点击右上角（或左上角）kernel选择python-3.9.0。\n",
+    "## 模型与数据集加载\n",
     "\n",
-    "![change-kernel](https://mindspore-demo.obs.cn-north-4.myhuaweicloud.com/imgs/ai-gallery/change-kernel.PNG)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b75eb5c6-11a6-43df-9067-fa15622cd517",
-   "metadata": {},
-   "source": [
-    "安装mindspore, mindnlp及其他依赖"
+    "本案例对roberta-large模型基于GLUE基准数据集进行prompt tuning。"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "b6bd85ef-29bf-40d5-8fef-00dfaab01a0d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%capture captured_output\n",
-    "\n",
-    "!pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/2.2.14/MindSpore/unified/x86_64/mindspore-2.2.14-cp39-cp39-linux_x86_64.whl --trusted-host ms-release.obs.cn-north-4.myhuaweicloud.com -i https://pypi.tuna.tsinghua.edu.cn/simple\n",
-    "!pip install mindnlp\n",
-    "!pip install ipywidgets\n",
-    "!pip install tqdm==4.65.0\n",
-    "!pip install scikit-learn"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
    "id": "7228a58b-4f81-4f5d-ac6c-d9439b3f4447",
    "metadata": {},
    "outputs": [
@@ -131,39 +54,50 @@
     "%env HF_ENDPOINT=https://hf-mirror.com"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "4cc505fa-31a4-4a07-8f9e-26bec54f4cdb",
-   "metadata": {},
-   "source": [
-    "## 模型与数据集加载\n",
-    "\n",
-    "本案例对roberta-large模型基于GLUE基准数据集进行prompt tuning。"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "id": "9ff5004e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/lvyufeng/miniconda3/envs/mindspore/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/lvyufeng/miniconda3/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/lvyufeng/miniconda3/envs/mindspore/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/lvyufeng/miniconda3/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 0.931 seconds.\n",
+      "Prefix dict has been built successfully.\n",
+      "/home/lvyufeng/miniconda3/envs/mindspore/lib/python3.9/site-packages/Cython/Compiler/Main.py:381: FutureWarning: Cython directive 'language_level' not set, using '3str' for now (Py3). This has changed from earlier releases! File: /home/lvyufeng/miniconda3/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",
+      "In file included from /home/lvyufeng/miniconda3/envs/mindspore/lib/python3.9/site-packages/numpy/core/include/numpy/ndarraytypes.h:1929,\n",
+      "                 from /home/lvyufeng/miniconda3/envs/mindspore/lib/python3.9/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n",
+      "                 from /home/lvyufeng/miniconda3/envs/mindspore/lib/python3.9/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n",
+      "                 from /home/lvyufeng/.pyxbld/temp.linux-aarch64-cpython-39/home/lvyufeng/miniconda3/envs/mindspore/lib/python3.9/site-packages/mindnlp/transformers/models/graphormer/algos_graphormer.c:1240:\n",
+      "/home/lvyufeng/miniconda3/envs/mindspore/lib/python3.9/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n",
+      "   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n",
+      "      |  ^~~~~~~\n"
+     ]
+    }
+   ],
    "source": [
-    "import argparse\n",
-    "import os\n",
-    "\n",
     "import mindspore\n",
-    "from mindspore.experimental.optim import AdamW\n",
     "from tqdm import tqdm\n",
-    "import evaluate\n",
+    "from mindnlp import evaluate\n",
     "from mindnlp.dataset import load_dataset\n",
-    "from mindnlp.engine import set_seed\n",
     "from mindnlp.transformers import AutoModelForSequenceClassification, AutoTokenizer\n",
-    "from mindnlp.modules.optimization import get_linear_schedule_with_warmup\n",
+    "from mindnlp.core.optim import AdamW\n",
+    "from mindnlp.common.optimization import get_linear_schedule_with_warmup\n",
     "from mindnlp.peft import (\n",
-    "    get_peft_config,\n",
     "    get_peft_model,\n",
-    "    get_peft_model_state_dict,\n",
-    "    set_peft_model_state_dict,\n",
     "    PeftType,\n",
     "    PromptTuningConfig,\n",
     ")"
@@ -171,22 +105,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "id": "e32c4a9e",
    "metadata": {},
    "outputs": [],
    "source": [
     "batch_size = 32\n",
-    "model_name_or_path = \"AI-ModelScope/roberta-large\"\n",
+    "model_name_or_path = \"roberta-large\"\n",
     "task = \"mrpc\"\n",
     "peft_type = PeftType.PROMPT_TUNING\n",
-    "# num_epochs = 20\n",
-    "num_epochs = 5"
+    "num_epochs = 20"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "5dcf6f6e-2d5e-4342-b264-67c7419374d4",
+   "id": "44950094-babf-4834-be57-e70a032754a8",
    "metadata": {},
    "source": [
     "prompt tuning配置，任务类型选为\"SEQ_CLS\", 即序列分类。"
@@ -194,20 +127,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "id": "622fe9c8",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# peft config\n",
     "peft_config = PromptTuningConfig(task_type=\"SEQ_CLS\", num_virtual_tokens=10)\n",
-    "# learning rate\n",
     "lr = 1e-3"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "2474cad0-3b50-4ba1-aea4-2787955f41c7",
+   "id": "b4491967-c060-4064-8b43-30d9728cfb03",
    "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。"
@@ -215,183 +146,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "id": "74e9efe0",
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c6f49682e9da4a26afdf8f040f76f1e0",
-       "version_major": 2,
-       "version_minor": 0
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-      ]
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-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7e2aff439ad6453bb108f33fce406924",
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-    },
-    {
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-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4de1307c3a9e4b7ba803f4c73e96b0c0",
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-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "aae340c43b2c4a2c88d97f62e7258a2d",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/lvyufeng/miniconda3/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 in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
+      "  warnings.warn(\n"
+     ]
     }
    ],
    "source": [
-    "# load tokenizer\n",
     "if any(k in model_name_or_path for k in (\"gpt\", \"opt\", \"bloom\")):\n",
     "    padding_side = \"left\"\n",
     "else:\n",
     "    padding_side = \"right\"\n",
     "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, padding_side=padding_side, mirror=\"modelscope\")\n",
+    "tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, padding_side=padding_side)\n",
     "if getattr(tokenizer, \"pad_token_id\") is None:\n",
     "    tokenizer.pad_token_id = tokenizer.eos_token_id"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "id": "41a63e71-e7c4-4e5d-9e22-6953d981d4b8",
    "metadata": {},
    "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e06db10e616242479f9db7e4a75056a2",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Downloading readme: 0.00B [00:00, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "237a88b7722746a38669eb6ce9b479d4",
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-     "output_type": "display_data"
-    },
-    {
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-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "130bcf9c3ab143558147c01a7520c1ba",
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-     "output_type": "display_data"
-    },
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-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f1f05ed14f8b4fb397503d189877f86f",
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-     "metadata": {},
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-    },
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-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6410985b167b414481fbb798a1787bed",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
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-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b9c722851679447782863eb0f2312f38",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Generating validation split:   0%|          | 0/408 [00:00<?, ? examples/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "27e0b89fd70941fa8e91fd8c042f6d89",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Generating test split:   0%|          | 0/1725 [00:00<?, ? examples/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -407,14 +191,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "id": "bd2d7cd5-62b8-4b7a-ac69-338e6319152e",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from mindnlp.dataset import BaseMapFuction\n",
+    "from mindnlp.dataset import BaseMapFunction\n",
     "\n",
-    "class MapFunc(BaseMapFuction):\n",
+    "class MapFunc(BaseMapFunction):\n",
     "    def __call__(self, sentence1, sentence2, label, idx):\n",
     "        outputs = tokenizer(sentence1, sentence2, truncation=True, max_length=None)\n",
     "        return outputs['input_ids'], outputs['attention_mask'], label\n",
@@ -435,19 +219,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "id": "3b1fd5fc-2285-409e-a4e5-cc3c9759d77a",
    "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"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -477,122 +252,84 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "id": "efb606a2-1fb5-415c-bf12-7e6fd324fe0a",
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "da23e19b7a7748e89a2f50ea4164dc11",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Downloading builder script: 0.00B [00:00, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "metric = evaluate.load(\"glue\", task)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "d7703d53-1018-4b99-bca0-f566adbc03f5",
-   "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",
-    "\n",
-    "如出现如下告警请忽略，并不影响模型的微调。\n",
-    "\n",
-    "```text\n",
-    "The following parameters in checkpoint files are not loaded:\n",
-    "['lm_head.bias', 'lm_head.dense.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.bias', 'lm_head.layer_norm.weight', 'roberta.embeddings.position_ids']\n",
-    "The following parameters in models are missing parameter:\n",
-    "['classifier.dense.weight', 'classifier.dense.bias', 'classifier.out_proj.weight', 'classifier.out_proj.bias']\n",
-    "```"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "id": "a3c15af0",
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "39732a0ed96c4cdc933b915ebf05ff90",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0.00/1.32G [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": [
-      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
-      "To disable this warning, you can either:\n",
-      "\t- Avoid using `tokenizers` before the fork if possible\n",
-      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
-      "The following parameters in checkpoint files are not loaded:\n",
-      "['lm_head.bias', 'lm_head.dense.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.bias', 'lm_head.layer_norm.weight', 'roberta.embeddings.position_ids']\n",
-      "The following parameters in models are missing parameter:\n",
-      "['classifier.dense.weight', 'classifier.dense.bias', 'classifier.out_proj.weight', 'classifier.out_proj.bias']\n"
+      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at 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: 2,113,540 || all params: 356,423,684 || trainable%: 0.5929852854559463\n"
+      "trainable params: 1,061,890 || all params: 356,423,684 || trainable%: 0.2979291353713745\n"
      ]
     }
    ],
    "source": [
-    "# load model\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path, return_dict=True, mirror=\"modelscope\")\n",
+    "model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path, return_dict=True)\n",
     "model = get_peft_model(model, peft_config)\n",
-    "# print number of trainable parameters\n",
     "model.print_trainable_parameters()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "3a653eb0-a45b-4232-9a90-2a88568f20b3",
+   "id": "7874852d-a564-4d7d-b5df-5e6bc55b61b8",
    "metadata": {},
    "source": [
-    "## 模型微调（prompt tuning）"
+    "加载模型并打印微调参数量，可以看到仅有不到0.3%的参数参与了微调。\n",
+    "\n",
+    "如出现如下告警请忽略，并不影响模型的微调。\n",
+    "\n",
+    "The following parameters in checkpoint files are not loaded:\n",
+    "['lm_head.bias', 'lm_head.dense.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.bias', 'lm_head.layer_norm.weight', 'roberta.embeddings.position_ids']\n",
+    "\n",
+    "The following parameters in models are missing parameter:\n",
+    "['classifier.dense.weight', 'classifier.dense.bias', 'classifier.out_proj.weight', 'classifier.out_proj.bias']"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c268ec75-718e-4ae7-b099-9f104b0a53f6",
+   "id": "aae75998-e3de-4eed-8772-00fc8a3a5675",
    "metadata": {},
    "source": [
+    "## 模型微调（prompt tuning）\n",
     "指定优化器和学习率调整策略"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "id": "6d3c5edb",
    "metadata": {},
    "outputs": [],
    "source": [
-    "optimizer = AdamW(params=model.trainable_params(), lr=lr)\n",
+    "optimizer = AdamW(params=model.parameters(), lr=lr)\n",
     "\n",
     "# Instantiate scheduler\n",
     "lr_scheduler = get_linear_schedule_with_warmup(\n",
@@ -604,58 +341,23 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ca53ea25-eb55-49b1-bab5-161288dcd806",
-   "metadata": {},
-   "source": [
-    "打印参与微调的模型参数"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "dbd66774-4482-448d-a1ee-f09f33cb8579",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Parameter (Tensor(shape=[1024, 1024], dtype=Float32, value=[...], name=base_model.classifier.original_module.dense.weight), requires_grad=True),\n",
-       " Parameter (Tensor(shape=[1024], dtype=Float32, value=[...], name=base_model.classifier.original_module.dense.bias), requires_grad=True),\n",
-       " Parameter (Tensor(shape=[2, 1024], dtype=Float32, value=[...], name=base_model.classifier.original_module.out_proj.weight), requires_grad=True),\n",
-       " Parameter (Tensor(shape=[2], dtype=Float32, value=[0. 0.], name=base_model.classifier.original_module.out_proj.bias), requires_grad=True),\n",
-       " Parameter (Tensor(shape=[1024, 1024], dtype=Float32, value=[...], name=base_model.classifier.modules_to_save.default.dense.weight), requires_grad=True),\n",
-       " Parameter (Tensor(shape=[1024], dtype=Float32, value=[...], name=base_model.classifier.modules_to_save.default.dense.bias), requires_grad=True),\n",
-       " Parameter (Tensor(shape=[2, 1024], dtype=Float32, value=[...], name=base_model.classifier.modules_to_save.default.out_proj.weight), requires_grad=True),\n",
-       " Parameter (Tensor(shape=[2], dtype=Float32, value=[ 0.00000000e+00  0.00000000e+00], name=base_model.classifier.modules_to_save.default.out_proj.bias), requires_grad=True),\n",
-       " Parameter (Tensor(shape=[10, 1024], dtype=Float32, value=[...], name=prompt_encoder.default.embedding.weight), requires_grad=True)]"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# print name of trainable parameters\n",
-    "model.trainable_params()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eae91b32-2b5e-4084-82c3-a6e52d0193a3",
+   "id": "f8443a40-13cb-4585-b553-687b5f478df6",
    "metadata": {},
    "source": [
     "按照如下步骤定义训练逻辑：\n",
     "\n",
-    "1. 构建正向计算函数\n",
-    "2. 函数变换，获取微分函数\n",
-    "3. 定义训练一个step的逻辑\n",
-    "4. 遍历训练数据集进行模型训练，同时每一个epoch后，遍历验证数据集获取当前的评价指标（accuracy、f1 score）"
+    "1、构建正向计算函数\n",
+    "\n",
+    "2、函数变换，获取微分函数\n",
+    "\n",
+    "3、定义训练一个step的逻辑\n",
+    "\n",
+    "4、遍历训练数据集进行模型训练，同时每一个epoch后，遍历验证数据集获取当前的评价指标（accuracy、f1 score）"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "4d279225",
    "metadata": {},
    "outputs": [
@@ -663,111 +365,349 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 115/115 [02:24<00:00,  1.25s/it]\n",
-      "100%|██████████| 13/13 [00:05<00:00,  2.31it/s]\n"
+      "  0%|                                                                                                | 0/115 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████| 115/115 [02:10<00:00,  1.13s/it]\n",
+      " 15%|█████████████▋                                                                           | 2/13 [00:01<00:04,  2.21it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:02<00:00,  5.53it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 0: {'accuracy': 0.7205882352941176, 'f1': 0.8267477203647416}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:21<00:00,  1.41it/s]\n",
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:02<00:00,  5.96it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "epoch 0: {'accuracy': 0.6911764705882353, 'f1': 0.8130563798219584}\n"
+      "epoch 1: {'accuracy': 0.7009803921568627, 'f1': 0.817910447761194}\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 115/115 [02:21<00:00,  1.23s/it]\n",
-      "100%|██████████| 13/13 [00:05<00:00,  2.56it/s]\n"
+      "100%|██████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:22<00:00,  1.39it/s]\n",
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:02<00:00,  5.82it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "epoch 1: {'accuracy': 0.6985294117647058, 'f1': 0.8150375939849624}\n"
+      "epoch 2: {'accuracy': 0.7058823529411765, 'f1': 0.8198198198198198}\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
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-      "100%|██████████| 13/13 [00:05<00:00,  2.57it/s]\n"
+      "100%|██████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:20<00:00,  1.43it/s]\n",
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:02<00:00,  5.88it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "epoch 2: {'accuracy': 0.7083333333333334, 'f1': 0.8199697428139183}\n"
+      "epoch 3: {'accuracy': 0.7058823529411765, 'f1': 0.8187311178247734}\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 115/115 [02:21<00:00,  1.23s/it]\n",
-      "100%|██████████| 13/13 [00:05<00:00,  2.54it/s]\n"
+      "100%|██████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:23<00:00,  1.37it/s]\n",
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:02<00:00,  5.76it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "epoch 3: {'accuracy': 0.7083333333333334, 'f1': 0.8221225710014948}\n"
+      "epoch 4: {'accuracy': 0.7107843137254902, 'f1': 0.8190184049079755}\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 115/115 [02:21<00:00,  1.23s/it]\n",
-      "100%|██████████| 13/13 [00:05<00:00,  2.55it/s]"
+      "100%|██████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:23<00:00,  1.37it/s]\n",
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:02<00:00,  5.77it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "epoch 4: {'accuracy': 0.7058823529411765, 'f1': 0.8198198198198198}\n"
+      "epoch 5: {'accuracy': 0.7205882352941176, 'f1': 0.8161290322580645}\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\n"
+      "100%|██████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:23<00:00,  1.38it/s]\n",
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:02<00:00,  5.80it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 6: {'accuracy': 0.7401960784313726, 'f1': 0.8295819935691319}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:24<00:00,  1.37it/s]\n",
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:02<00:00,  5.71it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 7: {'accuracy': 0.7156862745098039, 'f1': 0.8104575163398693}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:22<00:00,  1.40it/s]\n",
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:02<00:00,  5.89it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 8: {'accuracy': 0.7205882352941176, 'f1': 0.8093645484949833}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:15<00:00,  1.52it/s]\n",
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:01<00:00,  7.10it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 9: {'accuracy': 0.7328431372549019, 'f1': 0.8256}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:09<00:00,  1.67it/s]\n",
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:01<00:00,  6.92it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 10: {'accuracy': 0.7328431372549019, 'f1': 0.8233387358184765}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:05<00:00,  1.76it/s]\n",
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:01<00:00,  6.91it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 11: {'accuracy': 0.7401960784313726, 'f1': 0.8284789644012945}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:05<00:00,  1.74it/s]\n",
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:01<00:00,  7.02it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 12: {'accuracy': 0.7303921568627451, 'f1': 0.8264984227129337}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:02<00:00,  1.83it/s]\n",
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:01<00:00,  6.91it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 13: {'accuracy': 0.7352941176470589, 'f1': 0.8296529968454258}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:04<00:00,  1.78it/s]\n",
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:01<00:00,  6.79it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 14: {'accuracy': 0.7328431372549019, 'f1': 0.8244766505636071}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:06<00:00,  1.74it/s]\n",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 15: {'accuracy': 0.7377450980392157, 'f1': 0.8260162601626017}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:08<00:00,  1.69it/s]\n",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 16: {'accuracy': 0.7377450980392157, 'f1': 0.8288}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:07<00:00,  1.70it/s]\n",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 17: {'accuracy': 0.7401960784313726, 'f1': 0.8295819935691319}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:08<00:00,  1.67it/s]\n",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 18: {'accuracy': 0.7279411764705882, 'f1': 0.8235294117647058}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:07<00:00,  1.71it/s]\n",
+      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:01<00:00,  6.55it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 19: {'accuracy': 0.7328431372549019, 'f1': 0.8244766505636071}\n"
      ]
     }
    ],
    "source": [
-    "# define forward function\n",
+    "from mindnlp.core import value_and_grad\n",
     "def forward_fn(**batch):\n",
     "    outputs = model(**batch)\n",
     "    loss = outputs.loss\n",
     "    return loss\n",
     "\n",
-    "# Get gradient function\n",
-    "grad_fn = mindspore.value_and_grad(forward_fn, None, model.trainable_params())\n",
+    "grad_fn = value_and_grad(forward_fn, tuple(model.parameters()))\n",
     "\n",
-    "# Define function of one-step training\n",
-    "def train_step(**batch):\n",
-    "    loss, grads = grad_fn(**batch)\n",
-    "    optimizer(grads)\n",
-    "    return loss\n",
-    "\n",
-    "# Start training\n",
     "for epoch in range(num_epochs):\n",
     "    model.set_train()\n",
     "    train_total_size = train_dataset.get_dataset_size()\n",
-    "    # Iterate through the dataset\n",
     "    for step, batch in enumerate(tqdm(train_dataset.create_dict_iterator(), total=train_total_size)):\n",
-    "        loss = train_step(**batch)\n",
+    "        optimizer.zero_grad()\n",
+    "        loss = grad_fn(**batch)\n",
+    "        optimizer.step()\n",
     "        lr_scheduler.step()\n",
     "\n",
-    "    # Evaluate while training\n",
     "    model.set_train(False)\n",
     "    eval_total_size = eval_dataset.get_dataset_size()\n",
     "    for step, batch in enumerate(tqdm(eval_dataset.create_dict_iterator(), total=eval_total_size)):\n",
@@ -778,29 +718,25 @@
     "            predictions=predictions,\n",
     "            references=references,\n",
     "        )\n",
-    "    \n",
-    "    # Calculate accuracy and f1 score\n",
+    "\n",
     "    eval_metric = metric.compute()\n",
     "    print(f\"epoch {epoch}:\", eval_metric)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1f4ca2c1-d7a0-433d-8f9e-4436e4786a1a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
-  "AIGalleryInfo": {
-   "item_id": "016991f8-0e0d-44c8-96f7-8b2cad54c592"
-  },
-  "flavorInfo": {
-   "architecture": "X86_64",
-   "category": "GPU"
-  },
-  "imageInfo": {
-   "id": "e1a07296-22a8-4f05-8bc8-e936c8e54202",
-   "name": "mindspore1.7.0-cuda10.1-py3.7-ubuntu18.04"
-  },
   "kernelspec": {
-   "display_name": "python-3.9.0",
+   "display_name": "python-3.9.10",
    "language": "python",
-   "name": "python-3.9.0"
+   "name": "python-3.9.10"
   },
   "language_info": {
    "codemirror_mode": {
@@ -812,7 +748,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.0"
+   "version": "3.9.10"
   },
   "vscode": {
    "interpreter": {