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"
@@ -11,31 +11,31 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 6,
+      "execution_count": 1,
       "metadata": {
         "id": "kGW7vfRkrqHe",
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "8a12c95a-e99b-4dfe-b973-a093f2f1ab25"
+        "outputId": "b28824a9-de8c-4f8c-da38-b00f8388c23b"
       },
       "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"
+            "Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.11/dist-packages (0.28.1)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from huggingface_hub) (3.17.0)\n",
+            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.11/dist-packages (from huggingface_hub) (2024.10.0)\n",
+            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.11/dist-packages (from huggingface_hub) (24.2)\n",
+            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.11/dist-packages (from huggingface_hub) (6.0.2)\n",
+            "Requirement already satisfied: requests in /usr/local/lib/python3.11/dist-packages (from huggingface_hub) (2.32.3)\n",
+            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.11/dist-packages (from huggingface_hub) (4.67.1)\n",
+            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.11/dist-packages (from huggingface_hub) (4.12.2)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface_hub) (3.4.1)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface_hub) (3.10)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface_hub) (2.3.0)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface_hub) (2025.1.31)\n"
           ]
         }
       ],
@@ -45,7 +45,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 7,
+      "execution_count": 3,
       "metadata": {
         "id": "2I850FIsCVNw"
       },
@@ -55,7 +55,7 @@
         "from google.colab import userdata\n",
         "\n",
         "HF_TOKEN = userdata.get('HF_TOKEN')\n",
-        "login(HF_TOKEN, True)\n",
+        "login(HF_TOKEN, add_to_git_credential=True)\n",
         "REPO_ID='flunardelli/llm-metaeval'"
       ]
     },
@@ -68,25 +68,1213 @@
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 67,
+          "height": 1000,
           "referenced_widgets": [
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           ]
         },
         "id": "x5NxgFgRNKz-",
-        "outputId": "a53d5809-d136-4bc0-a96e-4f78a2d9b38a"
+        "outputId": "f87ff73b-dfb2-435b-c108-b0dc753ff8e8"
       },
-      "execution_count": 11,
+      "execution_count": 4,
       "outputs": [
         {
           "output_type": "display_data",
@@ -97,285 +1285,1782 @@
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "059f76f0b2ec4abaa93983f698501853"
+              "model_id": "9cc2bac65bce4700a5ef55d371122860"
             }
           },
           "metadata": {}
         },
         {
-          "output_type": "execute_result",
+          "output_type": "display_data",
           "data": {
             "text/plain": [
-              "'/content/results'"
+              "(…)Fresults_2024-11-25T00-33-16.122975.json:   0%|          | 0.00/6.86k [00:00<?, ?B/s]"
             ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "string"
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "849b0237b3c94d7a8e6d82b3d3888ed3"
             }
           },
-          "metadata": {},
-          "execution_count": 11
-        }
-      ]
-    },
-    {
-      "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/"
+          "metadata": {}
         },
-        "id": "b2DMzhWCUR4h",
-        "outputId": "9b69c0a9-24a1-4419-cb28-f9d9bdf48655"
-      },
-      "execution_count": 9,
-      "outputs": [
         {
-          "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
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "(…)Fresults_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": "aba77fff49cc4b6297ee93dfc87c1218"
+            }
+          },
+          "metadata": {}
         },
-        "id": "j9t2Oj7-Fgb3",
-        "outputId": "44fea3e2-6a6a-4ad8-c2c4-4cb3f8aafe7e"
-      },
-      "execution_count": 10,
-      "outputs": [
         {
-          "output_type": "execute_result",
+          "output_type": "display_data",
           "data": {
             "text/plain": [
-              "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  "
+              "(…)Fresults_2024-11-25T01-09-00.710251.json:   0%|          | 0.00/6.79k [00:00<?, ?B/s]"
             ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-7434b277-cc7d-4a83-b15f-d9b7400c29fd\" 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",
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "54bafefc748541d0bd117f6c413149f1"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "(…)Fresults_2024-11-25T00-25-44.330296.json:   0%|          | 0.00/6.79k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "998595bf51844320a5518d976d127f8d"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
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+            ],
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+        },
+        {
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+              "(…)Fresults_2024-11-25T03-24-54.408538.json:   0%|          | 0.00/6.76k [00:00<?, ?B/s]"
+            ],
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "(…)Fresults_2024-11-25T03-18-50.302136.json:   0%|          | 0.00/6.78k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "(…)Fresults_2024-11-25T03-21-54.085274.json:   0%|          | 0.00/6.81k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "ee0bd5356f2b4684a6ecaa4f3ee16c9f"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "(…)Fresults_2024-11-25T03-22-57.001476.json:   0%|          | 0.00/6.81k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "021ab4d9d8af41a0981ef9034f334d3c"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'/content/results'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "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",
+        "    try:\n",
+        "        with open(file_path, 'r') as file:\n",
+        "            data = json.load(file)\n",
+        "\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\n",
+        "\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",
+        "    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",
+        "    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",
+        "if __name__ == \"__main__\":\n",
+        "    directory_path = \"results/evals/output\"\n",
+        "    extracted_data = process_json_files(directory_path)\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": "2cbfec61-4612-4d81-f53e-49bf51847dbe"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "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": null,
+      "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        34.13      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  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-7434b277-cc7d-4a83-b15f-d9b7400c29fd\" 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",
@@ -746,33 +3431,36969 @@
               "    </div>\n",
               "  </div>\n"
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\"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": {
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+              "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          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    \"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      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+            }
+          },
+          "metadata": {},
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           "model_module_version": "1.2.0",
@@ -1099,7 +40720,7 @@
             "width": null
           }
         },
-        "e0836a02c6ec4c229b5f3d93bc0c2475": {
+        "66847012aa5442049a8c35493a750603": {
           "model_module": "@jupyter-widgets/controls",
           "model_name": "DescriptionStyleModel",
           "model_module_version": "1.5.0",