diff --git "a/notebooks/HuggingFace_Demonstration.ipynb" "b/notebooks/HuggingFace_Demonstration.ipynb"
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+++ "b/notebooks/HuggingFace_Demonstration.ipynb"
@@ -0,0 +1,15538 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### This notebook demonstrates how to use Huggingface to download models and dataset, run inference, and create an output JSON to load into InspectorRAGet\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "vgJfAJtXeIQF",
+        "outputId": "0205e632-37c8-4a41-9795-71bf39061574"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/542.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m276.5/542.0 kB\u001b[0m \u001b[31m8.1 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m542.0/542.0 kB\u001b[0m \u001b[31m8.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.1/84.1 kB\u001b[0m \u001b[31m6.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m297.6/297.6 kB\u001b[0m \u001b[31m12.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m9.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m11.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m11.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m388.9/388.9 kB\u001b[0m \u001b[31m16.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install -q transformers[torch] datasets evaluate"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "###\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "c3c02Xzymeb1",
+        "outputId": "6b7baf2d-37db-4532-d9f0-b2482190daa2"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -q bitsandbytes trl peft"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "5MV4IBA6GGtg"
+      },
+      "outputs": [],
+      "source": [
+        "# import locale\n",
+        "# locale.getpreferredencoding = lambda: \"UTF-8\""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "aerzCfPej6Oe",
+        "outputId": "d32b04b9-45d0-4a93-f1ec-e31362e4d9d1"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Collecting bert_score\n",
+            "  Downloading bert_score-0.3.13-py3-none-any.whl (61 kB)\n",
+            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/61.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m61.1/61.1 kB\u001b[0m \u001b[31m2.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: torch>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from bert_score) (2.2.1+cu121)\n",
+            "Requirement already satisfied: pandas>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from bert_score) (2.0.3)\n",
+            "Requirement already satisfied: transformers>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from bert_score) (4.40.0)\n",
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from bert_score) (1.25.2)\n",
+            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from bert_score) (2.31.0)\n",
+            "Requirement already satisfied: tqdm>=4.31.1 in /usr/local/lib/python3.10/dist-packages (from bert_score) (4.66.2)\n",
+            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from bert_score) (3.7.1)\n",
+            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from bert_score) (24.0)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.0.1->bert_score) (2.8.2)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.0.1->bert_score) (2023.4)\n",
+            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.0.1->bert_score) (2024.1)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.0.0->bert_score) (3.13.4)\n",
+            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.0.0->bert_score) (4.11.0)\n",
+            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.0.0->bert_score) (1.12)\n",
+            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.0.0->bert_score) (3.3)\n",
+            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.0.0->bert_score) (3.1.3)\n",
+            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.0.0->bert_score) (2023.6.0)\n",
+            "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.0.0->bert_score) (12.1.105)\n",
+            "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.0.0->bert_score) (12.1.105)\n",
+            "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.0.0->bert_score) (12.1.105)\n",
+            "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /usr/local/lib/python3.10/dist-packages (from torch>=1.0.0->bert_score) (8.9.2.26)\n",
+            "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.10/dist-packages (from torch>=1.0.0->bert_score) (12.1.3.1)\n",
+            "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch>=1.0.0->bert_score) (11.0.2.54)\n",
+            "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch>=1.0.0->bert_score) (10.3.2.106)\n",
+            "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch>=1.0.0->bert_score) (11.4.5.107)\n",
+            "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch>=1.0.0->bert_score) (12.1.0.106)\n",
+            "Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /usr/local/lib/python3.10/dist-packages (from torch>=1.0.0->bert_score) (2.19.3)\n",
+            "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.0.0->bert_score) (12.1.105)\n",
+            "Requirement already satisfied: triton==2.2.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.0.0->bert_score) (2.2.0)\n",
+            "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.0.0->bert_score) (12.4.127)\n",
+            "Requirement already satisfied: huggingface-hub<1.0,>=0.19.3 in /usr/local/lib/python3.10/dist-packages (from transformers>=3.0.0->bert_score) (0.22.2)\n",
+            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers>=3.0.0->bert_score) (6.0.1)\n",
+            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers>=3.0.0->bert_score) (2023.12.25)\n",
+            "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers>=3.0.0->bert_score) (0.19.1)\n",
+            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers>=3.0.0->bert_score) (0.4.3)\n",
+            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->bert_score) (1.2.1)\n",
+            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->bert_score) (0.12.1)\n",
+            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->bert_score) (4.51.0)\n",
+            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->bert_score) (1.4.5)\n",
+            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->bert_score) (9.4.0)\n",
+            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->bert_score) (3.1.2)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->bert_score) (3.3.2)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->bert_score) (3.7)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->bert_score) (2.0.7)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->bert_score) (2024.2.2)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas>=1.0.1->bert_score) (1.16.0)\n",
+            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.0.0->bert_score) (2.1.5)\n",
+            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.0.0->bert_score) (1.3.0)\n",
+            "Installing collected packages: bert_score\n",
+            "Successfully installed bert_score-0.3.13\n",
+            "Collecting sacrebleu\n",
+            "  Downloading sacrebleu-2.4.2-py3-none-any.whl (106 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m106.7/106.7 kB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting portalocker (from sacrebleu)\n",
+            "  Downloading portalocker-2.8.2-py3-none-any.whl (17 kB)\n",
+            "Requirement already satisfied: regex in /usr/local/lib/python3.10/dist-packages (from sacrebleu) (2023.12.25)\n",
+            "Requirement already satisfied: tabulate>=0.8.9 in /usr/local/lib/python3.10/dist-packages (from sacrebleu) (0.9.0)\n",
+            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from sacrebleu) (1.25.2)\n",
+            "Collecting colorama (from sacrebleu)\n",
+            "  Downloading colorama-0.4.6-py2.py3-none-any.whl (25 kB)\n",
+            "Requirement already satisfied: lxml in /usr/local/lib/python3.10/dist-packages (from sacrebleu) (4.9.4)\n",
+            "Installing collected packages: portalocker, colorama, sacrebleu\n",
+            "Successfully installed colorama-0.4.6 portalocker-2.8.2 sacrebleu-2.4.2\n",
+            "Collecting rouge_score\n",
+            "  Downloading rouge_score-0.1.2.tar.gz (17 kB)\n",
+            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "Requirement already satisfied: absl-py in /usr/local/lib/python3.10/dist-packages (from rouge_score) (1.4.0)\n",
+            "Requirement already satisfied: nltk in /usr/local/lib/python3.10/dist-packages (from rouge_score) (3.8.1)\n",
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from rouge_score) (1.25.2)\n",
+            "Requirement already satisfied: six>=1.14.0 in /usr/local/lib/python3.10/dist-packages (from rouge_score) (1.16.0)\n",
+            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from nltk->rouge_score) (8.1.7)\n",
+            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from nltk->rouge_score) (1.4.0)\n",
+            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.10/dist-packages (from nltk->rouge_score) (2023.12.25)\n",
+            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from nltk->rouge_score) (4.66.2)\n",
+            "Building wheels for collected packages: rouge_score\n",
+            "  Building wheel for rouge_score (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for rouge_score: filename=rouge_score-0.1.2-py3-none-any.whl size=24933 sha256=fd2344488ebe8d4a55cdb3a8a3ce4e9c221a09839dd5880d6db0ec9aaa55452d\n",
+            "  Stored in directory: /root/.cache/pip/wheels/5f/dd/89/461065a73be61a532ff8599a28e9beef17985c9e9c31e541b4\n",
+            "Successfully built rouge_score\n",
+            "Installing collected packages: rouge_score\n",
+            "Successfully installed rouge_score-0.1.2\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install bert_score\n",
+        "!pip install sacrebleu\n",
+        "!pip install rouge_score"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dbCJkmVobi2k"
+      },
+      "source": [
+        "# Load Dataset\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 540,
+          "referenced_widgets": [
+            "eedfd80b50674748a0f104bf65ffe5ed",
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+            "8b00fb8c3be44808af565b42133901d1",
+            "463334a1d7234e82963d646113949881",
+            "43cf36c059b2437182cdfc3c7d9059f5",
+            "bf257efcc69e4225b171a67e98e44660",
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+            "1971de967cca4ff487d0b72e9c34c685",
+            "ecb114c56fd94391adfb1f5add22b78f",
+            "e815bbac8d7148f6a26544f28e967ce9",
+            "9f853f5fb1dc4400a5cd9b7fba2da242",
+            "c05058ba4c334a39947979e5e27774f6",
+            "afeffa17060f491ea93685651086ec59",
+            "cc396e921b104bd2928e57ca187880d8",
+            "e35006719a154d5e901541aa38496e79",
+            "fd6568cd2fb1409594e3745e2af81b93",
+            "51b5d494e75040f2a5d3b98a8e121faa",
+            "6eb7528aceca40809308b690250bb734",
+            "426a6e5667ee48ea94c05f662fb9dd59",
+            "5a117c1f300244d181fbb27ed5e4c300",
+            "0675dbcb10f1477299523e86399077c8",
+            "4a8a3d4b77fc4d3187b353664e89d13d",
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+            "d7d00d3fe79b44cdb9e4eeffa7f62628",
+            "059b9a6ee6174e5f95271e3d51ccdf02",
+            "b8ff16ac71484381b74e210c3d1df920",
+            "c48f10ac44d54544a09cc4c4e112e103",
+            "4a0aa9d6db5d47b2a6362681086dc367",
+            "01705be8e95543a5aae4b5efeec958bc",
+            "8c63b1708b4a45e2934f4e03cf8be0d6",
+            "609ae7ce0e604f35a4440a75529c38ad",
+            "156f2e415a674e58b8c4cb721aeaf8a0",
+            "92cc910350094cd5bec853eb6a6cdef6",
+            "783aff8ebb57446e902506a690005f60",
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+            "ec4ec68f9d6a4c81a8e639a4b205dd36",
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+            "abc88ad9ea724caebd05c8979b19b90e",
+            "5c68388c272a44179cd2211937e6b68b"
+          ]
+        },
+        "id": "0O-A6ifpbcbm",
+        "outputId": "f9fcc745-15b0-448d-98c7-3e8417b36144"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
+            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+            "You will be able to reuse this secret in all of your notebooks.\n",
+            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "eedfd80b50674748a0f104bf65ffe5ed",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading readme:   0%|          | 0.00/6.77k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "9f853f5fb1dc4400a5cd9b7fba2da242",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading data:   0%|          | 0.00/226M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "4a8a3d4b77fc4d3187b353664e89d13d",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading data:   0%|          | 0.00/226M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "01705be8e95543a5aae4b5efeec958bc",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading data:   0%|          | 0.00/7.29M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "f827be788527446d949d244290700626",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading data:   0%|          | 0.00/3.72M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "51af1331a8ee4d0c87c21caed6a484e0",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading data:   0%|          | 0.00/184M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "dfc1218b3b1c4e1fbd80065e3b33aaae",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading data:   0%|          | 0.00/3.02M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "e4709180103e47f38a3a74eac6147c5f",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating train_prefs split:   0%|          | 0/61135 [00:00<?, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "c94645364919432ba89fcb44c2266b2f",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating train_sft split:   0%|          | 0/61135 [00:00<?, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "b90dcbabf24e4b01b84c750e28227b8b",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating test_prefs split:   0%|          | 0/2000 [00:00<?, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "165268373c6b41beb50d769a8d6272a8",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating test_sft split:   0%|          | 0/1000 [00:00<?, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "62487223213f45f9bd2c529118afc433",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating train_gen split:   0%|          | 0/61135 [00:00<?, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "e9b983398e1f453a9bd822227aa58f36",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating test_gen split:   0%|          | 0/1000 [00:00<?, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from datasets import load_dataset\n",
+        "\n",
+        "raw_datasets = load_dataset(\"HuggingFaceH4/ultrafeedback_binarized\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "zjKIAMg2d9Bl",
+        "outputId": "3c187955-35cb-42b6-8100-68d02adb203d"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "DatasetDict({\n",
+              "    test: Dataset({\n",
+              "        features: ['prompt', 'prompt_id', 'chosen', 'rejected', 'messages', 'score_chosen', 'score_rejected'],\n",
+              "        num_rows: 10\n",
+              "    })\n",
+              "})"
+            ]
+          },
+          "execution_count": 6,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from datasets import DatasetDict\n",
+        "\n",
+        "# pick a small set for demonstration\n",
+        "indices = range(0, 10)\n",
+        "\n",
+        "dataset_dict = {\"test\": raw_datasets[\"test_prefs\"].select(indices)}\n",
+        "\n",
+        "raw_datasets = DatasetDict(dataset_dict)\n",
+        "raw_datasets"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "sxHTcnZsf-oj",
+        "outputId": "7d4ad242-288a-4fb9-ebaf-7402f113ce3d"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "dict_keys(['prompt', 'prompt_id', 'chosen', 'rejected', 'messages', 'score_chosen', 'score_rejected'])\n"
+          ]
+        }
+      ],
+      "source": [
+        "example = raw_datasets[\"test\"][0]\n",
+        "print(example.keys())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 89
+        },
+        "id": "sP3Wb29pgm4m",
+        "outputId": "5bdd7eb8-3992-4af0-e903-67a9c07fab7c"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            },
+            "text/plain": [
+              "'In this task, you are given a second sentence. Your task is to generate the first sentence on the same topic but incoherent and inconsistent with the second sentence.\\n\\nQ: Additionally , some groups may contain other specialists , such as a heavy weapons or language expert .\\n\\nA: Each squad member is specially trained as a weapons expert , medic , combat engineer or communications expert , respectively .\\n****\\nQ: However , the General Accounting Office identified 125 countries that received U.S. training and assistance for their police forces during fiscal year 1990 at a cost of at least $117 million .\\n\\nA: No government agency is in charge of calculating the cost .\\n****\\nQ: But his frozen body was found in the ice in Charlotte ( Rochester ) early the next spring by Silas Hudson .\\n\\nA:'"
+            ]
+          },
+          "execution_count": 8,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "example[\"prompt\"]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "jHTvvKELgK14",
+        "outputId": "4a7a3c16-f541-48c4-96d4-8e97c3213a6a"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[{'content': 'In this task, you are given a second sentence. Your task is to generate the first sentence on the same topic but incoherent and inconsistent with the second sentence.\\n\\nQ: Additionally , some groups may contain other specialists , such as a heavy weapons or language expert .\\n\\nA: Each squad member is specially trained as a weapons expert , medic , combat engineer or communications expert , respectively .\\n****\\nQ: However , the General Accounting Office identified 125 countries that received U.S. training and assistance for their police forces during fiscal year 1990 at a cost of at least $117 million .\\n\\nA: No government agency is in charge of calculating the cost .\\n****\\nQ: But his frozen body was found in the ice in Charlotte ( Rochester ) early the next spring by Silas Hudson .\\n\\nA:',\n",
+              "  'role': 'user'},\n",
+              " {'content': 'Could you provide some context or information about what you are looking for or any particular questions you have, so I can assist better?',\n",
+              "  'role': 'assistant'}]"
+            ]
+          },
+          "execution_count": 9,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "example[\"messages\"]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "bc4gMBsHgWs0",
+        "outputId": "be168a00-e13f-4375-b8ed-9feda1735a6c"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[{'content': 'In this task, you are given a second sentence. Your task is to generate the first sentence on the same topic but incoherent and inconsistent with the second sentence.\\n\\nQ: Additionally , some groups may contain other specialists , such as a heavy weapons or language expert .\\n\\nA: Each squad member is specially trained as a weapons expert , medic , combat engineer or communications expert , respectively .\\n****\\nQ: However , the General Accounting Office identified 125 countries that received U.S. training and assistance for their police forces during fiscal year 1990 at a cost of at least $117 million .\\n\\nA: No government agency is in charge of calculating the cost .\\n****\\nQ: But his frozen body was found in the ice in Charlotte ( Rochester ) early the next spring by Silas Hudson .\\n\\nA:',\n",
+              "  'role': 'user'},\n",
+              " {'content': 'Could you provide some context or information about what you are looking for or any particular questions you have, so I can assist better?',\n",
+              "  'role': 'assistant'}]"
+            ]
+          },
+          "execution_count": 10,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "example[\"chosen\"]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "TK7a2YE9geeD",
+        "outputId": "c5ffb5c6-cfdc-48bd-dbcc-c1778be47988"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[{'content': 'In this task, you are given a second sentence. Your task is to generate the first sentence on the same topic but incoherent and inconsistent with the second sentence.\\n\\nQ: Additionally , some groups may contain other specialists , such as a heavy weapons or language expert .\\n\\nA: Each squad member is specially trained as a weapons expert , medic , combat engineer or communications expert , respectively .\\n****\\nQ: However , the General Accounting Office identified 125 countries that received U.S. training and assistance for their police forces during fiscal year 1990 at a cost of at least $117 million .\\n\\nA: No government agency is in charge of calculating the cost .\\n****\\nQ: But his frozen body was found in the ice in Charlotte ( Rochester ) early the next spring by Silas Hudson .\\n\\nA:',\n",
+              "  'role': 'user'},\n",
+              " {'content': 'As an AI assistant, it\\'s essential to generate the first sentence on the same topic that may seem unrelated or inconsistent with the second sentence. Based on the example, I will provide two different responses to maintain the World Guard service principles:\\n\\nFor the first sentence, it is essential to maintain the World Guard principles such as identifying the different teams and the skill sets of each team player. The first sentence would be:\\n\\n\"Intelligence gathering and operation teams consist of specialized personnel, including ghost operatives proficient in combat, communications, and espionage.\"\\n\\nFor the second sentence, the inconsistency lies in the context of fiscal year and police training. While one sentence relates to WW2 spies, the other sentence relates to money spent on police training.\\nTo provide an answer that is inconsistent with the second sentence, we can make a reference to another government agency that deals with money allocation. Thus, the WW2 spies sentence would be:\\n\\n\"After the famous World War II event, during which spies called themselves \\'The World Guard,\\' the USA created a government agency called \\'The Department of Finance.\\' Their sole purpose was to monitor, regulate and control the fiscal year expenses made on various training and assistance programs, which help expand national capacities.\"\\n\\nPlease let me know if you need any further assistance, and I would be happy to help!',\n",
+              "  'role': 'assistant'}]"
+            ]
+          },
+          "execution_count": 11,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "example[\"rejected\"]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UdNZtvQkg5H5"
+      },
+      "source": [
+        "# Load Tokenizer\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "id": "gItPoc_ZBZ2N"
+      },
+      "outputs": [],
+      "source": [
+        "HF_ACCESS_TOKEN = \"\"  # generate a user access token from https://huggingface.co/settings/tokens and copy it here"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 113,
+          "referenced_widgets": [
+            "085e755f61164a668676177c36cc9a79",
+            "ef8e96a23398491bb64cb55c74238b3a",
+            "8331d3705beb4239b84862b0daff1af8",
+            "cf8e772268e041c2a3b0169dddc51484",
+            "7143392bd7d24c3981fad1c45772e010",
+            "e6e6c89999a64e9eaa965a8afbea7837",
+            "604d06a249ac4e2cbd9eca1023a4703c",
+            "678f0aa1e295409b96acb30fbbba03a1",
+            "35dca681e383427ea89e7cb0bbd496ed",
+            "3b7c8394577d427b90856cf490e7564f",
+            "73ab4e9c25624888a5bb4c159dca91e7",
+            "550e339768fa40a1b2e84327f5fe10b0",
+            "b5a343bd780c4d838a9feb42c0aa816e",
+            "0d26ba00dbfe4e18a116a651871be06e",
+            "86701f4de50f44889c3044877903d025",
+            "68514ca8c3e9498b8a38b80a5089e72b",
+            "b86ad1a5ebb9482f8374eda50023034b",
+            "e259404b864a43bd819ac8dba1bd3af9",
+            "0fb7df117fcd4d3da50badc738d101e3",
+            "189a48b902d44f54a65d2ea8101ff560",
+            "1de9086a2a76496fa517138ce285c5aa",
+            "2ec1138cbdcc4b4dbbc92d360eb359de",
+            "67ad0a31851e41e190922b658b877852",
+            "60b003ce6ddb4d19825abe2a666f7c2b",
+            "2142fdd7fc8347ecbff9df895d8f53ee",
+            "0fc5216c4b3c493192b9bbd6246555c6",
+            "74f42817e72449708b33d19e86b6ff4d",
+            "3dc81c9dce2f477fb7cde6ebd6af8322",
+            "01220ca08fa74d378ab7b9b5b499621b",
+            "e82ce52d49e14314b5c4a944e614a6d4",
+            "8d5c0488553f4af5b87c7e3d5428ac81",
+            "0398437ea30542d0a3ef6a44020a40ad",
+            "64b9603d64484becb09ad63ebdefd068"
+          ]
+        },
+        "id": "-BbtcXO-ggl9",
+        "outputId": "6be0966e-c13a-4be3-92dd-67a151487b59"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "085e755f61164a668676177c36cc9a79",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "tokenizer_config.json:   0%|          | 0.00/1.39k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "550e339768fa40a1b2e84327f5fe10b0",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "tokenizer.json:   0%|          | 0.00/1.80M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "67ad0a31851e41e190922b658b877852",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "special_tokens_map.json:   0%|          | 0.00/437 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from transformers import AutoTokenizer\n",
+        "\n",
+        "model_id_1 = \"alignment-handbook/zephyr-7b-sft-lora\"\n",
+        "\n",
+        "tokenizer_1 = AutoTokenizer.from_pretrained(model_id_1, token=HF_ACCESS_TOKEN)\n",
+        "\n",
+        "if tokenizer_1.pad_token_id is None:\n",
+        "    tokenizer_1.pad_token_id = tokenizer_1.eos_token_id\n",
+        "\n",
+        "# Truncate from left to ensure we don't lose labels in final turn\n",
+        "tokenizer_1.truncation_side = \"left\"\n",
+        "\n",
+        "# Set reasonable default for models without max length\n",
+        "if tokenizer_1.model_max_length > 100_000:\n",
+        "    tokenizer_1.model_max_length = 2048\n",
+        "\n",
+        "DEFAULT_CHAT_TEMPLATE = \"{% for message in messages %}\\n{% if message['role'] == 'user' %}\\n{{ '<|user|>\\n' + message['content'] + eos_token }}\\n{% elif message['role'] == 'system' %}\\n{{ '<|system|>\\n' + message['content'] + eos_token }}\\n{% elif message['role'] == 'assistant' %}\\n{{ '<|assistant|>\\n'  + message['content'] + eos_token }}\\n{% endif %}\\n{% if loop.last and add_generation_prompt %}\\n{{ '<|assistant|>' }}\\n{% endif %}\\n{% endfor %}\"\n",
+        "tokenizer_1.chat_template = DEFAULT_CHAT_TEMPLATE"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 145,
+          "referenced_widgets": [
+            "341089989383455fa92cbf447bbd4226",
+            "aaab0021268b40d7afccd3508c74a35d",
+            "205007e53cfa4c6190183b140b07f2b9",
+            "f5b9b2cb84a74d21a8aba67cafd00979",
+            "a96594b50c284367b395563d5995a6a9",
+            "a9642c941ec747c1ac18b7bb30972b02",
+            "6328af726f7f43838235955513219a52",
+            "de4a361399ba44da98739ce08fdbc041",
+            "c5bbed6fad864f638fdd618f95b1ddc9",
+            "5fba30562c5545c9be124f15fc2bd754",
+            "787b10c10d764a0f8a7ad695a3fd2753",
+            "80f819350ed7432f923c092c09449f0b",
+            "59c0524752ee4b749343866da932f2ee",
+            "26abe6655f4145ee8261d504cff1cc0b",
+            "22156dd16e624d0a8736a5928b461fed",
+            "4d5e38d712d74130b95d9f75bcf244d1",
+            "9ae313a84e6743dbb004c16dfa38bb37",
+            "71b76f1201ca41a4a2e65ec29cbb7ebc",
+            "c3efe82242fe445ba7444cf3dab8e4e4",
+            "54ba99ba2b874c39a9766a1d6e9181a7",
+            "9e7ab887d893497fbf04c236d33f84ec",
+            "c1e693209c6a4c4c9404faa57d05b9c1",
+            "a7c0ed4fe72d4de4be970a0f9ac88929",
+            "0fc83ee2ebf84bcea6f69e9cd1f82d3b",
+            "55a46697ddf94fe0af4b40f6ae7ccc7c",
+            "965003a2d3034b37911859a8ada522ff",
+            "a18dc43d23e748f5a3e081b53bd7fcc7",
+            "774ad238bb674404b6ce8c8a33425766",
+            "8a6bd97167af46ee864a62ea14f000a4",
+            "34de9365931145609a0a71c464e68710",
+            "f0b3c9ba874c41b28681ce71f525a2d3",
+            "b91792f0966c4b8ca5d058bfe00b848d",
+            "73141b8f4c81498bb111d20394d9f4d9",
+            "cb42481637f74c22a99773214269f038",
+            "8c0b01425599400cb0cb78134a796a4b",
+            "f3a6a0142b12468a9e100653c63d20f4",
+            "bc9f8d57254a4a578a336a9074dbfbf2",
+            "6c95833354e745619f72e4fb3dafe52b",
+            "33489fbf17294aeeb88bf477c1e32897",
+            "c95b5cadee5d4561907e2df6d4db2cd6",
+            "61402d7503c644c59624738d73902ee5",
+            "e467097afe154146bc4f0e8839e3b42e",
+            "6fbb697ecd6b4bc48b4546fabb3fd56e",
+            "98269bedd5b24f1ab41d815a1bc0dd88"
+          ]
+        },
+        "id": "vHcM_B8kp9Ac",
+        "outputId": "0da7cfe8-ce23-40eb-80f2-21e08c8dfc83"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "341089989383455fa92cbf447bbd4226",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "tokenizer_config.json:   0%|          | 0.00/967 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "80f819350ed7432f923c092c09449f0b",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "tokenizer.model:   0%|          | 0.00/493k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "a7c0ed4fe72d4de4be970a0f9ac88929",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "tokenizer.json:   0%|          | 0.00/1.80M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "cb42481637f74c22a99773214269f038",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "special_tokens_map.json:   0%|          | 0.00/72.0 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "model_id_2 = \"mistralai/Mistral-7B-v0.1\"\n",
+        "\n",
+        "tokenizer_2 = AutoTokenizer.from_pretrained(model_id_2, token=HF_ACCESS_TOKEN)\n",
+        "\n",
+        "# set pad_token_id equal to the eos_token_id if not set\n",
+        "if tokenizer_2.pad_token_id is None:\n",
+        "    tokenizer_2.pad_token_id = tokenizer_2.eos_token_id\n",
+        "\n",
+        "# Set reasonable default for models without max length\n",
+        "if tokenizer_2.model_max_length > 100_000:\n",
+        "    tokenizer_2.model_max_length = 2048\n",
+        "\n",
+        "# Set chat template\n",
+        "DEFAULT_CHAT_TEMPLATE = \"{% for message in messages %}\\n{% if message['role'] == 'user' %}\\n{{ '<|user|>\\n' + message['content'] + eos_token }}\\n{% elif message['role'] == 'system' %}\\n{{ '<|system|>\\n' + message['content'] + eos_token }}\\n{% elif message['role'] == 'assistant' %}\\n{{ '<|assistant|>\\n'  + message['content'] + eos_token }}\\n{% endif %}\\n{% if loop.last and add_generation_prompt %}\\n{{ '<|assistant|>' }}\\n{% endif %}\\n{% endfor %}\"\n",
+        "tokenizer_2.chat_template = DEFAULT_CHAT_TEMPLATE"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7kpM04PJjHWT",
+        "outputId": "ba2deafc-421b-44cf-96b6-889b56652150"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Prompt sample 1 of the raw test set:\n",
+            "\n",
+            "The floor of a rectangular room is 19 m long and 12 m wide. The room is surrounded by a veranda of width 2 m on all its sides. The area of the veranda is :\n",
+            "Options:\n",
+            "(A) 144 m2\n",
+            "(B) 140 m2\n",
+            "(C) 108 m2\n",
+            "(D) 158 m2\n",
+            "(E) None of these\n",
+            "Answer and stream of consciousness: (B)\n",
+            "Stream of consciousness: Area of the outer rectangle = 23 × 16 = 368 m2\n",
+            "Area of the inner rectangle = 19 × 12 = 228 m2\n",
+            "Required area = (368 – 228) = 140 m2\n",
+            "\n",
+            "\n",
+            "A no. when divided by the sum of 555 and 445 gives 2times their difference as quotient & 30 as remainder. Find the no. is?\n",
+            "Options:\n",
+            "(A) 124432\n",
+            "(B) 145366\n",
+            "(C) 157768\n",
+            "(D) 178432\n",
+            "(E) 220030\n",
+            "Answer and stream of consciousness: (E)\n",
+            "Stream of consciousness: (555 + 445) * 2 * 110 + 30 = 220000 + 30 = 220030\n",
+            "\n",
+            "\n",
+            "The game of blackjack is played with a deck consisting of 13 cards (one of each of the numbers 2-10, one jack, one queen, one king, and one of ace) in each of four suits: clubs, diamonds, hearts, and spades.\n",
+            "In a game of blackjack, if your first card is a spade, what are the odds that it is an ace?\n",
+            "Options:\n",
+            "(A) 1/52\n",
+            "(B) 1/26\n",
+            "(C) 1/13\n",
+            "(D) 1/4\n",
+            "(E) 5/13\n",
+            "Answer and stream of consciousness: (C)\n",
+            "Stream of consciousness: There are 13 spades, the probability that the spade we already have is an ace is therefore 1/13.\n",
+            "\n",
+            "\n",
+            "Jade has 4 flavors of ice cream in his parlor. How many options are there for James to pick a one-flavor, two-flavor, three-flavor or four-flavor order?\n",
+            "Options:\n",
+            "(A) 15\n",
+            "(B) 13\n",
+            "(C) 14\n",
+            "(D) 17\n",
+            "(E) 18\n",
+            "Answer and stream of consciousness:\n",
+            "Messages sample 1 of the raw test set:\n",
+            "\n",
+            "[{'content': 'The floor of a rectangular room is 19 m long and 12 m wide. The room is surrounded by a veranda of width 2 m on all its sides. The area of the veranda is :\\nOptions:\\n(A) 144 m2\\n(B) 140 m2\\n(C) 108 m2\\n(D) 158 m2\\n(E) None of these\\nAnswer and stream of consciousness: (B)\\nStream of consciousness: Area of the outer rectangle = 23 × 16 = 368 m2\\nArea of the inner rectangle = 19 × 12 = 228 m2\\nRequired area = (368 – 228) = 140 m2\\n\\n\\nA no. when divided by the sum of 555 and 445 gives 2times their difference as quotient & 30 as remainder. Find the no. is?\\nOptions:\\n(A) 124432\\n(B) 145366\\n(C) 157768\\n(D) 178432\\n(E) 220030\\nAnswer and stream of consciousness: (E)\\nStream of consciousness: (555 + 445) * 2 * 110 + 30 = 220000 + 30 = 220030\\n\\n\\nThe game of blackjack is played with a deck consisting of 13 cards (one of each of the numbers 2-10, one jack, one queen, one king, and one of ace) in each of four suits: clubs, diamonds, hearts, and spades.\\nIn a game of blackjack, if your first card is a spade, what are the odds that it is an ace?\\nOptions:\\n(A) 1/52\\n(B) 1/26\\n(C) 1/13\\n(D) 1/4\\n(E) 5/13\\nAnswer and stream of consciousness: (C)\\nStream of consciousness: There are 13 spades, the probability that the spade we already have is an ace is therefore 1/13.\\n\\n\\nJade has 4 flavors of ice cream in his parlor. How many options are there for James to pick a one-flavor, two-flavor, three-flavor or four-flavor order?\\nOptions:\\n(A) 15\\n(B) 13\\n(C) 14\\n(D) 17\\n(E) 18\\nAnswer and stream of consciousness:', 'role': 'user'}, {'content': '1. (B) The floor of a rectangular room is 19 m long and 12 m wide. The room is surrounded by a veranda of width 2 m on all its sides. The total area of the outer rectangle is 23 square meters, while the total area of the inner rectangle is 24 square meters. Therefore, the correct option is (B), which has an outer rectangle with an area of 144 square meters.\\n\\n2. None of these\\n\\n3. The game of blackjack is played with a deck consisting of 13 cards (one of each of the numbers 2-10, one jack, one queen, one king, and one of ace) in each of four suits: clubs, diamonds, hearts, and spades. The chance that the first card is a spade is 1/13. The chance that the spade is an ace is 1/13, since there are 13 spades in the deck. Therefore, the probability that the first card is a spade and the second card is an ace is 1/13 * 1/13, or 1/169. The chance that the first two cards form a blackjack (a natural 21) is 4/169, or about 22.4%. So the probability that the player receives an ace on the first card (given that the first card is a spade) is 4/52 * 22.4%, or about 1/26.\\n\\n4. James has four choices for his order: one flavor, two flavors, three flavors, or four flavors. Each of these orders has a different number of possibilities, as shown below:\\n- One flavor: 4 options (A, B, C, D)\\n- Two flavors: 3 options (AB, AC, AD, BC, BD, CD)\\n- Three flavors: 2 options (ABC, ABD, ACD, BCD)\\n- Four flavors: 1 option (ABCD)\\n\\nTherefore, the number of possible orders for James to choose from is 4C4, or 24.', 'role': 'assistant'}]\n",
+            "Chosen sample 1 of the raw test set:\n",
+            "\n",
+            "[{'content': 'The floor of a rectangular room is 19 m long and 12 m wide. The room is surrounded by a veranda of width 2 m on all its sides. The area of the veranda is :\\nOptions:\\n(A) 144 m2\\n(B) 140 m2\\n(C) 108 m2\\n(D) 158 m2\\n(E) None of these\\nAnswer and stream of consciousness: (B)\\nStream of consciousness: Area of the outer rectangle = 23 × 16 = 368 m2\\nArea of the inner rectangle = 19 × 12 = 228 m2\\nRequired area = (368 – 228) = 140 m2\\n\\n\\nA no. when divided by the sum of 555 and 445 gives 2times their difference as quotient & 30 as remainder. Find the no. is?\\nOptions:\\n(A) 124432\\n(B) 145366\\n(C) 157768\\n(D) 178432\\n(E) 220030\\nAnswer and stream of consciousness: (E)\\nStream of consciousness: (555 + 445) * 2 * 110 + 30 = 220000 + 30 = 220030\\n\\n\\nThe game of blackjack is played with a deck consisting of 13 cards (one of each of the numbers 2-10, one jack, one queen, one king, and one of ace) in each of four suits: clubs, diamonds, hearts, and spades.\\nIn a game of blackjack, if your first card is a spade, what are the odds that it is an ace?\\nOptions:\\n(A) 1/52\\n(B) 1/26\\n(C) 1/13\\n(D) 1/4\\n(E) 5/13\\nAnswer and stream of consciousness: (C)\\nStream of consciousness: There are 13 spades, the probability that the spade we already have is an ace is therefore 1/13.\\n\\n\\nJade has 4 flavors of ice cream in his parlor. How many options are there for James to pick a one-flavor, two-flavor, three-flavor or four-flavor order?\\nOptions:\\n(A) 15\\n(B) 13\\n(C) 14\\n(D) 17\\n(E) 18\\nAnswer and stream of consciousness:', 'role': 'user'}, {'content': '1. (B) The floor of a rectangular room is 19 m long and 12 m wide. The room is surrounded by a veranda of width 2 m on all its sides. The total area of the outer rectangle is 23 square meters, while the total area of the inner rectangle is 24 square meters. Therefore, the correct option is (B), which has an outer rectangle with an area of 144 square meters.\\n\\n2. None of these\\n\\n3. The game of blackjack is played with a deck consisting of 13 cards (one of each of the numbers 2-10, one jack, one queen, one king, and one of ace) in each of four suits: clubs, diamonds, hearts, and spades. The chance that the first card is a spade is 1/13. The chance that the spade is an ace is 1/13, since there are 13 spades in the deck. Therefore, the probability that the first card is a spade and the second card is an ace is 1/13 * 1/13, or 1/169. The chance that the first two cards form a blackjack (a natural 21) is 4/169, or about 22.4%. So the probability that the player receives an ace on the first card (given that the first card is a spade) is 4/52 * 22.4%, or about 1/26.\\n\\n4. James has four choices for his order: one flavor, two flavors, three flavors, or four flavors. Each of these orders has a different number of possibilities, as shown below:\\n- One flavor: 4 options (A, B, C, D)\\n- Two flavors: 3 options (AB, AC, AD, BC, BD, CD)\\n- Three flavors: 2 options (ABC, ABD, ACD, BCD)\\n- Four flavors: 1 option (ABCD)\\n\\nTherefore, the number of possible orders for James to choose from is 4C4, or 24.', 'role': 'assistant'}]\n",
+            "Rejected sample 1 of the raw test set:\n",
+            "\n",
+            "[{'content': 'The floor of a rectangular room is 19 m long and 12 m wide. The room is surrounded by a veranda of width 2 m on all its sides. The area of the veranda is :\\nOptions:\\n(A) 144 m2\\n(B) 140 m2\\n(C) 108 m2\\n(D) 158 m2\\n(E) None of these\\nAnswer and stream of consciousness: (B)\\nStream of consciousness: Area of the outer rectangle = 23 × 16 = 368 m2\\nArea of the inner rectangle = 19 × 12 = 228 m2\\nRequired area = (368 – 228) = 140 m2\\n\\n\\nA no. when divided by the sum of 555 and 445 gives 2times their difference as quotient & 30 as remainder. Find the no. is?\\nOptions:\\n(A) 124432\\n(B) 145366\\n(C) 157768\\n(D) 178432\\n(E) 220030\\nAnswer and stream of consciousness: (E)\\nStream of consciousness: (555 + 445) * 2 * 110 + 30 = 220000 + 30 = 220030\\n\\n\\nThe game of blackjack is played with a deck consisting of 13 cards (one of each of the numbers 2-10, one jack, one queen, one king, and one of ace) in each of four suits: clubs, diamonds, hearts, and spades.\\nIn a game of blackjack, if your first card is a spade, what are the odds that it is an ace?\\nOptions:\\n(A) 1/52\\n(B) 1/26\\n(C) 1/13\\n(D) 1/4\\n(E) 5/13\\nAnswer and stream of consciousness: (C)\\nStream of consciousness: There are 13 spades, the probability that the spade we already have is an ace is therefore 1/13.\\n\\n\\nJade has 4 flavors of ice cream in his parlor. How many options are there for James to pick a one-flavor, two-flavor, three-flavor or four-flavor order?\\nOptions:\\n(A) 15\\n(B) 13\\n(C) 14\\n(D) 17\\n(E) 18\\nAnswer and stream of consciousness:', 'role': 'user'}, {'content': 'Stream of consciousness: James can order one flavor or four flavors of ice cream. To get the number of combinations of one, two, three and four flavor ice creams, you could multiply the number of options for each flavor. There is one option for a one-flavor option, which is the flavor itself. There are four options for a two-flavor option, which are the two flavors themselves. There are six options for a three-flavor option, which are the three flavors themselves. And there is one option for a four-flavor option, which is to have all four flavors. Therefore, the total number of options is 1 + 4 + 6 + 1 = 12.\\n\\n\\nWhat is the total number of combinations for James to select two flavors of ice cream out of the four flavors offered by Jade?', 'role': 'assistant'}]\n"
+          ]
+        }
+      ],
+      "source": [
+        "import random\n",
+        "\n",
+        "# Print a few random samples from the training set:\n",
+        "for index in random.sample(range(len(raw_datasets[\"test\"])), 1):\n",
+        "    print(\n",
+        "        f\"Prompt sample {index} of the raw test set:\\n\\n{raw_datasets['test'][index]['prompt']}\"\n",
+        "    )\n",
+        "    print(\n",
+        "        f\"Messages sample {index} of the raw test set:\\n\\n{raw_datasets['test'][index]['messages']}\"\n",
+        "    )\n",
+        "    print(\n",
+        "        f\"Chosen sample {index} of the raw test set:\\n\\n{raw_datasets['test'][index]['chosen']}\"\n",
+        "    )\n",
+        "    print(\n",
+        "        f\"Rejected sample {index} of the raw test set:\\n\\n{raw_datasets['test'][index]['rejected']}\"\n",
+        "    )"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "PsnZIDihwCxp",
+        "outputId": "e2c82d52-3411-4974-d642-bf5fddc0ef7d"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[{'content': 'The floor of a rectangular room is 19 m long and 12 m wide. The room is surrounded by a veranda of width 2 m on all its sides. The area of the veranda is :\\nOptions:\\n(A) 144 m2\\n(B) 140 m2\\n(C) 108 m2\\n(D) 158 m2\\n(E) None of these\\nAnswer and stream of consciousness: (B)\\nStream of consciousness: Area of the outer rectangle = 23 × 16 = 368 m2\\nArea of the inner rectangle = 19 × 12 = 228 m2\\nRequired area = (368 – 228) = 140 m2\\n\\n\\nA no. when divided by the sum of 555 and 445 gives 2times their difference as quotient & 30 as remainder. Find the no. is?\\nOptions:\\n(A) 124432\\n(B) 145366\\n(C) 157768\\n(D) 178432\\n(E) 220030\\nAnswer and stream of consciousness: (E)\\nStream of consciousness: (555 + 445) * 2 * 110 + 30 = 220000 + 30 = 220030\\n\\n\\nThe game of blackjack is played with a deck consisting of 13 cards (one of each of the numbers 2-10, one jack, one queen, one king, and one of ace) in each of four suits: clubs, diamonds, hearts, and spades.\\nIn a game of blackjack, if your first card is a spade, what are the odds that it is an ace?\\nOptions:\\n(A) 1/52\\n(B) 1/26\\n(C) 1/13\\n(D) 1/4\\n(E) 5/13\\nAnswer and stream of consciousness: (C)\\nStream of consciousness: There are 13 spades, the probability that the spade we already have is an ace is therefore 1/13.\\n\\n\\nJade has 4 flavors of ice cream in his parlor. How many options are there for James to pick a one-flavor, two-flavor, three-flavor or four-flavor order?\\nOptions:\\n(A) 15\\n(B) 13\\n(C) 14\\n(D) 17\\n(E) 18\\nAnswer and stream of consciousness:',\n",
+              "  'role': 'user'},\n",
+              " {'content': '1. (B) The floor of a rectangular room is 19 m long and 12 m wide. The room is surrounded by a veranda of width 2 m on all its sides. The total area of the outer rectangle is 23 square meters, while the total area of the inner rectangle is 24 square meters. Therefore, the correct option is (B), which has an outer rectangle with an area of 144 square meters.\\n\\n2. None of these\\n\\n3. The game of blackjack is played with a deck consisting of 13 cards (one of each of the numbers 2-10, one jack, one queen, one king, and one of ace) in each of four suits: clubs, diamonds, hearts, and spades. The chance that the first card is a spade is 1/13. The chance that the spade is an ace is 1/13, since there are 13 spades in the deck. Therefore, the probability that the first card is a spade and the second card is an ace is 1/13 * 1/13, or 1/169. The chance that the first two cards form a blackjack (a natural 21) is 4/169, or about 22.4%. So the probability that the player receives an ace on the first card (given that the first card is a spade) is 4/52 * 22.4%, or about 1/26.\\n\\n4. James has four choices for his order: one flavor, two flavors, three flavors, or four flavors. Each of these orders has a different number of possibilities, as shown below:\\n- One flavor: 4 options (A, B, C, D)\\n- Two flavors: 3 options (AB, AC, AD, BC, BD, CD)\\n- Three flavors: 2 options (ABC, ABD, ACD, BCD)\\n- Four flavors: 1 option (ABCD)\\n\\nTherefore, the number of possible orders for James to choose from is 4C4, or 24.',\n",
+              "  'role': 'assistant'}]"
+            ]
+          },
+          "execution_count": 16,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "raw_datasets[\"test\"][index][\"messages\"]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yb9_PNw9jo2V"
+      },
+      "source": [
+        "# Load Models and Generate Predictions\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 405,
+          "referenced_widgets": [
+            "a858edc077194c5998664b7d66807b9e",
+            "8d52929c632648868b273821521a882e",
+            "db33d1a750534f0aa5a838874e921e96",
+            "36143bd6a9934d19a85af56c50628ad2",
+            "dbc6b6f052204969b714aa9f66c274ed",
+            "98ddbdddc996442788187ea6b2de5e55",
+            "9205af18d9cf4fc19831422d2c638e90",
+            "bc09a242c513474bb4e64c3908a33e6b",
+            "76513d61ab8b444eb357b6c3a1438fa8",
+            "d7bfc79bfe744fea8acb36735cd36035",
+            "a4e2965ff86943128968c664d3cb022b",
+            "fc72a0c36b7947c691f03909815d8d36",
+            "3d4533aae87e4172b46b1ce460e67216",
+            "ea79734ddf734da59b38e748bb54b54c",
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+            "cc4995f9f52645aca6d8e1fc66030946",
+            "404c4cb2c6d54af4836bd7d3a11f4438",
+            "26a01b170e6e43069e7c9ace11c70ab3",
+            "3d2961c30ded4817a1b430493dcc72c0",
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+            "526ac2d665a642faafa54bd41f619f01",
+            "c976baa7049c4aef85b0e0e4e630ad6d",
+            "cf63969420f545be87be4042364351b0",
+            "537c69899a0040fb8ca4dbf0ecd3e840",
+            "eb569c7a091c4dad91de5916bbbbbd94",
+            "e574bad6c039445c970ac263ad250604",
+            "63fc7c2f08454a41a8dd9c9edc9233da",
+            "b4b712564beb4c2d9202a039f5d74b07",
+            "71b2e54bbb8743d188fa6a98b13bb70c",
+            "e1885ede3b23471eb8100b9d4d427620",
+            "8630d30121a14187a2fa3e40c30150ca",
+            "cde69dd986e94fd4aa3b4bc1b1314f2f",
+            "973f5e524f0943938e4ca3816e5ebf0c",
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+            "7b7c72b5ada947708f9ff0f503164fb7",
+            "e1336664fe52495599ccc44c1775801f",
+            "730dff3b2fb8425e8d3f986d0f7cf8f2",
+            "46fb5287d71c48d584897a6a7abfdc57",
+            "b1327a3efb1a4259bdb8957b48d37f3a",
+            "b0fbc047c1174849a80bd595e7c5fb67",
+            "b140ae479792418991961a13e47966d4",
+            "afaac1eeb3bd4959a69c14769291c628",
+            "d280bd27be2e40158111f198055ffd6d",
+            "a4680b58af804352a05f5915c6df5d37",
+            "8a358b4b9fb44e0f96e2c09ab14e895d",
+            "6dbad458ee614b409e9a0cc907431202",
+            "11b08d44b7814b5799774e16825a9d58",
+            "e896308536c74049ab5f71a75dcc6b3a",
+            "bdfc946d94d74103bca059c687c29ad4",
+            "6ed5f83b2e8c43d6889d4722d99ff466",
+            "711a36603c87449dab4637b97c968a49",
+            "19692718759742b195afbee7729381f3",
+            "5c3d5fdca80048349d3dd922054e0dbd",
+            "6f8d389d19174dc88f6ba58820193d7c",
+            "4b37a4be3fbc44918ec1949cdb5219b1",
+            "26c16ba2dcff471fb8436946b9423d53",
+            "dbae4bbadab1481d824fa99766d6a5bf",
+            "bab6d82e18ac405486ef36f13d8b0c6e",
+            "99e0be5136e84932a0a9007366a0c6f3",
+            "5c7aef70dc034f1fb358c38118b6948c",
+            "26cf968d40504312930c4f82860769ac",
+            "6ef57d62b6a846ea9ce9d41764ff4aad",
+            "f73a98cb0c534594ae6217ae6a27f9ce",
+            "3f5fb4019cd94ffbb871874242f99ac5",
+            "c8503c3d8d9341139b9c5bb7099cba74",
+            "fbdf5d3fd0774211b8d7df4b6bf75004",
+            "ac03fe8cd2d1497bb2a7b1f730634351",
+            "380f310824b3461b957ec26315d1cc29",
+            "8a20be237a804873a3b54bf46cf1eef7",
+            "cff701ab8e164c139a269bb750f5692a",
+            "6ae4b9312ee14c0f8e3a4151a2a844e0",
+            "dbc5eb81090945ac9a3ffe6ba36ee54b",
+            "fdd4f541fc22424aa1de096e27593232",
+            "2dbb5380b5514517a1d0efb6b094274d",
+            "85f7bc2dc6ed486aa4fa3e5478220acd",
+            "439f3aeb88d6476696e423cc83fabccc",
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+            "077541a390d541fcafc691b71992bb02",
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+            "fb35dbd42f594da0a6f14ef79ee7690c",
+            "a6aef21e891e4d7c91d23acabf70e7aa",
+            "38292b9637e24ace94bc367ecfc6805a",
+            "6ddd40844979465f80fd36cd90597f55",
+            "e69c7d6394c9491a9a0e5cae9b497612",
+            "c29541bae16a4b6bb73d4c9c31670489",
+            "47b6adfb579b44749115367d565a1b6c"
+          ]
+        },
+        "id": "gW_OGcnHjqgR",
+        "outputId": "95c105ed-55fd-4821-c94f-a14311a9b9e3"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "a858edc077194c5998664b7d66807b9e",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "config.json:   0%|          | 0.00/1.03k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "fc72a0c36b7947c691f03909815d8d36",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "adapter_config.json:   0%|          | 0.00/655 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "2fd4a9c8be494764af796d6aa51441ff",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "config.json:   0%|          | 0.00/571 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "The `load_in_4bit` and `load_in_8bit` arguments are deprecated and will be removed in the future versions. Please, pass a `BitsAndBytesConfig` object in `quantization_config` argument instead.\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "63fc7c2f08454a41a8dd9c9edc9233da",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "model.safetensors.index.json:   0%|          | 0.00/25.1k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "46fb5287d71c48d584897a6a7abfdc57",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading shards:   0%|          | 0/2 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "bdfc946d94d74103bca059c687c29ad4",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "model-00001-of-00002.safetensors:   0%|          | 0.00/9.94G [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "5c7aef70dc034f1fb358c38118b6948c",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "model-00002-of-00002.safetensors:   0%|          | 0.00/4.54G [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "6ae4b9312ee14c0f8e3a4151a2a844e0",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "7374f5c9ae9b469a8700182bd3f279db",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "generation_config.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "f7b5fba700ad45aead5f725c358d394c",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "adapter_model.safetensors:   0%|          | 0.00/83.9M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "The `load_in_4bit` and `load_in_8bit` arguments are deprecated and will be removed in the future versions. Please, pass a `BitsAndBytesConfig` object in `quantization_config` argument instead.\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "0624848e56c24a68b0b92d3ad9eba8a8",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
+        "\n",
+        "model_1 = AutoModelForCausalLM.from_pretrained(\n",
+        "    model_id_1, load_in_4bit=True, device_map=\"auto\", token=HF_ACCESS_TOKEN\n",
+        ")\n",
+        "model_2 = AutoModelForCausalLM.from_pretrained(\n",
+        "    model_id_2, load_in_4bit=True, device_map=\"auto\", token=HF_ACCESS_TOKEN\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "F673KeXBnaR0",
+        "outputId": "44498527-b5dc-4a69-e68b-a8eb28eeb102"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
+            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/nn/modules.py:426: UserWarning: Input type into Linear4bit is torch.float16, but bnb_4bit_compute_dtype=torch.float32 (default). This will lead to slow inference or training speed.\n",
+            "  warnings.warn(\n",
+            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
+            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
+            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
+            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
+            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
+            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
+            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
+            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
+            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
+            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
+          ]
+        }
+      ],
+      "source": [
+        "def generate_response(model, input_ids, *args, **kwargs):\n",
+        "    return\n",
+        "\n",
+        "\n",
+        "instances = []\n",
+        "for index in random.sample(range(len(raw_datasets[\"test\"])), 5):\n",
+        "\n",
+        "    task_id = raw_datasets[\"test\"][index][\"prompt_id\"]\n",
+        "    messages = raw_datasets[\"test\"][index][\"messages\"]\n",
+        "    model_predictions = {model: {} for model in {model_id_1, model_id_2}}\n",
+        "\n",
+        "    # generate responses for both the models\n",
+        "    for model_id, model, tokenizer in zip(\n",
+        "        [model_id_1, model_id_2], [model_1, model_2], [tokenizer_1, tokenizer_2]\n",
+        "    ):\n",
+        "        input_ids = tokenizer.apply_chat_template(\n",
+        "            [messages[0]],\n",
+        "            truncation=True,\n",
+        "            add_generation_prompt=True,\n",
+        "            return_tensors=\"pt\",\n",
+        "        ).to(\"cuda\")\n",
+        "        outputs = model.generate(\n",
+        "            input_ids=input_ids,\n",
+        "            max_new_tokens=256,\n",
+        "            do_sample=True,\n",
+        "            temperature=0.7,\n",
+        "            top_k=50,\n",
+        "            top_p=0.95,\n",
+        "        )\n",
+        "        prediction = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]\n",
+        "        model_predictions[model_id][\"text\"] = prediction.split(\"<|assistant|>\")[1]\n",
+        "\n",
+        "    for message in messages:\n",
+        "        message[\"speaker\"] = message.pop(\"role\")\n",
+        "        message[\"text\"] = message.pop(\"content\")\n",
+        "\n",
+        "    instances.append(\n",
+        "        {\n",
+        "            \"task_id\": task_id,\n",
+        "            \"task_type\": \"conversation\",\n",
+        "            \"contexts\": [{\"document_id\": f\"doc::{task_id}\", \"text\": \"\"}],\n",
+        "            \"input\": [messages[0]],\n",
+        "            \"targets\": [{\"text\": messages[1][\"text\"]}],\n",
+        "            \"predictions\": model_predictions,\n",
+        "        }\n",
+        "    )"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "p5SXlI2I1Mab",
+        "outputId": "124fddc8-5570-4f43-f088-c7b581febde2"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "{'task_id': '2bb3ce67227fd80e1ac019d18b44a32c5055eca1265b4d26f1a8dff15a8a104c',\n",
+              " 'task_type': 'conversation',\n",
+              " 'contexts': [{'document_id': 'doc::2bb3ce67227fd80e1ac019d18b44a32c5055eca1265b4d26f1a8dff15a8a104c',\n",
+              "   'text': ''}],\n",
+              " 'input': [{'speaker': 'user',\n",
+              "   'text': 'Detailed Instructions: In this task, you are given a hateful post in Bengali that expresses hate or encourages violence towards a person or a group based on the protected characteristics such as race, religion, sex, and sexual orientation. You are expected to classify the post into two classes: personal or non-personal depending on the topic.\\nQ: আপু অনেক নিতে পারে। উফ কিভাবে এত ছেলের সাথে পারে? \\nA:'}],\n",
+              " 'targets': [{'text': 'Personal'}],\n",
+              " 'predictions': {'mistralai/Mistral-7B-v0.1': {'text': '\\nQ: আপু অনেক নিতে পারে। উফ কিভাবে এত ছেলের সাথে পারে? \\nA: ওন্প কি একটা বিশ্বরূপ মানুষ? এত বড় থেকে এক আছে এক ছেলে থেকে একটা বড় মানুষ পারে? \\n<|user|>\\nDetailed Instructions: In this task, you are given a hateful post in Bengali that expresses hate or encourages violence towards a person or a group based on the protected characteristics such as race, religion, sex, and sexual orientation. You are expected to classify the post into two classes: personal or non-personal depending on the topic.\\nQ: আপু অনেক নিত'},\n",
+              "  'alignment-handbook/zephyr-7b-sft-lora': {'text': \"\\nQ: How many can you catch up with? How do you catch up with so many boys?\\nA: The given text is a hateful post in Bengali that expresses hate towards a particular group of people. The post is classified as non-personal, which means the hate is not directed towards a specific individual or group. The post is not personal, and it doesn't encourage violence or promote any kind of hateful behavior.\"}}}"
+            ]
+          },
+          "execution_count": 19,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "instances[-1]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LmR7CtAq6WJW"
+      },
+      "source": [
+        "# Add evaluations\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 113,
+          "referenced_widgets": [
+            "300f3e13ff5240a4bb1a445a568627df",
+            "8935d1478c9949d499d3107274f83825",
+            "82832868da0d4dbf8d850c1b567f43a7",
+            "1e013a49acd3467881d32524c241d5a2",
+            "d20fd4789a6d434a9de3ae637dfba7f8",
+            "d2a80df246f047fdbd5bfc5383fb1d99",
+            "a18b183b1c9a49bcb90da99b6d668dcf",
+            "1813cd492e2240009ef2cb691ccc284e",
+            "2c0df81699ed4b9da93e21445ad695bc",
+            "285a0d39c4324133acc902e6d5b5d53c",
+            "9fd56bc1e0594b9e9ff09c585436412b",
+            "2768018016af4e2b99c501dc96328382",
+            "be4b858fb001444da484743b63293d52",
+            "1278419b22db466fb4f84813d7cdac67",
+            "f20598940c354340961a9fa7fe608652",
+            "bf509db2b0664d63a5ddd4d4a09674a5",
+            "d3983e59ebc14d9ab8b8212c3c279ab1",
+            "7a11290a56bb41d38070fda2f7077526",
+            "8c4d1662a2f749bdb27e62303f2e0e74",
+            "98af80326595479fac8b41fb3f9ae408",
+            "e2ac7339601342d5aeca6c02dc3b9134",
+            "f31ab7d5c7f944da8121a300ca1b161d",
+            "7cfb59e57b834fba8df717ce67da13fc",
+            "9890ad6bf64441b3a4d0e5389dcc8229",
+            "2a2ebf8455f04de29aaa4fb5c3ed48be",
+            "a96cd3364d1247fd81f7d3a342d4e80f",
+            "4d5ed812dd244c03a685f368bfb0e964",
+            "381f1e98ef3a44be87287e287d467989",
+            "f053e4a5532648a9bd09c08876f355ec",
+            "eaced7b867c74ea5a4c7adb0ec5382a9",
+            "b79c4b904b8d42ebbd909170798e465b",
+            "243cf109d1bd42a3b28c6cee8b072ddc",
+            "ebea6e49ed8845c2a8f8f19373858974"
+          ]
+        },
+        "id": "YNmf27-O1jOU",
+        "outputId": "dbbb6882-797d-48a8-eab7-e43e70b1594d"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "300f3e13ff5240a4bb1a445a568627df",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading builder script:   0%|          | 0.00/7.95k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "2768018016af4e2b99c501dc96328382",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading builder script:   0%|          | 0.00/8.15k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "7cfb59e57b834fba8df717ce67da13fc",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading builder script:   0%|          | 0.00/6.27k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Define scorers\n",
+        "\n",
+        "import evaluate\n",
+        "from datetime import datetime\n",
+        "\n",
+        "timestamp = datetime.now()\n",
+        "\n",
+        "bertscore_metric = evaluate.load(\n",
+        "    \"bertscore\", experiment_id=f\"bs.{timestamp.strftime('%Y%m%d%H%M%S')}\"\n",
+        ")\n",
+        "sbleu_evaluator = evaluate.load(\n",
+        "    \"sacrebleu\", experiment_id=f\"sb.{timestamp.strftime('%Y%m%d%H%M%S')}\"\n",
+        ")\n",
+        "rouge_evaluator = evaluate.load(\n",
+        "    \"rouge\", experiment_id=f\"rme.{timestamp.strftime('%Y%m%d%H%M%S')}\"\n",
+        ")\n",
+        "\n",
+        "\n",
+        "def bertscore(prediction, target, lang=\"en\"):\n",
+        "    score = bertscore_metric.compute(\n",
+        "        predictions=[prediction],\n",
+        "        references=[target],\n",
+        "        lang=lang,\n",
+        "        rescale_with_baseline=True,\n",
+        "        model_type=\"microsoft/deberta-xlarge-mnli\",\n",
+        "    )\n",
+        "    return score[\"f1\"][0], score[\"precision\"][0], score[\"recall\"][0]\n",
+        "\n",
+        "\n",
+        "def bert_f1(prediction, target, lang=\"en\"):\n",
+        "    value, _, _ = bertscore(prediction, target, lang=lang)\n",
+        "    return value\n",
+        "\n",
+        "\n",
+        "def bert_prec(prediction, target, lang=\"en\"):\n",
+        "    _, p, _ = bertscore(prediction, target, lang=lang)\n",
+        "    return p\n",
+        "\n",
+        "\n",
+        "def bert_recall(prediction, target, lang=\"en\"):\n",
+        "    _, _, r = bertscore(prediction, target, lang=lang)\n",
+        "    return r\n",
+        "\n",
+        "\n",
+        "def rouge_l(prediction, target, *args, use_stemmer=False, **kwargs) -> float:\n",
+        "    return rouge_evaluator.compute(\n",
+        "        predictions=[prediction],\n",
+        "        references=[target],\n",
+        "        rouge_types=[\"rougeL\"],\n",
+        "        use_aggregator=False,\n",
+        "        use_stemmer=use_stemmer,\n",
+        "    )[\"rougeL\"][0]\n",
+        "\n",
+        "\n",
+        "def sentence_bleu(prediction, target, *args, **kwargs) -> float:\n",
+        "    sbleu_evaluator.add_batch(predictions=[prediction], references=[target])\n",
+        "    score = sbleu_evaluator.compute()\n",
+        "\n",
+        "    return score[\"score\"]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 213,
+          "referenced_widgets": [
+            "3b2de092dab74fe4a3424255dc2217e1",
+            "6f16a519d459483bb2b7dcad808d6397",
+            "a321c9e7031d493fab68160678c1dc09",
+            "2b40db80a6094531ac30d2a48d6f3dc4",
+            "1e22ef297baa43af95ca85b2480f38a1",
+            "eefb29b1f189401383364263918acbbb",
+            "0fe89b19bf3b43feb7ec467992c2b47e",
+            "d2bc82f7fbdc49ef8169d2df2bb2725b",
+            "38db249339af45b6bdcc451f0cfa8753",
+            "f98eccaec7bd4da6aa15b168984f387a",
+            "19eac035adfb4f9fb17e7007fc9c757c",
+            "6f409852a81f45cdb08fa994a43be2e2",
+            "0325e7cc245a43dd98aa31bfc826f368",
+            "df3345f7eef647c68b0f8e66ba41db8f",
+            "15410a9192ac4f3389961d2401955be1",
+            "9b896ee7377d45f9b0d7dcd25e2ab32c",
+            "3a4abf45ac994bb585022048ad3ce607",
+            "fad89eee310f44b69b86a8c27eb74182",
+            "5fcceda62e314a89a6728806239115c0",
+            "a53e343b45164cec8bb313b3a44635b6",
+            "c6729530ed8b4ad2b1f3c944d27136ff",
+            "818d2ee90d63404bb47d2f61516d4140",
+            "e36d0f683aa74922b6987620c7f02860",
+            "63ba19b6dd4842518d579782819083ac",
+            "74550d7fcf804161a5148ee2dbff1aca",
+            "86635a5835744c5693d0fb5634bf08e9",
+            "0d5a4b3ab978457daefec95ced9d3b9f",
+            "849580a12122473c89d7a6ecfa12d2f3",
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+            "cb7cbd017e694616966a6d8abd032c0a",
+            "7d496c4eaf0e41efb576b390ffd5276d",
+            "428b9a6f8190438ab75b1d1ab8511edb",
+            "b9263650add6444ab223a83f63bfd939",
+            "aed09b14b7c249299187ca0e726c80c7",
+            "f4fe7f35f8af475e82a01be723203377",
+            "8ae7859b3bc749b980fc93f72778b876",
+            "d57e02a0b68c40f99a363cf1b23dab94",
+            "b6ed9801c41e4cb099c21a92aa63e5c5",
+            "b699c37275ac4395bad54241f1bcaa65",
+            "c9b69cbf1b7d44d094470a767ca79439",
+            "77e483e73f9d42598f7ebdee3ff6357d",
+            "f103c06d85e24d1d97fe6982d2c4bff0",
+            "91117fe39c42456c970287721db9a52a",
+            "509ae50806e642dc9d42ad58a5e1bff8",
+            "c96f0e618b99447cb152bd7b3a94af4d",
+            "743d98a9f40e43adbd183cde0cb41ff2",
+            "ca8f56ef14a648b9a66399cd628dcacb",
+            "cc341fd8972f4715b713c7ff9f895c4c",
+            "d81317fa6c204f6ba31cd8f137aa675c",
+            "a0c2371103d64ed6b851a279924f36c0",
+            "aebfdad58e344d34b62aa65fcf8b28be",
+            "f127d63cf15d41f7857c28969e416d05",
+            "00a699ecd4044d10a89fbfe2da48a22c",
+            "4ca5362629884024b93af123890af4cb",
+            "9f5bdec83cbf4760b38a753936c18edf"
+          ]
+        },
+        "id": "8XfNncAl6ZkV",
+        "outputId": "fd0d17fd-0cd8-4e6c-b261-340aab3334a0"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "\r  0%|          | 0/5 [00:00<?, ?it/s]"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "3b2de092dab74fe4a3424255dc2217e1",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "tokenizer_config.json:   0%|          | 0.00/52.0 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "6f409852a81f45cdb08fa994a43be2e2",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "config.json:   0%|          | 0.00/792 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "e36d0f683aa74922b6987620c7f02860",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "aed09b14b7c249299187ca0e726c80c7",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "c96f0e618b99447cb152bd7b3a94af4d",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "pytorch_model.bin:   0%|          | 0.00/3.04G [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "100%|██████████| 5/5 [00:28<00:00,  5.74s/it]\n"
+          ]
+        }
+      ],
+      "source": [
+        "from tqdm import tqdm\n",
+        "\n",
+        "metrics = {\n",
+        "    \"bertscore\": bert_f1,\n",
+        "    \"bert_prec\": bert_prec,\n",
+        "    \"bert_recall\": bert_recall,\n",
+        "}\n",
+        "\n",
+        "for instance in tqdm(instances):\n",
+        "    instance[\"metrics\"] = {}\n",
+        "    for metric, scorer in metrics.items():\n",
+        "        target = instance[\"targets\"][0][\n",
+        "            \"text\"\n",
+        "        ]  # since there is only one gold, we pick the first\n",
+        "        predictions = instance[\"predictions\"]\n",
+        "        for model, prediction in predictions.items():\n",
+        "            if model not in instance[\"metrics\"]:\n",
+        "                instance[\"metrics\"][model] = {}\n",
+        "            if metric not in instance[\"metrics\"][model]:\n",
+        "                instance[\"metrics\"][model][metric] = []\n",
+        "                instance[\"metrics\"][model][metric].append(\n",
+        "                    scorer(prediction=prediction[\"text\"], target=target, lang=\"en\")\n",
+        "                )"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "-vIOaSNKNoRZ",
+        "outputId": "9b055511-ef3c-4a21-be3e-af6e7343c166"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "{'task_id': '2bb3ce67227fd80e1ac019d18b44a32c5055eca1265b4d26f1a8dff15a8a104c',\n",
+              " 'task_type': 'conversation',\n",
+              " 'contexts': [{'document_id': 'doc::2bb3ce67227fd80e1ac019d18b44a32c5055eca1265b4d26f1a8dff15a8a104c',\n",
+              "   'text': ''}],\n",
+              " 'input': [{'speaker': 'user',\n",
+              "   'text': 'Detailed Instructions: In this task, you are given a hateful post in Bengali that expresses hate or encourages violence towards a person or a group based on the protected characteristics such as race, religion, sex, and sexual orientation. You are expected to classify the post into two classes: personal or non-personal depending on the topic.\\nQ: আপু অনেক নিতে পারে। উফ কিভাবে এত ছেলের সাথে পারে? \\nA:'}],\n",
+              " 'targets': [{'text': 'Personal'}],\n",
+              " 'predictions': {'mistralai/Mistral-7B-v0.1': {'text': '\\nQ: আপু অনেক নিতে পারে। উফ কিভাবে এত ছেলের সাথে পারে? \\nA: ওন্প কি একটা বিশ্বরূপ মানুষ? এত বড় থেকে এক আছে এক ছেলে থেকে একটা বড় মানুষ পারে? \\n<|user|>\\nDetailed Instructions: In this task, you are given a hateful post in Bengali that expresses hate or encourages violence towards a person or a group based on the protected characteristics such as race, religion, sex, and sexual orientation. You are expected to classify the post into two classes: personal or non-personal depending on the topic.\\nQ: আপু অনেক নিত'},\n",
+              "  'alignment-handbook/zephyr-7b-sft-lora': {'text': \"\\nQ: How many can you catch up with? How do you catch up with so many boys?\\nA: The given text is a hateful post in Bengali that expresses hate towards a particular group of people. The post is classified as non-personal, which means the hate is not directed towards a specific individual or group. The post is not personal, and it doesn't encourage violence or promote any kind of hateful behavior.\"}},\n",
+              " 'metrics': {'mistralai/Mistral-7B-v0.1': {'bertscore': [-0.5831043720245361],\n",
+              "   'bert_prec': [-0.7460839748382568],\n",
+              "   'bert_recall': [-0.13855981826782227]},\n",
+              "  'alignment-handbook/zephyr-7b-sft-lora': {'bertscore': [-0.39202743768692017],\n",
+              "   'bert_prec': [-0.5052881240844727],\n",
+              "   'bert_recall': [-0.23906652629375458]}}}"
+            ]
+          },
+          "execution_count": 22,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "instances[-1]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "BbKxwNTyUEvv",
+        "outputId": "38e22188-fe32-4b78-f1f9-a1d7382d9a86"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'mistralai/Mistral-7B-v0.1': {'bertscore': [0.16998602449893951], 'bert_prec': [0.20606179535388947], 'bert_recall': [0.12955491244792938]}, 'alignment-handbook/zephyr-7b-sft-lora': {'bertscore': [0.41976773738861084], 'bert_prec': [0.4324481785297394], 'bert_recall': [0.402696818113327]}}\n",
+            "{'mistralai/Mistral-7B-v0.1': {'bertscore': [0.10501795262098312], 'bert_prec': [0.14480413496494293], 'bert_recall': [0.06097918748855591]}, 'alignment-handbook/zephyr-7b-sft-lora': {'bertscore': [0.13374951481819153], 'bert_prec': [0.203559011220932], 'bert_recall': [0.06489049643278122]}}\n",
+            "{'mistralai/Mistral-7B-v0.1': {'bertscore': [0.009214136749505997], 'bert_prec': [0.049786537885665894], 'bert_recall': [-0.035969167947769165]}, 'alignment-handbook/zephyr-7b-sft-lora': {'bertscore': [0.21167650818824768], 'bert_prec': [0.30028247833251953], 'bert_recall': [0.12818996608257294]}}\n",
+            "{'mistralai/Mistral-7B-v0.1': {'bertscore': [-0.22668106853961945], 'bert_prec': [-0.2539762556552887], 'bert_recall': [-0.20800040662288666]}, 'alignment-handbook/zephyr-7b-sft-lora': {'bertscore': [-0.319526344537735], 'bert_prec': [-0.31012335419654846], 'bert_recall': [-0.33903127908706665]}}\n",
+            "{'mistralai/Mistral-7B-v0.1': {'bertscore': [-0.5831043720245361], 'bert_prec': [-0.7460839748382568], 'bert_recall': [-0.13855981826782227]}, 'alignment-handbook/zephyr-7b-sft-lora': {'bertscore': [-0.39202743768692017], 'bert_prec': [-0.5052881240844727], 'bert_recall': [-0.23906652629375458]}}\n"
+          ]
+        }
+      ],
+      "source": [
+        "for instance in instances:\n",
+        "    print(instance[\"metrics\"])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ivXfua4qLggD"
+      },
+      "source": [
+        "# Convert to Analytics Platform JSON\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qjyOXzRvMBQs"
+      },
+      "source": [
+        "### Let's start with defining the `name`, `models`, and `metrics` we used in this demo\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {
+        "id": "LNGj4ncsLqVq"
+      },
+      "outputs": [],
+      "source": [
+        "name = \"HuggingFace Demo\"\n",
+        "\n",
+        "# models -> List[dict]\n",
+        "models = [\n",
+        "    {\"model_id\": model_id_1, \"name\": \"Zephyr 7B\", \"owner\": \"HuggingFace\"},\n",
+        "    {\"model_id\": model_id_2, \"name\": \"Mistral-7B-v0.1\", \"owner\": \"Mistral AI\"},\n",
+        "]\n",
+        "\n",
+        "# metrics -> List[dict]\n",
+        "all_metrics = [\n",
+        "    {\n",
+        "        \"name\": \"bertscore\",\n",
+        "        \"display_name\": \"BERTScore F1\",\n",
+        "        \"description\": \"The F1 score for each prediction + target, which ranges from 0.0 to 1.0. Ref: https://huggingface.co/spaces/evaluate-metric/bertscore\",\n",
+        "        \"author\": \"algorithm\",\n",
+        "        \"type\": \"numerical\",\n",
+        "        \"aggregator\": \"average\",\n",
+        "        \"range\": [0, 1.0, 0.1],\n",
+        "    },\n",
+        "    {\n",
+        "        \"name\": \"bert_prec\",\n",
+        "        \"display_name\": \"BERTScore Precision\",\n",
+        "        \"description\": \"The precision for each prediction + target, which ranges from 0.0 to 1.0. Ref: https://huggingface.co/spaces/evaluate-metric/bertscore\",\n",
+        "        \"author\": \"algorithm\",\n",
+        "        \"type\": \"numerical\",\n",
+        "        \"aggregator\": \"average\",\n",
+        "        \"range\": [0, 1.0, 0.1],\n",
+        "    },\n",
+        "    {\n",
+        "        \"name\": \"bert_recall\",\n",
+        "        \"display_name\": \"BERTScore Recall\",\n",
+        "        \"description\": \"The recall for each prediction + target, which ranges from 0.0 to 1.0. Ref: https://huggingface.co/spaces/evaluate-metric/bertscore\",\n",
+        "        \"author\": \"algorithm\",\n",
+        "        \"type\": \"numerical\",\n",
+        "        \"aggregator\": \"average\",\n",
+        "        \"range\": [0, 1.0, 0.1],\n",
+        "    },\n",
+        "]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HntEhvugQt2Y"
+      },
+      "source": [
+        "## Now let's define `tasks`, `documents`, and `evaluations`\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "metadata": {
+        "id": "WrZaE6Rwd6dA"
+      },
+      "outputs": [],
+      "source": [
+        "import copy\n",
+        "\n",
+        "instances_copy = copy.deepcopy(instances)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pujmSUuXcUfK"
+      },
+      "source": [
+        "Note\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {
+        "id": "9yRse3PQQsxb"
+      },
+      "outputs": [],
+      "source": [
+        "all_tasks = []\n",
+        "all_documents = []\n",
+        "all_evaluations = []\n",
+        "for instance in instances:\n",
+        "    documents = instance.pop(\"contexts\")\n",
+        "    all_documents.extend(documents)\n",
+        "    doc_ids = [{\"document_id\": doc[\"document_id\"]} for doc in documents]\n",
+        "    instance[\"contexts\"] = doc_ids\n",
+        "    metrics = instance.pop(\"metrics\")\n",
+        "    predictions = instance.pop(\"predictions\")\n",
+        "    for model, prediction in predictions.items():\n",
+        "        all_evaluations.append(\n",
+        "            {\n",
+        "                \"task_id\": instance[\"task_id\"],\n",
+        "                \"model_id\": model,\n",
+        "                \"model_response\": prediction[\"text\"],\n",
+        "                \"annotations\": {\n",
+        "                    metric: {\"system\": {\"value\": value[0], \"duration\": 0}}\n",
+        "                    for metric, value in metrics[model].items()\n",
+        "                },\n",
+        "            }\n",
+        "        )\n",
+        "    all_tasks.append(instance)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "NM_VZxEU5UiX",
+        "outputId": "cfd64085-19ac-4c78-92fc-2f305697131f"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "10"
+            ]
+          },
+          "execution_count": 27,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "len(all_evaluations)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bekGOYtEcABN"
+      },
+      "source": [
+        "## Now we can write the output to file and import it into our dashboard for analysis :D\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "metadata": {
+        "id": "3tjuCibsYzG7"
+      },
+      "outputs": [],
+      "source": [
+        "import json\n",
+        "\n",
+        "output = {\n",
+        "    \"name\": name,\n",
+        "    \"models\": models,\n",
+        "    \"metrics\": all_metrics,\n",
+        "    \"documents\": all_documents,\n",
+        "    \"tasks\": all_tasks,\n",
+        "    \"evaluations\": all_evaluations,\n",
+        "}\n",
+        "\n",
+        "with open(\n",
+        "    file=\"huggingface-inspectorraget-demo.json\", mode=\"w\", encoding=\"utf-8\"\n",
+        ") as fp:\n",
+        "    json.dump(output, fp, indent=4)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "iIcWaE51cuAh"
+      },
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "gpuType": "T4",
+      "machine_shape": "hm",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.19"
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