diff --git a/Online/inference/08-LSTM+CRF/mindspore_sequence_labeling.ipynb b/Online/inference/08-LSTM+CRF/mindspore_sequence_labeling.ipynb
index 1691a6e..7025af2 100644
--- a/Online/inference/08-LSTM+CRF/mindspore_sequence_labeling.ipynb
+++ b/Online/inference/08-LSTM+CRF/mindspore_sequence_labeling.ipynb
@@ -48,13 +48,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "f01741d9",
    "metadata": {},
-   "outputs": [],
+   "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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.6.0\n"
+     ]
+    }
+   ],
    "source": [
     "import mindspore\n",
-    "mindspore.set_context(max_device_memory=\"2GB\", mode=mindspore.GRAPH_MODE, device_target=\"Ascend\",  jit_config={\"jit_level\":\"O2\"}, ascend_config={\"precision_mode\":\"allow_mix_precision\"})"
+    "import mindspore as ms\n",
+    "import mindspore.nn as nn\n",
+    "import mindspore.ops as ops\n",
+    "import mindspore.numpy as mnp\n",
+    "from mindspore.common.initializer import initializer, Uniform\n",
+    "import mindspore.mint as mint\n",
+    "mindspore.set_context(max_device_memory=\"2GB\", mode=mindspore.PYNATIVE_MODE, device_target=\"Ascend\",  jit_config={\"jit_level\":\"O0\"}, ascend_config={\"precision_mode\":\"allow_mix_precision\"})\n",
+    "print(mindspore.__version__)\n"
    ]
   },
   {
@@ -124,7 +153,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "d09936ad-6b20-4423-9f57-8e14917e61d4",
    "metadata": {},
    "outputs": [],
@@ -142,7 +171,7 @@
     "    score = start_trans[tags[0]]\n",
     "    # score += 第一次发射概率\n",
     "    # shape: (batch_size,)\n",
-    "    score += emissions[0, mnp.arange(batch_size), tags[0]]\n",
+    "    score += emissions[0, mint.arange(batch_size), tags[0]]\n",
     "\n",
     "    for i in range(1, seq_length):\n",
     "        # 标签由i-1转移至i的转移概率（当mask == 1时有效）\n",
@@ -151,11 +180,11 @@
     "\n",
     "        # 预测tags[i]的发射概率（当mask == 1时有效）\n",
     "        # shape: (batch_size,)\n",
-    "        score += emissions[i, mnp.arange(batch_size), tags[i]] * mask[i]\n",
+    "        score += emissions[i, mint.arange(batch_size), tags[i]] * mask[i]\n",
     "\n",
     "    # 结束转移\n",
     "    # shape: (batch_size,)\n",
-    "    last_tags = tags[seq_ends, mnp.arange(batch_size)]\n",
+    "    last_tags = tags[seq_ends, mint.arange(batch_size)]\n",
     "    # score += 结束转移概率\n",
     "    # shape: (batch_size,)\n",
     "    score += end_trans[last_tags]\n",
@@ -185,7 +214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "d9a0ef6a-1c3a-400e-9053-e0659e8f9e7e",
    "metadata": {},
    "outputs": [],
@@ -217,18 +246,18 @@
     "\n",
     "        # 对score_i做log_sum_exp运算，用于下一个Token的score计算\n",
     "        # shape: (batch_size, num_tags)\n",
-    "        next_score = ops.logsumexp(next_score, axis=1)\n",
+    "        next_score = mint.logsumexp(next_score, dim=1)\n",
     "\n",
     "        # 当mask == 1时，score才会变化\n",
     "        # shape: (batch_size, num_tags)\n",
-    "        score = mnp.where(mask[i].expand_dims(1), next_score, score)\n",
+    "        score = mint.where(mask[i].expand_dims(1), next_score, score)\n",
     "\n",
     "    # 最后加结束转移概率\n",
     "    # shape: (batch_size, num_tags)\n",
     "    score += end_trans\n",
     "    # 对所有可能的路径得分求log_sum_exp\n",
     "    # shape: (batch_size,)\n",
-    "    return ops.logsumexp(score, axis=1)"
+    "    return mint.logsumexp(score, dim=1)"
    ]
   },
   {
@@ -251,7 +280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "7c286935-74eb-413a-8e47-b8fb5264e6b6",
    "metadata": {},
    "outputs": [],
@@ -262,20 +291,22 @@
     "\n",
     "    seq_length = mask.shape[0]\n",
     "\n",
-    "    score = start_trans + emissions[0]\n",
+    "    score = (start_trans + emissions[0]).astype(ms.float32)\n",
     "    history = ()\n",
-    "\n",
+    "    mask_bool = mask.astype(ms.uint8)\n",
     "    for i in range(1, seq_length):\n",
     "        broadcast_score = score.expand_dims(2)\n",
     "        broadcast_emission = emissions[i].expand_dims(1)\n",
     "        next_score = broadcast_score + trans + broadcast_emission\n",
     "\n",
     "        # 求当前Token对应score取值最大的标签，并保存\n",
+    "        #indices = next_score.argmax(axis=1)\n",
     "        indices = next_score.argmax(axis=1)\n",
     "        history += (indices,)\n",
     "\n",
     "        next_score = next_score.max(axis=1)\n",
-    "        score = mnp.where(mask[i].expand_dims(1), next_score, score)\n",
+    "        score = mint.where(mask_bool[i].expand_dims(1), next_score, score)\n",
+    "\n",
     "\n",
     "    score += end_trans\n",
     "\n",
@@ -321,37 +352,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "120a39c7-c89d-4cd3-8a75-da27d2853f0e",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[WARNING] ME(97316:281472950050832,MainProcess):2024-09-06-10:50:02.452.307 [mindspore/run_check/_check_version.py:357] MindSpore version 2.2.14 and Ascend AI software package (Ascend Data Center Solution)version 7.0 does not match, the version of software package expect one of ['7.1']. Please refer to the match info on: https://www.mindspore.cn/install\n",
-      "/home/ma-user/anaconda3/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/ma-user/anaconda3/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",
-      "[WARNING] ME(97316:281472950050832,MainProcess):2024-09-06-10:50:04.855.681 [mindspore/run_check/_check_version.py:375] MindSpore version 2.2.14 and \"te\" wheel package version 7.0 does not match. For details, refer to the installation guidelines: https://www.mindspore.cn/install\n",
-      "[WARNING] ME(97316:281472950050832,MainProcess):2024-09-06-10:50:04.861.443 [mindspore/run_check/_check_version.py:382] MindSpore version 2.2.14 and \"hccl\" wheel package version 7.0 does not match. For details, refer to the installation guidelines: https://www.mindspore.cn/install\n",
-      "[WARNING] ME(97316:281472950050832,MainProcess):2024-09-06-10:50:04.862.319 [mindspore/run_check/_check_version.py:396] Please pay attention to the above warning, countdown: 3\n",
-      "[WARNING] ME(97316:281472950050832,MainProcess):2024-09-06-10:50:05.864.215 [mindspore/run_check/_check_version.py:396] Please pay attention to the above warning, countdown: 2\n",
-      "[WARNING] ME(97316:281472950050832,MainProcess):2024-09-06-10:50:06.866.175 [mindspore/run_check/_check_version.py:396] Please pay attention to the above warning, countdown: 1\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "import mindspore as ms\n",
-    "import mindspore.nn as nn\n",
-    "import mindspore.ops as ops\n",
-    "import mindspore.numpy as mnp\n",
-    "from mindspore.common.initializer import initializer, Uniform\n",
     "\n",
     "def sequence_mask(seq_length, max_length, batch_first=False):\n",
     "    \"\"\"根据序列实际长度和最大长度生成mask矩阵\"\"\"\n",
-    "    range_vector = mnp.arange(0, max_length, 1, seq_length.dtype)\n",
+    "    range_vector = mint.arange(0, max_length, 1, dtype=seq_length.dtype)\n",
     "    result = range_vector < seq_length.view(seq_length.shape + (1,))\n",
     "    if batch_first:\n",
     "        return result.astype(ms.int64)\n",
@@ -385,7 +394,7 @@
     "            max_length, batch_size = tags.shape\n",
     "\n",
     "        if seq_length is None:\n",
-    "            seq_length = mnp.full((batch_size,), max_length, ms.int64)\n",
+    "            seq_length = mint.full((batch_size,), max_length, ms.int64)\n",
     "\n",
     "        mask = sequence_mask(seq_length, max_length)\n",
     "\n",
@@ -412,7 +421,7 @@
     "            batch_size, max_length = emissions.shape[:2]\n",
     "\n",
     "        if seq_length is None:\n",
-    "            seq_length = mnp.full((batch_size,), max_length, ms.int64)\n",
+    "            seq_length = mint.full((batch_size,), max_length, ms.int64)\n",
     "\n",
     "        mask = sequence_mask(seq_length, max_length)\n",
     "\n",
@@ -437,7 +446,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "c07555e3-f2a2-4c25-beff-5a78491ab2d1",
    "metadata": {},
    "outputs": [],
@@ -469,7 +478,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "fa53f535-34bc-49a3-b769-e85d3a184cc0",
    "metadata": {},
    "outputs": [],
@@ -497,7 +506,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "5c255358-938b-4e88-a810-2fe4e616a044",
    "metadata": {},
    "outputs": [
@@ -507,7 +516,7 @@
        "21"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -526,7 +535,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "e6d3f2bb-0ea7-457c-8a74-a8e0ea78a109",
    "metadata": {},
    "outputs": [],
@@ -544,7 +553,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "7899b6e6-95a4-4ffe-ba49-b27cebe8b306",
    "metadata": {},
    "outputs": [],
@@ -569,7 +578,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "0e984dcb-6f89-4520-940f-620cdb997529",
    "metadata": {},
    "outputs": [
@@ -579,7 +588,7 @@
        "((2, 11), (2, 11), (2,))"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -599,10 +608,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "65304086-de03-4edc-b3a2-3ca5e43c4795",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from https://cdn.modelers.cn/lfs/b8/d8/c8dd957c842f8c2c4c07ee2e77dee1fca7abe0e8305fdc6b301453aae00b?response-content-disposition=attachment%3B+filename%3D%22lstm-crf.ckpt%22&AWSAccessKeyId=HAZQA0Q6AQL2GHX4TKTL&Expires=1761968039&Signature=eI%2FfqpDCWGkcGche5vPhitwqZik%3D (19 kB)\n",
+      "\n",
+      "file_sizes: 100%|██████████████████████████| 19.8k/19.8k [00:00<00:00, 10.8MB/s]\n",
+      "Successfully downloaded file to ./lstm-crf.ckpt\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Tensor(shape=[2, 3], dtype=Float32, value=\n",
+       "[[ 3.28305664e+01,  3.76408653e+01,  3.22400093e+01],\n",
+       " [ 2.89628849e+01,  2.69458580e+01,  3.40970802e+01]])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from mindspore import load_param_into_net, load_checkpoint\n",
     "from download import download\n",
@@ -628,7 +660,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 13,
    "id": "ed5ff78a-099c-41f8-ac1e-8f9fbfab7659",
    "metadata": {},
    "outputs": [
@@ -638,7 +670,7 @@
        "[[0, 1, 1, 1, 2, 2, 2, 2, 2, 0, 1], [0, 1, 2, 2, 2, 2, 2, 2, 2]]"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -658,7 +690,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 14,
    "id": "df505983-9c1f-4e09-8a67-1cd78ce69fd0",
    "metadata": {},
    "outputs": [],
@@ -674,9 +706,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 15,
    "id": "e30d43c7-7c09-445e-94a4-33fccfaeaf11",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -685,7 +719,7 @@
        " ['B', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'O']]"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -697,9 +731,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "MindSpore",
+   "display_name": "jupyter",
    "language": "python",
-   "name": "mindspore"
+   "name": "jupyter"
   },
   "language_info": {
    "codemirror_mode": {
@@ -711,7 +745,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.18"
+   "version": "3.9.23"
   },
   "vscode": {
    "interpreter": {