diff --git "a/llm_metaeval_eval_harness_results.ipynb" "b/llm_metaeval_eval_harness_results.ipynb"
--- "a/llm_metaeval_eval_harness_results.ipynb"
+++ "b/llm_metaeval_eval_harness_results.ipynb"
@@ -11,18 +11,41 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 6,
       "metadata": {
-        "id": "kGW7vfRkrqHe"
+        "id": "kGW7vfRkrqHe",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "8a12c95a-e99b-4dfe-b973-a093f2f1ab25"
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.10/dist-packages (0.26.2)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (3.16.1)\n",
+            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2024.10.0)\n",
+            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (24.2)\n",
+            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (6.0.2)\n",
+            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2.32.3)\n",
+            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.66.6)\n",
+            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.12.2)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.4.0)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.10)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2.2.3)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2024.8.30)\n"
+          ]
+        }
+      ],
       "source": [
         "!pip install huggingface_hub"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 2,
+      "execution_count": 7,
       "metadata": {
         "id": "2I850FIsCVNw"
       },
@@ -45,39175 +68,711 @@
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
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+          "height": 67,
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+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import json\n",
+        "import os\n",
+        "from datetime import datetime\n",
+        "import re\n",
+        "import pandas as pd\n",
+        "from natsort import natsorted\n",
+        "\n",
+        "def parse_json(file_path):\n",
+        "    \"\"\"\n",
+        "    Parses the given JSON file and extracts the 'acc,none' value and 'model_name'.\n",
+        "    \"\"\"\n",
+        "    try:\n",
+        "        with open(file_path, 'r') as file:\n",
+        "            data = json.load(file)\n",
+        "\n",
+        "        # Extract 'acc,none' value\n",
+        "        results = data.get(\"results\", {})\n",
+        "        acc_value = None\n",
+        "        for key, value in results.items():\n",
+        "            if \"acc,none\" in value:\n",
+        "                acc_value = value[\"acc,none\"]\n",
+        "                break  # Assumes we only need the first match\n",
+        "\n",
+        "        # Extract 'model_name'\n",
+        "        model_name = data.get(\"model_name\")\n",
+        "\n",
+        "        return {\n",
+        "            \"model_name\": model_name,\n",
+        "            \"task\": key,\n",
+        "            \"acc_value\": acc_value\n",
+        "        }\n",
+        "\n",
+        "    except (json.JSONDecodeError, KeyError) as e:\n",
+        "        print(f\"Error parsing {file_path}: {e}\")\n",
+        "        return None\n",
+        "\n",
+        "\n",
+        "def extract_datetime(file_name):\n",
+        "    \"\"\"\n",
+        "    Extracts the datetime portion from a file name with the specified pattern.\n",
+        "    \"\"\"\n",
+        "    pattern = r\"results_(\\d{4}-\\d{2}-\\d{2}T\\d{2}-\\d{2}-\\d{2}\\.\\d+)\\.json\"\n",
+        "    match = re.search(pattern, file_name)\n",
+        "    if match:\n",
+        "        return match.group(1)  # Extract the first capturing group\n",
+        "    return None\n",
+        "\n",
+        "def process_json_files(base_directory):\n",
+        "    \"\"\"\n",
+        "    Processes all JSON files in the specified directory and its subdirectories.\n",
+        "    \"\"\"\n",
+        "    results = []\n",
+        "    for root, _, files in os.walk(base_directory):\n",
+        "        for file_name in files:\n",
+        "            if file_name.endswith(\".json\"):\n",
+        "                file_path = os.path.join(root, file_name)\n",
+        "                parsed_data = parse_json(file_path)\n",
+        "                parsed_data['datetime'] = extract_datetime(file_name)\n",
+        "                if parsed_data:\n",
+        "                    results.append(parsed_data)\n",
+        "\n",
+        "    return results\n",
+        "\n",
+        "# Example usage:\n",
+        "if __name__ == \"__main__\":\n",
+        "    directory_path = \"results/evals/output\"  # Update this to your JSON files directory\n",
+        "    extracted_data = process_json_files(directory_path)\n",
+        "    # extracted_data_sorted = sorted(extracted_data, key = lambda x: (x['model_name'], x['task'], x['datetime']))\n",
+        "    # for entry in extracted_data_sorted:\n",
+        "    #     print(f\"Model Name: {entry['model_name']}, Task {entry['task']}, Date {entry['datetime']},  Acc Value: {entry['acc_value']}\")\n",
+        "\n",
+        "    extracted_data_sorted = sorted(\n",
+        "        extracted_data,\n",
+        "        key=lambda x: (x['model_name'], x['task'], x['datetime'])\n",
+        "    )\n",
+        "\n",
+        "    # Group by (model_name, task) and keep only the latest entry\n",
+        "    latest_entries = {}\n",
+        "    for entry in extracted_data_sorted:\n",
+        "        key = (entry['model_name'], entry['task'])  # Group by model_name and task\n",
+        "        # Replace existing entry if current entry is newer\n",
+        "        if key not in latest_entries or entry['datetime'] > latest_entries[key]['datetime']:\n",
+        "            latest_entries[key] = entry\n",
+        "    latest_entries_result = []\n",
+        "    for key, entry in latest_entries.items():\n",
+        "        entry['acc_value'] = \"%.2f\" % round(entry['acc_value']*100, 2)\n",
+        "        latest_entries_result.append(entry)\n",
+        "\n",
+        "\n",
+        "    df = pd.DataFrame(latest_entries_result)\n",
+        "    pivot_table = df.pivot(index=\"model_name\", columns=\"task\", values=\"acc_value\")\n",
+        "    pivot_table = pivot_table[natsorted(pivot_table.columns)]\n",
+        "    # Display the latest entries\n",
+        "    for key, entry in latest_entries.items():\n",
+        "        print(f\"Model Name: {entry['model_name']}, Task: {entry['task']}, Date: {entry['datetime']}, Acc Value: {entry['acc_value']}\")\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
         },
+        "id": "b2DMzhWCUR4h",
+        "outputId": "9b69c0a9-24a1-4419-cb28-f9d9bdf48655"
+      },
+      "execution_count": 9,
+      "outputs": [
         {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "(…)/results_2024-11-24T14-16-57.794406.json:   0%|          | 0.00/6.78k [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "75e5b92e00b94454a044ca16d9b8fe13"
-            }
-          },
-          "metadata": {}
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Model Name: meta-llama/Llama-3.2-1B-Instruct, Task: mmlu_all, Date: 2024-12-05T10-07-32.229420, Acc Value: 34.13\n",
+            "Model Name: meta-llama/Llama-3.2-1B-Instruct, Task: pub_task_1, Date: 2024-12-04T09-46-13.895610, Acc Value: 58.60\n",
+            "Model Name: meta-llama/Llama-3.2-1B-Instruct, Task: pub_task_10, Date: 2024-12-04T10-27-24.033942, Acc Value: 78.10\n",
+            "Model Name: meta-llama/Llama-3.2-1B-Instruct, Task: pub_task_11, Date: 2024-12-04T10-30-42.581549, Acc Value: 31.56\n",
+            "Model Name: meta-llama/Llama-3.2-1B-Instruct, Task: pub_task_12, Date: 2024-12-04T10-34-08.691174, Acc Value: 84.12\n",
+            "Model Name: meta-llama/Llama-3.2-1B-Instruct, Task: pub_task_13, Date: 2024-12-04T10-36-54.246366, Acc Value: 48.40\n",
+            "Model Name: meta-llama/Llama-3.2-1B-Instruct, Task: pub_task_14, Date: 2024-12-04T10-37-56.290613, Acc Value: 33.62\n",
+            "Model Name: meta-llama/Llama-3.2-1B-Instruct, Task: pub_task_2, Date: 2024-12-04T09-50-36.416792, Acc Value: 51.20\n",
+            "Model Name: meta-llama/Llama-3.2-1B-Instruct, Task: pub_task_3, Date: 2024-12-04T09-55-52.575686, Acc Value: 57.42\n",
+            "Model Name: meta-llama/Llama-3.2-1B-Instruct, Task: pub_task_4, Date: 2024-12-04T10-08-54.537419, Acc Value: 24.70\n",
+            "Model Name: meta-llama/Llama-3.2-1B-Instruct, Task: pub_task_5, Date: 2024-12-04T10-10-41.137500, Acc Value: 49.90\n",
+            "Model Name: meta-llama/Llama-3.2-1B-Instruct, Task: pub_task_6, Date: 2024-12-04T10-12-31.411026, Acc Value: 50.05\n",
+            "Model Name: meta-llama/Llama-3.2-1B-Instruct, Task: pub_task_7, Date: 2024-12-04T10-15-47.808656, Acc Value: 51.98\n",
+            "Model Name: meta-llama/Llama-3.2-1B-Instruct, Task: pub_task_8, Date: 2024-12-04T10-19-48.272257, Acc Value: 55.31\n",
+            "Model Name: meta-llama/Llama-3.2-1B-Instruct, Task: pub_task_9, Date: 2024-12-04T10-23-50.268393, Acc Value: 52.20\n",
+            "Model Name: meta-llama/Llama-3.2-3B-Instruct, Task: mmlu_all, Date: 2024-12-05T10-28-27.223863, Acc Value: 53.23\n",
+            "Model Name: meta-llama/Llama-3.2-3B-Instruct, Task: pub_task_1, Date: 2024-12-04T11-03-01.529235, Acc Value: 81.04\n",
+            "Model Name: meta-llama/Llama-3.2-3B-Instruct, Task: pub_task_10, Date: 2024-12-04T12-31-04.414803, Acc Value: 75.81\n",
+            "Model Name: meta-llama/Llama-3.2-3B-Instruct, Task: pub_task_11, Date: 2024-12-04T12-37-54.346037, Acc Value: 33.67\n",
+            "Model Name: meta-llama/Llama-3.2-3B-Instruct, Task: pub_task_12, Date: 2024-12-04T12-44-45.900074, Acc Value: 53.30\n",
+            "Model Name: meta-llama/Llama-3.2-3B-Instruct, Task: pub_task_13, Date: 2024-12-04T12-50-17.737269, Acc Value: 70.00\n",
+            "Model Name: meta-llama/Llama-3.2-3B-Instruct, Task: pub_task_14, Date: 2024-12-04T12-52-04.030761, Acc Value: 59.83\n",
+            "Model Name: meta-llama/Llama-3.2-3B-Instruct, Task: pub_task_2, Date: 2024-12-04T11-11-51.908847, Acc Value: 75.30\n",
+            "Model Name: meta-llama/Llama-3.2-3B-Instruct, Task: pub_task_3, Date: 2024-12-04T11-22-49.948783, Acc Value: 77.69\n",
+            "Model Name: meta-llama/Llama-3.2-3B-Instruct, Task: pub_task_4, Date: 2024-12-04T11-52-43.868109, Acc Value: 53.65\n",
+            "Model Name: meta-llama/Llama-3.2-3B-Instruct, Task: pub_task_5, Date: 2024-12-04T11-56-03.314161, Acc Value: 69.05\n",
+            "Model Name: meta-llama/Llama-3.2-3B-Instruct, Task: pub_task_6, Date: 2024-12-04T11-59-30.170935, Acc Value: 63.55\n",
+            "Model Name: meta-llama/Llama-3.2-3B-Instruct, Task: pub_task_7, Date: 2024-12-04T12-06-20.816022, Acc Value: 78.64\n",
+            "Model Name: meta-llama/Llama-3.2-3B-Instruct, Task: pub_task_8, Date: 2024-12-04T12-15-00.410767, Acc Value: 92.54\n",
+            "Model Name: meta-llama/Llama-3.2-3B-Instruct, Task: pub_task_9, Date: 2024-12-04T12-23-42.320725, Acc Value: 59.94\n",
+            "Model Name: meta-llama/Meta-Llama-3-8B, Task: mmlu_all, Date: 2024-12-05T11-08-04.943164, Acc Value: 57.54\n",
+            "Model Name: meta-llama/Meta-Llama-3-8B, Task: pub_task_1, Date: 2024-12-04T13-39-12.867724, Acc Value: 79.84\n",
+            "Model Name: meta-llama/Meta-Llama-3-8B, Task: pub_task_10, Date: 2024-12-04T16-30-25.978146, Acc Value: 78.00\n",
+            "Model Name: meta-llama/Meta-Llama-3-8B, Task: pub_task_11, Date: 2024-12-04T16-43-34.533561, Acc Value: 41.67\n",
+            "Model Name: meta-llama/Meta-Llama-3-8B, Task: pub_task_12, Date: 2024-12-04T16-56-04.873961, Acc Value: 73.39\n",
+            "Model Name: meta-llama/Meta-Llama-3-8B, Task: pub_task_13, Date: 2024-12-04T17-05-59.454197, Acc Value: 77.50\n",
+            "Model Name: meta-llama/Meta-Llama-3-8B, Task: pub_task_14, Date: 2024-12-04T17-08-59.676663, Acc Value: 62.88\n",
+            "Model Name: meta-llama/Meta-Llama-3-8B, Task: pub_task_2, Date: 2024-12-04T13-56-19.017336, Acc Value: 79.61\n",
+            "Model Name: meta-llama/Meta-Llama-3-8B, Task: pub_task_3, Date: 2024-12-04T14-17-13.903279, Acc Value: 76.90\n",
+            "Model Name: meta-llama/Meta-Llama-3-8B, Task: pub_task_4, Date: 2024-12-04T15-17-10.760422, Acc Value: 50.00\n",
+            "Model Name: meta-llama/Meta-Llama-3-8B, Task: pub_task_5, Date: 2024-12-04T15-23-12.253601, Acc Value: 69.75\n",
+            "Model Name: meta-llama/Meta-Llama-3-8B, Task: pub_task_6, Date: 2024-12-04T15-29-24.543280, Acc Value: 52.45\n",
+            "Model Name: meta-llama/Meta-Llama-3-8B, Task: pub_task_7, Date: 2024-12-04T15-42-32.945279, Acc Value: 82.43\n",
+            "Model Name: meta-llama/Meta-Llama-3-8B, Task: pub_task_8, Date: 2024-12-04T15-59-19.291670, Acc Value: 95.14\n",
+            "Model Name: meta-llama/Meta-Llama-3-8B, Task: pub_task_9, Date: 2024-12-04T16-16-07.277328, Acc Value: 67.68\n",
+            "Model Name: mistralai/Mixtral-8x7B-Instruct-v0.1, Task: mmlu_all, Date: 2024-11-24T15-20-49.697273, Acc Value: 34.47\n",
+            "Model Name: mistralai/Mixtral-8x7B-Instruct-v0.1, Task: pub_task_1, Date: 2024-11-25T03-10-25.443130, Acc Value: 86.92\n",
+            "Model Name: mistralai/Mixtral-8x7B-Instruct-v0.1, Task: pub_task_10, Date: 2024-11-25T03-21-54.085274, Acc Value: 78.10\n",
+            "Model Name: mistralai/Mixtral-8x7B-Instruct-v0.1, Task: pub_task_11, Date: 2024-11-25T03-22-57.001476, Acc Value: 38.33\n",
+            "Model Name: mistralai/Mixtral-8x7B-Instruct-v0.1, Task: pub_task_12, Date: 2024-11-25T03-24-06.031245, Acc Value: 83.69\n",
+            "Model Name: mistralai/Mixtral-8x7B-Instruct-v0.1, Task: pub_task_13, Date: 2024-11-25T03-24-54.408538, Acc Value: 46.90\n",
+            "Model Name: mistralai/Mixtral-8x7B-Instruct-v0.1, Task: pub_task_14, Date: 2024-11-25T03-25-37.703112, Acc Value: 37.99\n",
+            "Model Name: mistralai/Mixtral-8x7B-Instruct-v0.1, Task: pub_task_2, Date: 2024-11-25T03-13-13.183813, Acc Value: 55.91\n",
+            "Model Name: mistralai/Mixtral-8x7B-Instruct-v0.1, Task: pub_task_3, Date: 2024-11-25T03-14-39.515145, Acc Value: 60.97\n",
+            "Model Name: mistralai/Mixtral-8x7B-Instruct-v0.1, Task: pub_task_4, Date: 2024-11-25T03-15-48.048668, Acc Value: 28.50\n",
+            "Model Name: mistralai/Mixtral-8x7B-Instruct-v0.1, Task: pub_task_5, Date: 2024-11-25T03-16-49.842533, Acc Value: 50.15\n",
+            "Model Name: mistralai/Mixtral-8x7B-Instruct-v0.1, Task: pub_task_6, Date: 2024-11-25T03-17-51.307160, Acc Value: 50.10\n",
+            "Model Name: mistralai/Mixtral-8x7B-Instruct-v0.1, Task: pub_task_7, Date: 2024-11-25T03-18-50.302136, Acc Value: 54.63\n",
+            "Model Name: mistralai/Mixtral-8x7B-Instruct-v0.1, Task: pub_task_8, Date: 2024-11-25T03-19-48.639957, Acc Value: 53.16\n",
+            "Model Name: mistralai/Mixtral-8x7B-Instruct-v0.1, Task: pub_task_9, Date: 2024-11-25T03-20-46.585027, Acc Value: 54.75\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "pivot_table\n",
+        "#pivot_table.to_html()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 365
         },
+        "id": "j9t2Oj7-Fgb3",
+        "outputId": "44fea3e2-6a6a-4ad8-c2c4-4cb3f8aafe7e"
+      },
+      "execution_count": 10,
+      "outputs": [
         {
-          "output_type": "display_data",
+          "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "(…)/results_2024-11-25T01-24-09.465256.json:   0%|          | 0.00/6.82k [00:00<?, ?B/s]"
+              "task                                 mmlu_all pub_task_1 pub_task_2  \\\n",
+              "model_name                                                            \n",
+              "meta-llama/Llama-3.2-1B-Instruct        34.13      58.60      51.20   \n",
+              "meta-llama/Llama-3.2-3B-Instruct        53.23      81.04      75.30   \n",
+              "meta-llama/Meta-Llama-3-8B              57.54      79.84      79.61   \n",
+              "mistralai/Mixtral-8x7B-Instruct-v0.1    34.47      86.92      55.91   \n",
+              "\n",
+              "task                                 pub_task_3 pub_task_4 pub_task_5  \\\n",
+              "model_name                                                              \n",
+              "meta-llama/Llama-3.2-1B-Instruct          57.42      24.70      49.90   \n",
+              "meta-llama/Llama-3.2-3B-Instruct          77.69      53.65      69.05   \n",
+              "meta-llama/Meta-Llama-3-8B                76.90      50.00      69.75   \n",
+              "mistralai/Mixtral-8x7B-Instruct-v0.1      60.97      28.50      50.15   \n",
+              "\n",
+              "task                                 pub_task_6 pub_task_7 pub_task_8  \\\n",
+              "model_name                                                              \n",
+              "meta-llama/Llama-3.2-1B-Instruct          50.05      51.98      55.31   \n",
+              "meta-llama/Llama-3.2-3B-Instruct          63.55      78.64      92.54   \n",
+              "meta-llama/Meta-Llama-3-8B                52.45      82.43      95.14   \n",
+              "mistralai/Mixtral-8x7B-Instruct-v0.1      50.10      54.63      53.16   \n",
+              "\n",
+              "task                                 pub_task_9 pub_task_10 pub_task_11  \\\n",
+              "model_name                                                                \n",
+              "meta-llama/Llama-3.2-1B-Instruct          52.20       78.10       31.56   \n",
+              "meta-llama/Llama-3.2-3B-Instruct          59.94       75.81       33.67   \n",
+              "meta-llama/Meta-Llama-3-8B                67.68       78.00       41.67   \n",
+              "mistralai/Mixtral-8x7B-Instruct-v0.1      54.75       78.10       38.33   \n",
+              "\n",
+              "task                                 pub_task_12 pub_task_13 pub_task_14  \n",
+              "model_name                                                                \n",
+              "meta-llama/Llama-3.2-1B-Instruct           84.12       48.40       33.62  \n",
+              "meta-llama/Llama-3.2-3B-Instruct           53.30       70.00       59.83  \n",
+              "meta-llama/Meta-Llama-3-8B                 73.39       77.50       62.88  \n",
+              "mistralai/Mixtral-8x7B-Instruct-v0.1       83.69       46.90       37.99  "
             ],
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-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "(…)/results_2024-11-25T01-37-19.887934.json:   0%|          | 0.00/6.84k [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
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-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "(…)/results_2024-11-25T01-31-02.772412.json:   0%|          | 0.00/6.81k [00:00<?, ?B/s]"
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-          "data": {
-            "text/plain": [
-              "(…)/results_2024-11-25T01-42-58.651528.json:   0%|          | 0.00/6.84k [00:00<?, ?B/s]"
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-            "application/vnd.jupyter.widget-view+json": {
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-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "(…)/results_2024-11-25T01-52-04.439565.json:   0%|          | 0.00/6.80k [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
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-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "(…)/results_2024-12-02T21-59-16.367056.json:   0%|          | 0.00/7.04k [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
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-          "metadata": {}
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "'/content/results'"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "string"
-            }
-          },
-          "metadata": {},
-          "execution_count": 3
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import json\n",
-        "import os\n",
-        "from datetime import datetime\n",
-        "import re\n",
-        "import pandas as pd\n",
-        "from natsort import natsorted\n",
-        "\n",
-        "def parse_json(file_path):\n",
-        "    \"\"\"\n",
-        "    Parses the given JSON file and extracts the 'acc,none' value and 'model_name'.\n",
-        "    \"\"\"\n",
-        "    try:\n",
-        "        with open(file_path, 'r') as file:\n",
-        "            data = json.load(file)\n",
-        "\n",
-        "        # Extract 'acc,none' value\n",
-        "        results = data.get(\"results\", {})\n",
-        "        acc_value = None\n",
-        "        for key, value in results.items():\n",
-        "            if \"acc,none\" in value:\n",
-        "                acc_value = value[\"acc,none\"]\n",
-        "                break  # Assumes we only need the first match\n",
-        "\n",
-        "        # Extract 'model_name'\n",
-        "        model_name = data.get(\"model_name\")\n",
-        "\n",
-        "        return {\n",
-        "            \"model_name\": model_name,\n",
-        "            \"task\": key,\n",
-        "            \"acc_value\": acc_value\n",
-        "        }\n",
-        "\n",
-        "    except (json.JSONDecodeError, KeyError) as e:\n",
-        "        print(f\"Error parsing {file_path}: {e}\")\n",
-        "        return None\n",
-        "\n",
-        "\n",
-        "def extract_datetime(file_name):\n",
-        "    \"\"\"\n",
-        "    Extracts the datetime portion from a file name with the specified pattern.\n",
-        "    \"\"\"\n",
-        "    pattern = r\"results_(\\d{4}-\\d{2}-\\d{2}T\\d{2}-\\d{2}-\\d{2}\\.\\d+)\\.json\"\n",
-        "    match = re.search(pattern, file_name)\n",
-        "    if match:\n",
-        "        return match.group(1)  # Extract the first capturing group\n",
-        "    return None\n",
-        "\n",
-        "def process_json_files(base_directory):\n",
-        "    \"\"\"\n",
-        "    Processes all JSON files in the specified directory and its subdirectories.\n",
-        "    \"\"\"\n",
-        "    results = []\n",
-        "    for root, _, files in os.walk(base_directory):\n",
-        "        for file_name in files:\n",
-        "            if file_name.endswith(\".json\"):\n",
-        "                file_path = os.path.join(root, file_name)\n",
-        "                parsed_data = parse_json(file_path)\n",
-        "                parsed_data['datetime'] = extract_datetime(file_name)\n",
-        "                if parsed_data:\n",
-        "                    results.append(parsed_data)\n",
-        "\n",
-        "    return results\n",
-        "\n",
-        "# Example usage:\n",
-        "if __name__ == \"__main__\":\n",
-        "    directory_path = \"results/evals/output\"  # Update this to your JSON files directory\n",
-        "    extracted_data = process_json_files(directory_path)\n",
-        "    # extracted_data_sorted = sorted(extracted_data, key = lambda x: (x['model_name'], x['task'], x['datetime']))\n",
-        "    # for entry in extracted_data_sorted:\n",
-        "    #     print(f\"Model Name: {entry['model_name']}, Task {entry['task']}, Date {entry['datetime']},  Acc Value: {entry['acc_value']}\")\n",
-        "\n",
-        "    extracted_data_sorted = sorted(\n",
-        "        extracted_data,\n",
-        "        key=lambda x: (x['model_name'], x['task'], x['datetime'])\n",
-        "    )\n",
-        "\n",
-        "    # Group by (model_name, task) and keep only the latest entry\n",
-        "    latest_entries = {}\n",
-        "    for entry in extracted_data_sorted:\n",
-        "        key = (entry['model_name'], entry['task'])  # Group by model_name and task\n",
-        "        # Replace existing entry if current entry is newer\n",
-        "        if key not in latest_entries or entry['datetime'] > latest_entries[key]['datetime']:\n",
-        "            latest_entries[key] = entry\n",
-        "    latest_entries_result = []\n",
-        "    for key, entry in latest_entries.items():\n",
-        "        entry['acc_value'] = \"%.2f\" % round(entry['acc_value']*100, 2)\n",
-        "        latest_entries_result.append(entry)\n",
-        "\n",
-        "\n",
-        "    df = pd.DataFrame(latest_entries_result)\n",
-        "    pivot_table = df.pivot(index=\"model_name\", columns=\"task\", values=\"acc_value\")\n",
-        "    pivot_table = pivot_table[natsorted(pivot_table.columns)]\n",
-        "    # Display the latest entries\n",
-        "    # for key, entry in latest_entries.items():\n",
-        "    #     print(f\"Model Name: {entry['model_name']}, Task: {entry['task']}, Date: {entry['datetime']}, Acc Value: {entry['acc_value']}\")\n",
-        "\n"
-      ],
-      "metadata": {
-        "id": "b2DMzhWCUR4h"
-      },
-      "execution_count": 4,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "pivot_table\n",
-        "#pivot_table.to_html()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 365
-        },
-        "id": "j9t2Oj7-Fgb3",
-        "outputId": "171ac97b-bfa1-4456-f6ac-c207293936f5"
-      },
-      "execution_count": 5,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "task                                 mmlu_all pub_task_1 pub_task_2  \\\n",
-              "model_name                                                            \n",
-              "meta-llama/Llama-3.2-1B-Instruct        34.65      58.60      51.20   \n",
-              "meta-llama/Llama-3.2-3B-Instruct        52.19      81.04      75.30   \n",
-              "meta-llama/Meta-Llama-3-8B              53.65      79.84      79.61   \n",
-              "mistralai/Mixtral-8x7B-Instruct-v0.1    34.47      86.92      55.91   \n",
-              "\n",
-              "task                                 pub_task_3 pub_task_4 pub_task_5  \\\n",
-              "model_name                                                              \n",
-              "meta-llama/Llama-3.2-1B-Instruct          57.42      24.70      49.90   \n",
-              "meta-llama/Llama-3.2-3B-Instruct          77.69      53.65      69.05   \n",
-              "meta-llama/Meta-Llama-3-8B                76.90      50.00      69.75   \n",
-              "mistralai/Mixtral-8x7B-Instruct-v0.1      60.97      28.50      50.15   \n",
-              "\n",
-              "task                                 pub_task_6 pub_task_7 pub_task_8  \\\n",
-              "model_name                                                              \n",
-              "meta-llama/Llama-3.2-1B-Instruct          50.05      51.98      55.31   \n",
-              "meta-llama/Llama-3.2-3B-Instruct          63.55      78.64      92.54   \n",
-              "meta-llama/Meta-Llama-3-8B                52.45      82.43      95.14   \n",
-              "mistralai/Mixtral-8x7B-Instruct-v0.1      50.10      54.63      53.16   \n",
-              "\n",
-              "task                                 pub_task_9 pub_task_10 pub_task_11  \\\n",
-              "model_name                                                                \n",
-              "meta-llama/Llama-3.2-1B-Instruct          52.20       78.10       31.56   \n",
-              "meta-llama/Llama-3.2-3B-Instruct          59.94       75.81       33.67   \n",
-              "meta-llama/Meta-Llama-3-8B                67.68       78.00       41.67   \n",
-              "mistralai/Mixtral-8x7B-Instruct-v0.1      54.75       78.10       38.33   \n",
-              "\n",
-              "task                                 pub_task_12 pub_task_13 pub_task_14  \n",
-              "model_name                                                                \n",
-              "meta-llama/Llama-3.2-1B-Instruct           84.12       48.40       33.62  \n",
-              "meta-llama/Llama-3.2-3B-Instruct           53.30       70.00       59.83  \n",
-              "meta-llama/Meta-Llama-3-8B                 73.39       77.50       62.88  \n",
-              "mistralai/Mixtral-8x7B-Instruct-v0.1       83.69       46.90       37.99  "
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-ae956774-08a1-4d39-96f2-f37f10a86f59\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th>task</th>\n",
-              "      <th>mmlu_all</th>\n",
-              "      <th>pub_task_1</th>\n",
-              "      <th>pub_task_2</th>\n",
-              "      <th>pub_task_3</th>\n",
-              "      <th>pub_task_4</th>\n",
-              "      <th>pub_task_5</th>\n",
-              "      <th>pub_task_6</th>\n",
-              "      <th>pub_task_7</th>\n",
-              "      <th>pub_task_8</th>\n",
-              "      <th>pub_task_9</th>\n",
-              "      <th>pub_task_10</th>\n",
-              "      <th>pub_task_11</th>\n",
-              "      <th>pub_task_12</th>\n",
-              "      <th>pub_task_13</th>\n",
-              "      <th>pub_task_14</th>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>model_name</th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>meta-llama/Llama-3.2-1B-Instruct</th>\n",
-              "      <td>34.65</td>\n",
-              "      <td>58.60</td>\n",
-              "      <td>51.20</td>\n",
-              "      <td>57.42</td>\n",
-              "      <td>24.70</td>\n",
-              "      <td>49.90</td>\n",
-              "      <td>50.05</td>\n",
-              "      <td>51.98</td>\n",
-              "      <td>55.31</td>\n",
-              "      <td>52.20</td>\n",
-              "      <td>78.10</td>\n",
-              "      <td>31.56</td>\n",
-              "      <td>84.12</td>\n",
-              "      <td>48.40</td>\n",
-              "      <td>33.62</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>meta-llama/Llama-3.2-3B-Instruct</th>\n",
-              "      <td>52.19</td>\n",
-              "      <td>81.04</td>\n",
-              "      <td>75.30</td>\n",
-              "      <td>77.69</td>\n",
-              "      <td>53.65</td>\n",
-              "      <td>69.05</td>\n",
-              "      <td>63.55</td>\n",
-              "      <td>78.64</td>\n",
-              "      <td>92.54</td>\n",
-              "      <td>59.94</td>\n",
-              "      <td>75.81</td>\n",
-              "      <td>33.67</td>\n",
-              "      <td>53.30</td>\n",
-              "      <td>70.00</td>\n",
-              "      <td>59.83</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>meta-llama/Meta-Llama-3-8B</th>\n",
-              "      <td>53.65</td>\n",
-              "      <td>79.84</td>\n",
-              "      <td>79.61</td>\n",
-              "      <td>76.90</td>\n",
-              "      <td>50.00</td>\n",
-              "      <td>69.75</td>\n",
-              "      <td>52.45</td>\n",
-              "      <td>82.43</td>\n",
-              "      <td>95.14</td>\n",
-              "      <td>67.68</td>\n",
-              "      <td>78.00</td>\n",
-              "      <td>41.67</td>\n",
-              "      <td>73.39</td>\n",
-              "      <td>77.50</td>\n",
-              "      <td>62.88</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>mistralai/Mixtral-8x7B-Instruct-v0.1</th>\n",
-              "      <td>34.47</td>\n",
-              "      <td>86.92</td>\n",
-              "      <td>55.91</td>\n",
-              "      <td>60.97</td>\n",
-              "      <td>28.50</td>\n",
-              "      <td>50.15</td>\n",
-              "      <td>50.10</td>\n",
-              "      <td>54.63</td>\n",
-              "      <td>53.16</td>\n",
-              "      <td>54.75</td>\n",
-              "      <td>78.10</td>\n",
-              "      <td>38.33</td>\n",
-              "      <td>83.69</td>\n",
-              "      <td>46.90</td>\n",
-              "      <td>37.99</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ae956774-08a1-4d39-96f2-f37f10a86f59')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-ae956774-08a1-4d39-96f2-f37f10a86f59 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-ae956774-08a1-4d39-96f2-f37f10a86f59');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-c58d11b1-109e-4e77-9349-33cfbf55bc0c\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c58d11b1-109e-4e77-9349-33cfbf55bc0c')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
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-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
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+              "      <td>78.64</td>\n",
+              "      <td>92.54</td>\n",
+              "      <td>59.94</td>\n",
+              "      <td>75.81</td>\n",
+              "      <td>33.67</td>\n",
+              "      <td>53.30</td>\n",
+              "      <td>70.00</td>\n",
+              "      <td>59.83</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>meta-llama/Meta-Llama-3-8B</th>\n",
+              "      <td>57.54</td>\n",
+              "      <td>79.84</td>\n",
+              "      <td>79.61</td>\n",
+              "      <td>76.90</td>\n",
+              "      <td>50.00</td>\n",
+              "      <td>69.75</td>\n",
+              "      <td>52.45</td>\n",
+              "      <td>82.43</td>\n",
+              "      <td>95.14</td>\n",
+              "      <td>67.68</td>\n",
+              "      <td>78.00</td>\n",
+              "      <td>41.67</td>\n",
+              "      <td>73.39</td>\n",
+              "      <td>77.50</td>\n",
+              "      <td>62.88</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mistralai/Mixtral-8x7B-Instruct-v0.1</th>\n",
+              "      <td>34.47</td>\n",
+              "      <td>86.92</td>\n",
+              "      <td>55.91</td>\n",
+              "      <td>60.97</td>\n",
+              "      <td>28.50</td>\n",
+              "      <td>50.15</td>\n",
+              "      <td>50.10</td>\n",
+              "      <td>54.63</td>\n",
+              "      <td>53.16</td>\n",
+              "      <td>54.75</td>\n",
+              "      <td>78.10</td>\n",
+              "      <td>38.33</td>\n",
+              "      <td>83.69</td>\n",
+              "      <td>46.90</td>\n",
+              "      <td>37.99</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7434b277-cc7d-4a83-b15f-d9b7400c29fd')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-7434b277-cc7d-4a83-b15f-d9b7400c29fd button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-7434b277-cc7d-4a83-b15f-d9b7400c29fd');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-f6472cf9-fbb8-4ac5-b170-2e69c96638c3\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f6472cf9-fbb8-4ac5-b170-2e69c96638c3')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-f6472cf9-fbb8-4ac5-b170-2e69c96638c3 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_07e34785-74ff-4bbf-a4e0-08ff08e251e5\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('pivot_table')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_07e34785-74ff-4bbf-a4e0-08ff08e251e5 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('pivot_table');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "pivot_table",
+              "summary": "{\n  \"name\": \"pivot_table\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"model_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"meta-llama/Llama-3.2-3B-Instruct\",\n          \"mistralai/Mixtral-8x7B-Instruct-v0.1\",\n          \"meta-llama/Llama-3.2-1B-Instruct\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mmlu_all\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"53.23\",\n          \"34.47\",\n          \"34.13\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pub_task_1\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"81.04\",\n          \"86.92\",\n          \"58.60\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pub_task_2\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"75.30\",\n          \"55.91\",\n          \"51.20\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pub_task_3\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"77.69\",\n          \"60.97\",\n          \"57.42\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pub_task_4\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"53.65\",\n          \"28.50\",\n          \"24.70\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pub_task_5\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"69.05\",\n          \"50.15\",\n          \"49.90\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pub_task_6\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"63.55\",\n          \"50.10\",\n          \"50.05\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pub_task_7\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"78.64\",\n          \"54.63\",\n          \"51.98\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pub_task_8\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"92.54\",\n          \"53.16\",\n          \"55.31\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pub_task_9\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"59.94\",\n          \"54.75\",\n          \"52.20\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pub_task_10\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"78.10\",\n          \"75.81\",\n          \"78.00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pub_task_11\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"33.67\",\n          \"38.33\",\n          \"31.56\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pub_task_12\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"53.30\",\n          \"83.69\",\n          \"84.12\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pub_task_13\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"70.00\",\n          \"46.90\",\n          \"48.40\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pub_task_14\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"59.83\",\n          \"37.99\",\n          \"33.62\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 10
+        }
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "machine_shape": "hm",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "widgets": {
+      "application/vnd.jupyter.widget-state+json": {
+        "059f76f0b2ec4abaa93983f698501853": {
           "model_module": "@jupyter-widgets/controls",
           "model_name": "HBoxModel",
           "model_module_version": "1.5.0",
@@ -39228,14 +787,14 @@
             "_view_name": "HBoxView",
             "box_style": "",
             "children": [
-              "IPY_MODEL_8f01f03fe5f84118ac3adcd5af77c6ae",
-              "IPY_MODEL_2d999e3bceee46a58e3df96c091b6982",
-              "IPY_MODEL_bbddbbf035514730aab0abd48794900f"
+              "IPY_MODEL_ee288cd780cd4d69b52b79c4bfd93a8b",
+              "IPY_MODEL_45c16ad788d34a83ba05efdf19a1bf0f",
+              "IPY_MODEL_a074e3d188e64797906358c21cd5a79b"
             ],
-            "layout": "IPY_MODEL_f8eecc3b5e804225a7b1885aa436e250"
+            "layout": "IPY_MODEL_5a891e46d6e94060b7c1b23798cf4063"
           }
         },
-        "8f01f03fe5f84118ac3adcd5af77c6ae": {
+        "ee288cd780cd4d69b52b79c4bfd93a8b": {
           "model_module": "@jupyter-widgets/controls",
           "model_name": "HTMLModel",
           "model_module_version": "1.5.0",
@@ -39250,13 +809,13 @@
             "_view_name": "HTMLView",
             "description": "",
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