diff --git a/Build-reasoning-model/Own_reasoning_model_with_GRPO.ipynb b/Build-reasoning-model/Own_reasoning_model_with_GRPO.ipynb
index 5003e0e1f..c6415fcb0 100644
--- a/Build-reasoning-model/Own_reasoning_model_with_GRPO.ipynb
+++ b/Build-reasoning-model/Own_reasoning_model_with_GRPO.ipynb
@@ -2,14 +2,14 @@
   "cells": [
     {
       "cell_type": "code",
-      "source": [
-        "# Reference: https://unsloth.ai/blog/r1-reasoning"
-      ],
+      "execution_count": null,
       "metadata": {
         "id": "kY8jzinCqlzF"
       },
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "# Reference: https://unsloth.ai/blog/r1-reasoning"
+      ]
     },
     {
       "cell_type": "code",
@@ -36,8 +36,8 @@
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "Unsloth: Patching Xformers to fix some performance issues.\n",
             "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
@@ -176,8 +176,8 @@
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "==((====))==  Unsloth 2025.2.4: Fast Llama patching. Transformers: 4.48.2.\n",
             "   \\\\   /|    GPU: Tesla T4. Max memory: 14.741 GB. Platform: Linux.\n",
@@ -196,64 +196,64 @@
           ]
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "tokenizer_config.json:   0%|          | 0.00/55.5k [00:00<?, ?B/s]"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "0891f4be495844c2abc8ed2a9e0081c1",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "0891f4be495844c2abc8ed2a9e0081c1"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "tokenizer_config.json:   0%|          | 0.00/55.5k [00:00<?, ?B/s]"
+            ]
           },
+          "output_type": "display_data",
           "metadata": {}
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "tokenizer.json:   0%|          | 0.00/17.2M [00:00<?, ?B/s]"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "29a6c495b5964d70a594ec1e45b27f13",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "29a6c495b5964d70a594ec1e45b27f13"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "tokenizer.json:   0%|          | 0.00/17.2M [00:00<?, ?B/s]"
+            ]
           },
+          "output_type": "display_data",
           "metadata": {}
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "special_tokens_map.json:   0%|          | 0.00/454 [00:00<?, ?B/s]"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "b1eb7b31b8a847f9ab747e32fc3dad41",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "b1eb7b31b8a847f9ab747e32fc3dad41"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "special_tokens_map.json:   0%|          | 0.00/454 [00:00<?, ?B/s]"
+            ]
           },
+          "output_type": "display_data",
           "metadata": {}
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "generation_config.json:   0%|          | 0.00/239 [00:00<?, ?B/s]"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "76dbac87ea1e4d79afe609d25b693d52",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "76dbac87ea1e4d79afe609d25b693d52"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "generation_config.json:   0%|          | 0.00/239 [00:00<?, ?B/s]"
+            ]
           },
+          "output_type": "display_data",
           "metadata": {}
         },
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "INFO 02-07 18:06:03 cuda.py:179] Cannot use FlashAttention-2 backend for Volta and Turing GPUs.\n",
             "INFO 02-07 18:06:03 cuda.py:227] Using XFormers backend.\n",
@@ -263,50 +263,50 @@
           ]
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "model.safetensors:   0%|          | 0.00/5.70G [00:00<?, ?B/s]"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "ef3a81e813d844b887537a3ac5d5005b",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "ef3a81e813d844b887537a3ac5d5005b"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "model.safetensors:   0%|          | 0.00/5.70G [00:00<?, ?B/s]"
+            ]
           },
+          "output_type": "display_data",
           "metadata": {}
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Loading safetensors checkpoint shards:   0% Completed | 0/1 [00:00<?, ?it/s]\n"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "4cc4d99b961b4d74a662b736e325add2",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "4cc4d99b961b4d74a662b736e325add2"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Loading safetensors checkpoint shards:   0% Completed | 0/1 [00:00<?, ?it/s]\n"
+            ]
           },
+          "output_type": "display_data",
           "metadata": {}
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Loading safetensors checkpoint shards:   0% Completed | 0/1 [00:00<?, ?it/s]\n"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "bf5af084b7524cf3818592777ecfedf9",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "bf5af084b7524cf3818592777ecfedf9"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Loading safetensors checkpoint shards:   0% Completed | 0/1 [00:00<?, ?it/s]\n"
+            ]
           },
+          "output_type": "display_data",
           "metadata": {}
         },
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "INFO 02-07 18:08:10 model_runner.py:1115] Loading model weights took 5.3541 GB\n",
             "INFO 02-07 18:08:10 punica_selector.py:18] Using PunicaWrapperGPU.\n",
@@ -319,72 +319,72 @@
           ]
         },
         {
-          "output_type": "stream",
           "name": "stderr",
+          "output_type": "stream",
           "text": [
             "Capturing CUDA graph shapes: 100%|██████████| 23/23 [00:38<00:00,  1.69s/it]"
           ]
         },
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "INFO 02-07 18:09:07 model_runner.py:1562] Graph capturing finished in 39 secs, took 0.58 GiB\n",
             "INFO 02-07 18:09:07 llm_engine.py:431] init engine (profile, create kv cache, warmup model) took 56.64 seconds\n"
           ]
         },
         {
-          "output_type": "stream",
           "name": "stderr",
+          "output_type": "stream",
           "text": [
             "\n"
           ]
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "tokenizer_config.json:   0%|          | 0.00/55.5k [00:00<?, ?B/s]"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "59ff091ac493435c89b4d413703ccf11",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "59ff091ac493435c89b4d413703ccf11"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "tokenizer_config.json:   0%|          | 0.00/55.5k [00:00<?, ?B/s]"
+            ]
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+          "output_type": "display_data",
           "metadata": {}
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-          "output_type": "display_data",
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-            "text/plain": [
-              "tokenizer.json:   0%|          | 0.00/17.2M [00:00<?, ?B/s]"
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-              "model_id": "2daa1ed02aac48949ddb3b8bae31b901"
-            }
+              "version_minor": 0
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+            "text/plain": [
+              "tokenizer.json:   0%|          | 0.00/17.2M [00:00<?, ?B/s]"
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-              "special_tokens_map.json:   0%|          | 0.00/454 [00:00<?, ?B/s]"
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+              "model_id": "e19f37e488334e40833ffae132deb313",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "e19f37e488334e40833ffae132deb313"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "special_tokens_map.json:   0%|          | 0.00/454 [00:00<?, ?B/s]"
+            ]
           },
+          "output_type": "display_data",
           "metadata": {}
         },
         {
-          "output_type": "stream",
           "name": "stderr",
+          "output_type": "stream",
           "text": [
             "Not an error, but Unsloth cannot patch MLP layers with our manual autograd engine since either LoRA adapters\n",
             "are not enabled or a bias term (like in Qwen) is used.\n",
@@ -418,6 +418,11 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "QrOLEFL-pr0w"
+      },
+      "outputs": [],
       "source": [
         "from datasets import load_dataset\n",
         "import re\n",
@@ -450,18 +455,12 @@
         "    return data # type: ignore\n",
         "\n",
         "dataset = create_dataset()"
-      ],
-      "metadata": {
-        "id": "QrOLEFL-pr0w"
-      },
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "cXk993X6C2ZZ",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 209,
@@ -534,91 +533,92 @@
             "22a7c3880d7c435897454a33674acef4"
           ]
         },
+        "id": "cXk993X6C2ZZ",
         "outputId": "c88b8073-25fc-4ba2-ec3d-cd741f769abc"
       },
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "README.md:   0%|          | 0.00/7.94k [00:00<?, ?B/s]"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "16bee5fc9d6d419bbb68f9fac449dbea",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "16bee5fc9d6d419bbb68f9fac449dbea"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "README.md:   0%|          | 0.00/7.94k [00:00<?, ?B/s]"
+            ]
           },
+          "output_type": "display_data",
           "metadata": {}
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "train-00000-of-00001.parquet:   0%|          | 0.00/2.31M [00:00<?, ?B/s]"
-            ],
             "application/vnd.jupyter.widget-view+json": {
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-              "model_id": "2317c89fb9e647f48d827e8f1c485413"
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+              "version_minor": 0
+            },
+            "text/plain": [
+              "train-00000-of-00001.parquet:   0%|          | 0.00/2.31M [00:00<?, ?B/s]"
+            ]
           },
+          "output_type": "display_data",
           "metadata": {}
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "test-00000-of-00001.parquet:   0%|          | 0.00/419k [00:00<?, ?B/s]"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "663fd256bd3e498bac72bb47c99b7b80",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "663fd256bd3e498bac72bb47c99b7b80"
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+              "version_minor": 0
+            },
+            "text/plain": [
+              "test-00000-of-00001.parquet:   0%|          | 0.00/419k [00:00<?, ?B/s]"
+            ]
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+          "output_type": "display_data",
           "metadata": {}
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Generating train split:   0%|          | 0/7473 [00:00<?, ? examples/s]"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "377e8698b65042f88f7a19f2596f29cc",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "377e8698b65042f88f7a19f2596f29cc"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating train split:   0%|          | 0/7473 [00:00<?, ? examples/s]"
+            ]
           },
+          "output_type": "display_data",
           "metadata": {}
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Generating test split:   0%|          | 0/1319 [00:00<?, ? examples/s]"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "e4b65d06051d4a90a7fec89e29f0e044",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "e4b65d06051d4a90a7fec89e29f0e044"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating test split:   0%|          | 0/1319 [00:00<?, ? examples/s]"
+            ]
           },
+          "output_type": "display_data",
           "metadata": {}
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Map:   0%|          | 0/7473 [00:00<?, ? examples/s]"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "d33508ee802642f6856ff7d79a35c131",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "d33508ee802642f6856ff7d79a35c131"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Map:   0%|          | 0/7473 [00:00<?, ? examples/s]"
+            ]
           },
+          "output_type": "display_data",
           "metadata": {}
         }
       ],
@@ -685,8 +685,8 @@
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stderr",
+          "output_type": "stream",
           "text": [
             "torch.distributed process group is initialized, but parallel_mode != ParallelMode.DISTRIBUTED. In order to use Torch DDP, launch your script with `python -m torch.distributed.launch\n"
           ]
@@ -733,9 +733,6 @@
       },
       "outputs": [
         {
-          "metadata": {
-            "tags": null
-          },
           "name": "stderr",
           "output_type": "stream",
           "text": [
@@ -747,9 +744,6 @@
           ]
         },
         {
-          "metadata": {
-            "tags": null
-          },
           "name": "stdout",
           "output_type": "stream",
           "text": [
@@ -1513,12 +1507,12 @@
               "<IPython.core.display.HTML object>"
             ]
           },
-          "metadata": {},
-          "output_type": "display_data"
+          "output_type": "display_data",
+          "metadata": {}
         },
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "-------------------- Question:\n",
             "The gauge on a water tank shows that the tank is 1/3 full of water. To fill the tank, 16 gallons of water are added. How many gallons of water does the tank hold when full? \n",
@@ -4132,11 +4126,7 @@
           ]
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ],
             "text/html": [
               "\n",
               "    <div>\n",
@@ -4886,13 +4876,17 @@
               "    </tr>\n",
               "  </tbody>\n",
               "</table><p>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
             ]
           },
+          "output_type": "display_data",
           "metadata": {}
         },
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "-------------------- Question:\n",
             "Edith is a receptionist at a local office and is organizing files into cabinets. She had 60 files and finished organizing half of them this morning. She has another 15 files to organize in the afternoon and the rest of the files are missing. How many files are missing? \n",
@@ -4937,9 +4931,9 @@
           ]
         },
         {
-          "output_type": "error",
           "ename": "KeyboardInterrupt",
           "evalue": "",
+          "output_type": "error",
           "traceback": [
             "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
             "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
@@ -4991,14 +4985,14 @@
     },
     {
       "cell_type": "code",
-      "source": [
-        "from vllm import SamplingParams"
-      ],
+      "execution_count": null,
       "metadata": {
         "id": "jjwJiYl0CF7j"
       },
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "from vllm import SamplingParams"
+      ]
     },
     {
       "cell_type": "code",
@@ -5012,8 +5006,8 @@
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "9.11 is bigger than 9.9.\n"
           ]
@@ -5062,8 +5056,8 @@
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "<comparing two decimal numbers>\n",
             "Since both numbers have the same integer part (9), we need to compare their fractional parts (0.11 and 0.9). \n",
@@ -5110,5482 +5104,6 @@
     },
     "language_info": {
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diff --git a/Build-reasoning-model/fix_notebooks.py b/Build-reasoning-model/fix_notebooks.py
new file mode 100644
index 000000000..4e732a4fa
--- /dev/null
+++ b/Build-reasoning-model/fix_notebooks.py
@@ -0,0 +1,40 @@
+# automatically repair metadata issues across all notebook
+
+import json
+import os
+
+def fix_notebook(nb_path):
+    with open(nb_path, "r", encoding="utf-8") as f:
+        nb = json.load(f)
+
+    changed = False
+
+    for cell in nb.get("cells", []):
+        if "outputs" in cell:
+            for output in cell["outputs"]:
+                # Case 1: Invalid metadata (e.g. contains None or tags)
+                if "metadata" in output:
+                    if output["metadata"] is None or (
+                        isinstance(output["metadata"], dict)
+                        and any(v is None for v in output["metadata"].values())
+                    ):
+                        output["metadata"] = {}
+                        changed = True
+                # Case 2: Missing metadata entirely (required for display_data)
+                elif output.get("output_type") == "display_data":
+                    output["metadata"] = {}
+                    changed = True
+
+    if changed:
+        with open(nb_path, "w", encoding="utf-8") as f:
+            json.dump(nb, f, indent=2, ensure_ascii=False)
+        print(f"Fixed metadata issues in: {nb_path}")
+    else:
+        print(f"No fixes needed in: {nb_path}")
+
+if __name__ == "__main__":
+    folder = "."
+    for root, _, files in os.walk(folder):
+        for file in files:
+            if file.endswith(".ipynb"):
+                fix_notebook(os.path.join(root, file))
diff --git a/Build-reasoning-model/validate_nb.py b/Build-reasoning-model/validate_nb.py
new file mode 100644
index 000000000..e27e1e693
--- /dev/null
+++ b/Build-reasoning-model/validate_nb.py
@@ -0,0 +1,11 @@
+import nbformat
+from nbformat import ValidationError
+from pathlib import Path
+
+for p in Path('.').rglob('*.ipynb'):
+    nb = nbformat.read(str(p), as_version=4)
+    try:
+        nbformat.validate(nb)
+        print('VALID ->', p)
+    except ValidationError as e:
+        print('INVALID ->', p, e)