{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "0c24ca36-1782-4ce8-8094-6f6528dada19",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "ca69632ef05f489e880b07508f27d06f",
            "8da3dd6d8c4b429dbd210f16a0582bbe",
            "74710e24023a4573aabf17f7700f8826",
            "6308be8b153b4881a474a948bd711e54",
            "3d1ef8ba839b453685e3ab25aa6b6380",
            "9a16234f15644908a80ba1301c301d90",
            "9f5cfca36d974d50a11191d3a8355ba8",
            "10c130bf6603404283cd2b65ae636636",
            "806cd78bd8ca44778cb516e6200266cf",
            "c2094af4b01f4f9cb945e171c0b67bd5",
            "e05dd676fa4f43a59feb98b5972aeb9d",
            "bcfa4830693549289fe8de5d7f97433a",
            "334dce386ffc49418ae87da711b2c8ac",
            "44f37c15b9304849a588fc7fd07836e9",
            "3b70e8bba97b4930849c9d11e7c4e593",
            "841f1fdcce8c48989359dd90b2f1dd36",
            "4d8a473154684687b2ee1581353e7d6e",
            "7b7d0d9117334298988969700907f51a",
            "145665d2283a4e2a89a20cf31239dedf",
            "540682587c0848d5b8f250a23d231d3e"
          ]
        },
        "id": "0c24ca36-1782-4ce8-8094-6f6528dada19",
        "outputId": "b65916f8-e6a8-42b5-fd77-6edb6d49ebe3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ca69632ef05f489e880b07508f27d06f"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install unsloth\n",
        "!pip install --force-reinstall --no-cache-dir --no-deps git+https://github.com/unslothai/unsloth.git"
      ],
      "metadata": {
        "id": "REqA0fPKcTST",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "07f6d779-030a-4307-9841-ebeb77cf026f"
      },
      "id": "REqA0fPKcTST",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting unsloth\n",
            "  Downloading unsloth-2025.1.6-py3-none-any.whl.metadata (53 kB)\n",
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            "\u001b[?25hCollecting unsloth_zoo>=2025.1.4 (from unsloth)\n",
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            "Collecting tyro (from unsloth)\n",
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            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.7/115.7 kB\u001b[0m \u001b[31m10.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading dill-0.3.8-py3-none-any.whl (116 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m11.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading fsspec-2024.9.0-py3-none-any.whl (179 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m179.3/179.3 kB\u001b[0m \u001b[31m17.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading multiprocess-0.70.16-py311-none-any.whl (143 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m143.5/143.5 kB\u001b[0m \u001b[31m15.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading shtab-1.7.1-py3-none-any.whl (14 kB)\n",
            "Downloading cut_cross_entropy-25.1.1-py3-none-any.whl (22 kB)\n",
            "Downloading xxhash-3.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.8/194.8 kB\u001b[0m \u001b[31m16.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: xxhash, shtab, protobuf, hf_transfer, fsspec, dill, multiprocess, tyro, xformers, datasets, cut_cross_entropy, bitsandbytes, trl, unsloth_zoo, unsloth\n",
            "  Attempting uninstall: protobuf\n",
            "    Found existing installation: protobuf 4.25.6\n",
            "    Uninstalling protobuf-4.25.6:\n",
            "      Successfully uninstalled protobuf-4.25.6\n",
            "  Attempting uninstall: fsspec\n",
            "    Found existing installation: fsspec 2024.10.0\n",
            "    Uninstalling fsspec-2024.10.0:\n",
            "      Successfully uninstalled fsspec-2024.10.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "grpcio-status 1.62.3 requires protobuf>=4.21.6, but you have protobuf 3.20.3 which is incompatible.\n",
            "tensorflow-metadata 1.16.1 requires protobuf<6.0.0dev,>=4.25.2; python_version >= \"3.11\", but you have protobuf 3.20.3 which is incompatible.\n",
            "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed bitsandbytes-0.45.1 cut_cross_entropy-25.1.1 datasets-3.2.0 dill-0.3.8 fsspec-2024.9.0 hf_transfer-0.1.9 multiprocess-0.70.16 protobuf-3.20.3 shtab-1.7.1 trl-0.13.0 tyro-0.9.13 unsloth-2025.1.6 unsloth_zoo-2025.1.5 xformers-0.0.29.post1 xxhash-3.5.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "google"
                ]
              },
              "id": "a6fa01fa61274498b3b20b05fc26a356"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting git+https://github.com/unslothai/unsloth.git\n",
            "  Cloning https://github.com/unslothai/unsloth.git to /tmp/pip-req-build-x8rzc8cf\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/unslothai/unsloth.git /tmp/pip-req-build-x8rzc8cf\n",
            "  Resolved https://github.com/unslothai/unsloth.git to commit bdf0cd6033595be4e7ed23d0d002bb176d343152\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: unsloth\n",
            "  Building wheel for unsloth (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for unsloth: filename=unsloth-2025.1.7-py3-none-any.whl size=174896 sha256=45a380b0ad1ecfc4224d614d81bf885af821fade2e2f4fc53da58ba8198d09ca\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-0ogxw7i6/wheels/d1/17/05/850ab10c33284a4763b0595cd8ea9d01fce6e221cac24b3c01\n",
            "Successfully built unsloth\n",
            "Installing collected packages: unsloth\n",
            "  Attempting uninstall: unsloth\n",
            "    Found existing installation: unsloth 2025.1.6\n",
            "    Uninstalling unsloth-2025.1.6:\n",
            "      Successfully uninstalled unsloth-2025.1.6\n",
            "Successfully installed unsloth-2025.1.7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "50190966-b741-47c4-915e-43c81fd1413a",
      "metadata": {
        "id": "50190966-b741-47c4-915e-43c81fd1413a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "408ea58d-715f-4c09-d6be-fe259fa50a0d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: datasets in /usr/local/lib/python3.11/dist-packages (3.2.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (4.67.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from datasets) (3.17.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.11/dist-packages (from datasets) (1.26.4)\n",
            "Requirement already satisfied: pyarrow>=15.0.0 in /usr/local/lib/python3.11/dist-packages (from datasets) (17.0.0)\n",
            "Requirement already satisfied: dill<0.3.9,>=0.3.0 in /usr/local/lib/python3.11/dist-packages (from datasets) (0.3.8)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (from datasets) (2.2.2)\n",
            "Requirement already satisfied: requests>=2.32.2 in /usr/local/lib/python3.11/dist-packages (from datasets) (2.32.3)\n",
            "Requirement already satisfied: xxhash in /usr/local/lib/python3.11/dist-packages (from datasets) (3.5.0)\n",
            "Requirement already satisfied: multiprocess<0.70.17 in /usr/local/lib/python3.11/dist-packages (from datasets) (0.70.16)\n",
            "Requirement already satisfied: fsspec<=2024.9.0,>=2023.1.0 in /usr/local/lib/python3.11/dist-packages (from fsspec[http]<=2024.9.0,>=2023.1.0->datasets) (2024.9.0)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.11/dist-packages (from datasets) (3.11.11)\n",
            "Requirement already satisfied: huggingface-hub>=0.23.0 in /usr/local/lib/python3.11/dist-packages (from datasets) (0.27.1)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from datasets) (24.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.11/dist-packages (from datasets) (6.0.2)\n",
            "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (2.4.4)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (1.3.2)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (24.3.0)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (1.5.0)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (6.1.0)\n",
            "Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (0.2.1)\n",
            "Requirement already satisfied: yarl<2.0,>=1.17.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (1.18.3)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.11/dist-packages (from huggingface-hub>=0.23.0->datasets) (4.12.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests>=2.32.2->datasets) (3.4.1)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests>=2.32.2->datasets) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests>=2.32.2->datasets) (2.3.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests>=2.32.2->datasets) (2024.12.14)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas->datasets) (2024.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas->datasets) (2025.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.17.0)\n",
            "Requirement already satisfied: unsloth in /usr/local/lib/python3.11/dist-packages (2025.1.7)\n",
            "Collecting git+https://github.com/unslothai/unsloth.git\n",
            "  Cloning https://github.com/unslothai/unsloth.git to /tmp/pip-req-build-1608rmeh\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/unslothai/unsloth.git /tmp/pip-req-build-1608rmeh\n",
            "  Resolved https://github.com/unslothai/unsloth.git to commit bdf0cd6033595be4e7ed23d0d002bb176d343152\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: unsloth\n",
            "  Building wheel for unsloth (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for unsloth: filename=unsloth-2025.1.7-py3-none-any.whl size=174896 sha256=87dd520b64e6228f1fc6b01bc10a63919387ef51bfa62043ac48e5ef8574ece3\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-hu99o3ny/wheels/d1/17/05/850ab10c33284a4763b0595cd8ea9d01fce6e221cac24b3c01\n",
            "Successfully built unsloth\n",
            "Installing collected packages: unsloth\n",
            "  Attempting uninstall: unsloth\n",
            "    Found existing installation: unsloth 2025.1.7\n",
            "    Uninstalling unsloth-2025.1.7:\n",
            "      Successfully uninstalled unsloth-2025.1.7\n",
            "Successfully installed unsloth-2025.1.7\n"
          ]
        }
      ],
      "source": [
        "!pip install datasets tqdm\n",
        "!pip install unsloth\n",
        "!pip install --force-reinstall --no-cache-dir --no-deps git+https://github.com/unslothai/unsloth.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "fbc9900d-28d2-4bda-9848-b572fbe778d2",
      "metadata": {
        "id": "fbc9900d-28d2-4bda-9848-b572fbe778d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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        },
        "outputId": "7c330a09-fc7d-46ed-fe30-a41ab41428bb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
            "🦥 Unsloth Zoo will now patch everything to make training faster!\n",
            "==((====))==  Unsloth 2025.1.7: Fast Llama patching. Transformers: 4.47.1.\n",
            "   \\\\   /|    GPU: NVIDIA A100-SXM4-40GB. Max memory: 39.564 GB. Platform: Linux.\n",
            "O^O/ \\_/ \\    Torch: 2.5.1+cu121. CUDA: 8.0. CUDA Toolkit: 12.1. Triton: 3.1.0\n",
            "\\        /    Bfloat16 = TRUE. FA [Xformers = 0.0.29.post1. FA2 = False]\n",
            " \"-____-\"     Free Apache license: http://github.com/unslothai/unsloth\n",
            "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n"
          ]
        },
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Unsloth 2025.1.7 patched 40 layers with 40 QKV layers, 40 O layers and 40 MLP layers.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PeftModelForCausalLM(\n",
              "  (base_model): LoraModel(\n",
              "    (model): LlamaForCausalLM(\n",
              "      (model): LlamaModel(\n",
              "        (embed_tokens): Embedding(100352, 5120, padding_idx=100351)\n",
              "        (layers): ModuleList(\n",
              "          (0-39): 40 x LlamaDecoderLayer(\n",
              "            (self_attn): LlamaAttention(\n",
              "              (q_proj): lora.Linear(\n",
              "                (base_layer): Linear(in_features=5120, out_features=5120, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Identity()\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=5120, out_features=16, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=16, out_features=5120, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "                (lora_magnitude_vector): ModuleDict()\n",
              "              )\n",
              "              (k_proj): lora.Linear(\n",
              "                (base_layer): Linear(in_features=5120, out_features=1280, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Identity()\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=5120, out_features=16, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=16, out_features=1280, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "                (lora_magnitude_vector): ModuleDict()\n",
              "              )\n",
              "              (v_proj): lora.Linear(\n",
              "                (base_layer): Linear(in_features=5120, out_features=1280, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Identity()\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=5120, out_features=16, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=16, out_features=1280, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "                (lora_magnitude_vector): ModuleDict()\n",
              "              )\n",
              "              (o_proj): lora.Linear(\n",
              "                (base_layer): Linear(in_features=5120, out_features=5120, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Identity()\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=5120, out_features=16, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=16, out_features=5120, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "                (lora_magnitude_vector): ModuleDict()\n",
              "              )\n",
              "              (rotary_emb): LlamaRotaryEmbedding()\n",
              "            )\n",
              "            (mlp): LlamaMLP(\n",
              "              (gate_proj): lora.Linear(\n",
              "                (base_layer): Linear(in_features=5120, out_features=17920, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Identity()\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=5120, out_features=16, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=16, out_features=17920, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "                (lora_magnitude_vector): ModuleDict()\n",
              "              )\n",
              "              (up_proj): lora.Linear(\n",
              "                (base_layer): Linear(in_features=5120, out_features=17920, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Identity()\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=5120, out_features=16, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=16, out_features=17920, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "                (lora_magnitude_vector): ModuleDict()\n",
              "              )\n",
              "              (down_proj): lora.Linear(\n",
              "                (base_layer): Linear(in_features=17920, out_features=5120, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Identity()\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=17920, out_features=16, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=16, out_features=5120, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "                (lora_magnitude_vector): ModuleDict()\n",
              "              )\n",
              "              (act_fn): SiLU()\n",
              "            )\n",
              "            (input_layernorm): LlamaRMSNorm((5120,), eps=1e-05)\n",
              "            (post_attention_layernorm): LlamaRMSNorm((5120,), eps=1e-05)\n",
              "          )\n",
              "        )\n",
              "        (norm): LlamaRMSNorm((5120,), eps=1e-05)\n",
              "        (rotary_emb): LlamaRotaryEmbedding()\n",
              "      )\n",
              "      (lm_head): Linear(in_features=5120, out_features=100352, bias=False)\n",
              "    )\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "from unsloth import FastLanguageModel\n",
        "import pandas as pd\n",
        "from datasets import load_dataset\n",
        "import os\n",
        "import torch\n",
        "import torch.nn.functional as F\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
        "from tqdm import tqdm\n",
        "tqdm.pandas()\n",
        "max_seq_length = 2048\n",
        "load_in_4bit = False\n",
        "name = \"1024m/PHI-4-B40\"\n",
        "model, tokenizer = FastLanguageModel.from_pretrained(model_name = name, max_seq_length = max_seq_length, load_in_4bit = load_in_4bit,)\n",
        "model = FastLanguageModel.get_peft_model( model, r = 16, target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\",], lora_alpha = 16, lora_dropout = 0, bias = \"none\", use_gradient_checkpointing = \"unsloth\", random_state = 3407, use_rslora = False, loftq_config = None,)\n",
        "FastLanguageModel.for_inference(model)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**SINGLE TEST CASE**"
      ],
      "metadata": {
        "id": "FICHwqm5aLUV"
      },
      "id": "FICHwqm5aLUV"
    },
    {
      "cell_type": "code",
      "source": [
        "input_text = \"जोड़ें 46,911 + 653,092 ### A) 699,903 B) 700,003 C) 913,203 D) 1,122,202 ### MCQ ###\"\n",
        "prompt = f\"### INPUT : {input_text} RESPONSE : \"\n",
        "message = [{\"role\": \"user\", \"content\": prompt}]\n",
        "inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n",
        "outputs = model.generate(input_ids=inputs, max_new_tokens=200, use_cache=True, temperature=0.1, min_p=0.1, pad_token_id=tokenizer.eos_token_id)\n",
        "response = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
        "processed_response = response.split(\"### RESPONSE :\\nmodel\")[-1].strip()\n",
        "print(f\"Generated Response (20 tokens):\\n{processed_response}\\n\")\n",
        "with torch.no_grad():\n",
        "    outputs = model.generate(input_ids=inputs, max_new_tokens=3, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n",
        "    scores = outputs.scores\n",
        "token_ids_a = tokenizer.encode('A', add_special_tokens=False)[0]\n",
        "token_ids_b = tokenizer.encode('B', add_special_tokens=False)[0]\n",
        "token_ids_c = tokenizer.encode('C', add_special_tokens=False)[0]\n",
        "token_ids_d = tokenizer.encode('D', add_special_tokens=False)[0]\n",
        "for i, score in enumerate(scores, 1):\n",
        "    probs = F.softmax(score, dim=-1)\n",
        "    prob_a = probs[0, token_ids_a].item()\n",
        "    prob_b = probs[0, token_ids_b].item()\n",
        "    prob_c = probs[0, token_ids_c].item()\n",
        "    prob_d = probs[0, token_ids_d].item()\n",
        "    print(f\"Probability of 'A' at token {i}: {prob_a:.4f}\")\n",
        "    print(f\"Probability of 'B' at token {i}: {prob_b:.4f}\")\n",
        "    print(f\"Probability of 'C' at token {i}: {prob_c:.4f}\")\n",
        "    print(f\"Probability of 'D' at token {i}: {prob_d:.4f}\")"
      ],
      "metadata": {
        "id": "r1dozae-gO5B",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f072b695-2824-4ea0-ae0e-9c89b2fd61fa"
      },
      "id": "r1dozae-gO5B",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Generated Response (20 tokens):\n",
            "user### INPUT : जोड़ें 46,911 + 653,092 ### A) 699,903 B) 700,003 C) 913,203 D) 1,122,202 ### MCQ ### RESPONSE :assistantजोड़ने के लिए 46,911 और 653,092:\n",
            "\n",
            "```\n",
            "  46,911\n",
            "+653,092\n",
            "---------\n",
            " 700,003\n",
            "```\n",
            "\n",
            "इसलिए, सही उत्तर है B) 700,003.\n",
            "\n",
            "Probability of 'A' at token 1: 0.0004\n",
            "Probability of 'B' at token 1: 0.0002\n",
            "Probability of 'C' at token 1: 0.0000\n",
            "Probability of 'D' at token 1: 0.0001\n",
            "Probability of 'A' at token 2: 0.0000\n",
            "Probability of 'B' at token 2: 0.0000\n",
            "Probability of 'C' at token 2: 0.0000\n",
            "Probability of 'D' at token 2: 0.0000\n",
            "Probability of 'A' at token 3: 0.0000\n",
            "Probability of 'B' at token 3: 0.0000\n",
            "Probability of 'C' at token 3: 0.0000\n",
            "Probability of 'D' at token 3: 0.0000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**ARC CHALLENGE ENGLISH**"
      ],
      "metadata": {
        "id": "7Al9PZfU2bhu"
      },
      "id": "7Al9PZfU2bhu"
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "1749c745-d1fb-430b-9469-4913bb2a6cb5",
      "metadata": {
        "id": "1749c745-d1fb-430b-9469-4913bb2a6cb5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376,
          "referenced_widgets": [
            "df1fb9504d14452592cbf15434d2bcc7",
            "439b7e33bfa6456c8c74c8b336c5bbf1",
            "9dd65531453a431cb52d8a14912841e3",
            "1eab052b7aee4ab79d19ea726d9db982",
            "fba95c42e535442cb44401af023da530",
            "cf0aec06fb7047b8ba9b1dfac71ba81b",
            "ccb8f39bfd444857bc9acb89bf44e84d",
            "46ef14a39acc4e1a9c199a8842c94ce8",
            "d0b2faadd77a421b91342f6c876b27e8",
            "6996a17852cf4fd79cd6abb409059e5a",
            "561ad42af3ba46a4b17ba9f78be7ab78",
            "ad9660960bf040dea70eb68604d6b0cb",
            "a5fc2120f95040e799d7eb4ed309c1bb",
            "402ae09c743e4a02a021f6292e00a2ab",
            "22f7930c71d648e6a5dcfc58c454e8b3",
            "fd71982505094bc3b11763fcdf8ffab6",
            "611be6d6fdd049fcb338a29c7b51fb93",
            "aa4a791e9c7d4f87b0eeea238c80766a",
            "8dce54f997b64ff4a0ad11ab1dcaf75d",
            "5d4d58e958974389853ac907e67e182e",
            "e66ce370de514e86bb705f9e958f7e02",
            "17f16952ba9c41e2ad3b0f665152985e"
          ]
        },
        "outputId": "73814751-662f-40ea-baef-23ae76e4c4de"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "ARC_Challenge_E.csv:   0%|          | 0.00/367k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "df1fb9504d14452592cbf15434d2bcc7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ad9660960bf040dea70eb68604d6b0cb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1172\n",
            "Average 'tok' value: 70.65870307167235\n",
            "Max 'tok' value: 198\n",
            "Output\n",
            "B    311\n",
            "C    310\n",
            "D    285\n",
            "A    266\n",
            "Name: count, dtype: int64\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1172/1172 [04:36<00:00,  4.23it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ANS\n",
            "B    315\n",
            "C    303\n",
            "D    278\n",
            "A    276\n",
            "Name: count, dtype: int64\n",
            "Accuracy: 0.9266\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "dataset = load_dataset(\"1-800-LLMs/Test-Collection\", data_files=\"ARC_Challenge_E.csv\", split=\"train\")\n",
        "df = dataset.to_pandas()\n",
        "print(len(df))\n",
        "df['tok'] = df['Input'].apply(lambda x: len(tokenizer.encode(x)))\n",
        "print(f\"Average 'tok' value: {df['tok'].mean()}\")\n",
        "print(f\"Max 'tok' value: {df['tok'].max()}\")\n",
        "df = df.sort_values('tok', ascending=False)\n",
        "df['Output'] = df['Output'].replace({'1': 'A', '2': 'B', '3': 'C', '4': 'D'})\n",
        "print(df['Output'].value_counts())\n",
        "responses = []\n",
        "prob_a1_list = []\n",
        "prob_a2_list = []\n",
        "prob_a3_list = []\n",
        "prob_b1_list = []\n",
        "prob_b2_list = []\n",
        "prob_b3_list = []\n",
        "prob_c1_list = []\n",
        "prob_c2_list = []\n",
        "prob_c3_list = []\n",
        "prob_d1_list = []\n",
        "prob_d2_list = []\n",
        "prob_d3_list = []\n",
        "batch_size = 1\n",
        "for start in tqdm(range(0, len(df), batch_size)):\n",
        "    batch_texts = df['Input'][start:start+batch_size].tolist()\n",
        "    for input_text in batch_texts:\n",
        "        prompt = f\"### INPUT : {input_text} Respond with just one letter based on these options : \"\n",
        "        message = [{\"role\": \"user\", \"content\": prompt}]\n",
        "        inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n",
        "        with torch.no_grad():\n",
        "            outputs = model.generate(input_ids=inputs, max_new_tokens=3, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n",
        "            scores = outputs.scores  # tuple of [batch_size, vocab_size] for each token\n",
        "        token_ids_a = tokenizer.encode('A', add_special_tokens=False)[0]\n",
        "        token_ids_b = tokenizer.encode('B', add_special_tokens=False)[0]\n",
        "        token_ids_c = tokenizer.encode('C', add_special_tokens=False)[0]\n",
        "        token_ids_d = tokenizer.encode('D', add_special_tokens=False)[0]\n",
        "        for i in range(3):\n",
        "            if i < len(scores):\n",
        "                probs = F.softmax(scores[i], dim=-1)\n",
        "                prob_a = probs[0, token_ids_a].item()\n",
        "                prob_b = probs[0, token_ids_b].item()\n",
        "                prob_c = probs[0, token_ids_c].item()\n",
        "                prob_d = probs[0, token_ids_d].item()\n",
        "            else:\n",
        "                prob_a, prob_b, prob_c, prob_d = 0.0, 0.0, 0.0, 0.0\n",
        "            if i == 0:\n",
        "                prob_a1_list.append(prob_a)\n",
        "                prob_b1_list.append(prob_b)\n",
        "                prob_c1_list.append(prob_c)\n",
        "                prob_d1_list.append(prob_d)\n",
        "            elif i == 1:\n",
        "                prob_a2_list.append(prob_a)\n",
        "                prob_b2_list.append(prob_b)\n",
        "                prob_c2_list.append(prob_c)\n",
        "                prob_d2_list.append(prob_d)\n",
        "            elif i == 2:\n",
        "                prob_a3_list.append(prob_a)\n",
        "                prob_b3_list.append(prob_b)\n",
        "                prob_c3_list.append(prob_c)\n",
        "                prob_d3_list.append(prob_d)\n",
        "df['A1'] = prob_a1_list\n",
        "df['A2'] = prob_a2_list\n",
        "df['A3'] = prob_a3_list\n",
        "df['B1'] = prob_b1_list\n",
        "df['B2'] = prob_b2_list\n",
        "df['B3'] = prob_b3_list\n",
        "df['C1'] = prob_c1_list\n",
        "df['C2'] = prob_c2_list\n",
        "df['C3'] = prob_c3_list\n",
        "df['D1'] = prob_d1_list\n",
        "df['D2'] = prob_d2_list\n",
        "df['D3'] = prob_d3_list\n",
        "df['A'] = df['A1'] + df['A2'] + df['A3']\n",
        "df['B'] = df['B1'] + df['B2'] + df['B3']\n",
        "df['C'] = df['C1'] + df['C2'] + df['C3']\n",
        "df['D'] = df['D1'] + df['D2'] + df['D3']\n",
        "df['ANS'] = df[['A', 'B', 'C', 'D']].idxmax(axis=1)\n",
        "print(df['ANS'].value_counts())\n",
        "accuracy_arc_c_eng = (df['Output'] == df['ANS']).mean()\n",
        "print(f\"Accuracy: {accuracy_arc_c_eng:.4f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**ARC CHALLENGE HINDI**"
      ],
      "metadata": {
        "id": "PubN4p-32_EC"
      },
      "id": "PubN4p-32_EC"
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = load_dataset(\"1-800-LLMs/Test-Collection\", data_files=\"ARC_Challenge_H.csv\", split=\"train\")\n",
        "df = dataset.to_pandas()\n",
        "print(len(df))\n",
        "df['tok'] = df['Input'].apply(lambda x: len(tokenizer.encode(x)))\n",
        "print(f\"Average 'tok' value: {df['tok'].mean()}\")\n",
        "print(f\"Max 'tok' value: {df['tok'].max()}\")\n",
        "df = df.sort_values('tok', ascending=False)\n",
        "df['Output'] = df['Output'].replace({'1': 'A', '2': 'B', '3': 'C', '4': 'D'})\n",
        "print(df['Output'].value_counts())\n",
        "responses = []\n",
        "prob_a1_list = []\n",
        "prob_a2_list = []\n",
        "prob_a3_list = []\n",
        "prob_b1_list = []\n",
        "prob_b2_list = []\n",
        "prob_b3_list = []\n",
        "prob_c1_list = []\n",
        "prob_c2_list = []\n",
        "prob_c3_list = []\n",
        "prob_d1_list = []\n",
        "prob_d2_list = []\n",
        "prob_d3_list = []\n",
        "batch_size = 1\n",
        "for start in tqdm(range(0, len(df), batch_size)):\n",
        "    batch_texts = df['Input'][start:start+batch_size].tolist()\n",
        "    for input_text in batch_texts:\n",
        "        prompt = f\"### INPUT : {input_text} Respond with just one letter based on these options : \"\n",
        "        message = [{\"role\": \"user\", \"content\": prompt}]\n",
        "        inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n",
        "        with torch.no_grad():\n",
        "            outputs = model.generate(input_ids=inputs, max_new_tokens=3, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n",
        "            scores = outputs.scores  # tuple of [batch_size, vocab_size] for each token\n",
        "        token_ids_a = tokenizer.encode('A', add_special_tokens=False)[0]\n",
        "        token_ids_b = tokenizer.encode('B', add_special_tokens=False)[0]\n",
        "        token_ids_c = tokenizer.encode('C', add_special_tokens=False)[0]\n",
        "        token_ids_d = tokenizer.encode('D', add_special_tokens=False)[0]\n",
        "        for i in range(3):\n",
        "            if i < len(scores):\n",
        "                probs = F.softmax(scores[i], dim=-1)\n",
        "                prob_a = probs[0, token_ids_a].item()\n",
        "                prob_b = probs[0, token_ids_b].item()\n",
        "                prob_c = probs[0, token_ids_c].item()\n",
        "                prob_d = probs[0, token_ids_d].item()\n",
        "            else:\n",
        "                prob_a, prob_b, prob_c, prob_d = 0.0, 0.0, 0.0, 0.0\n",
        "            if i == 0:\n",
        "                prob_a1_list.append(prob_a)\n",
        "                prob_b1_list.append(prob_b)\n",
        "                prob_c1_list.append(prob_c)\n",
        "                prob_d1_list.append(prob_d)\n",
        "            elif i == 1:\n",
        "                prob_a2_list.append(prob_a)\n",
        "                prob_b2_list.append(prob_b)\n",
        "                prob_c2_list.append(prob_c)\n",
        "                prob_d2_list.append(prob_d)\n",
        "            elif i == 2:\n",
        "                prob_a3_list.append(prob_a)\n",
        "                prob_b3_list.append(prob_b)\n",
        "                prob_c3_list.append(prob_c)\n",
        "                prob_d3_list.append(prob_d)\n",
        "df['A1'] = prob_a1_list\n",
        "df['A2'] = prob_a2_list\n",
        "df['A3'] = prob_a3_list\n",
        "df['B1'] = prob_b1_list\n",
        "df['B2'] = prob_b2_list\n",
        "df['B3'] = prob_b3_list\n",
        "df['C1'] = prob_c1_list\n",
        "df['C2'] = prob_c2_list\n",
        "df['C3'] = prob_c3_list\n",
        "df['D1'] = prob_d1_list\n",
        "df['D2'] = prob_d2_list\n",
        "df['D3'] = prob_d3_list\n",
        "df['A'] = df['A1'] + df['A2'] + df['A3']\n",
        "df['B'] = df['B1'] + df['B2'] + df['B3']\n",
        "df['C'] = df['C1'] + df['C2'] + df['C3']\n",
        "df['D'] = df['D1'] + df['D2'] + df['D3']\n",
        "df['ANS'] = df[['A', 'B', 'C', 'D']].idxmax(axis=1)\n",
        "print(df['ANS'].value_counts())\n",
        "accuracy_arc_c_hin = (df['Output'] == df['ANS']).mean()\n",
        "print(f\"Accuracy: {accuracy_arc_c_hin:.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376,
          "referenced_widgets": [
            "e67b27d2113c4f3c9f55c144e2953550",
            "61de01b154fd42fd891946e4f57ef062",
            "c8605e5555194411a861471e9a79a8dc",
            "44a45ce71f2a4fe083f9de004c8b2e37",
            "6fec2129351c4b0da653622894661fbe",
            "aee433f3ad7e413a849e87dd160cd6c2",
            "b750082a1cc543bd9d6bdc5efce2ab6d",
            "c0332da7d3484f2180affaa56fd0c1c3",
            "93a374326f024fc9baa4031b2beee515",
            "740f1c0e77ce4c6a81d2070e4cdba3d2",
            "0bfaff7a38c64724be71d7e8e27943e7",
            "832ea8ddf5e34aa1a41a0c45cb7b54aa",
            "b417a2f4269f4b26845bb51c3c55c5df",
            "819915d795c2442283743ccdb98efa9d",
            "13954dc7ec4d4b0dad204301fbbb188b",
            "fa4cf95db7fa443c8e3d5e788dd4e071",
            "35c062f4fa02494eb3f277925fe1254f",
            "85cca05edb244ff281a4fc17d337fd55",
            "b9c3c58ffb31449b98f28a9ac630583f",
            "16ca6a100d564b458fc4b4f132374cf2",
            "1094ebc9aebd45df9f64619cdc36e6bf",
            "3d59e7a488074a5ebc82c502967362ce"
          ]
        },
        "id": "mPFAiosJ3jzD",
        "outputId": "7ac10110-9109-4f2a-bfe4-b5382c0f8ddc"
      },
      "id": "mPFAiosJ3jzD",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "ARC_Challenge_H.csv:   0%|          | 0.00/822k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e67b27d2113c4f3c9f55c144e2953550"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "832ea8ddf5e34aa1a41a0c45cb7b54aa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1172\n",
            "Average 'tok' value: 264.9129692832764\n",
            "Max 'tok' value: 957\n",
            "Output\n",
            "B    311\n",
            "C    310\n",
            "D    285\n",
            "A    266\n",
            "Name: count, dtype: int64\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1172/1172 [04:58<00:00,  3.93it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ANS\n",
            "B    329\n",
            "C    303\n",
            "A    291\n",
            "D    249\n",
            "Name: count, dtype: int64\n",
            "Accuracy: 0.8225\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**ARC EASY ENGLISH**"
      ],
      "metadata": {
        "id": "cT9I3npw43AP"
      },
      "id": "cT9I3npw43AP"
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = load_dataset(\"1-800-LLMs/Test-Collection\", data_files=\"ARC_Easy_E.csv\", split=\"train\")\n",
        "df = dataset.to_pandas()\n",
        "print(len(df))\n",
        "df['tok'] = df['Input'].apply(lambda x: len(tokenizer.encode(x)))\n",
        "print(f\"Average 'tok' value: {df['tok'].mean()}\")\n",
        "print(f\"Max 'tok' value: {df['tok'].max()}\")\n",
        "df = df.sort_values('tok', ascending=False)\n",
        "df['Output'] = df['Output'].replace({'1': 'A', '2': 'B', '3': 'C', '4': 'D'})\n",
        "print(df['Output'].value_counts())\n",
        "responses = []\n",
        "prob_a1_list = []\n",
        "prob_a2_list = []\n",
        "prob_a3_list = []\n",
        "prob_b1_list = []\n",
        "prob_b2_list = []\n",
        "prob_b3_list = []\n",
        "prob_c1_list = []\n",
        "prob_c2_list = []\n",
        "prob_c3_list = []\n",
        "prob_d1_list = []\n",
        "prob_d2_list = []\n",
        "prob_d3_list = []\n",
        "batch_size = 1\n",
        "for start in tqdm(range(0, len(df), batch_size)):\n",
        "    batch_texts = df['Input'][start:start+batch_size].tolist()\n",
        "    for input_text in batch_texts:\n",
        "        prompt = f\"### INPUT : {input_text} Respond with just one letter based on these options : \"\n",
        "        message = [{\"role\": \"user\", \"content\": prompt}]\n",
        "        inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n",
        "        with torch.no_grad():\n",
        "            outputs = model.generate(input_ids=inputs, max_new_tokens=3, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n",
        "            scores = outputs.scores  # tuple of [batch_size, vocab_size] for each token\n",
        "        token_ids_a = tokenizer.encode('A', add_special_tokens=False)[0]\n",
        "        token_ids_b = tokenizer.encode('B', add_special_tokens=False)[0]\n",
        "        token_ids_c = tokenizer.encode('C', add_special_tokens=False)[0]\n",
        "        token_ids_d = tokenizer.encode('D', add_special_tokens=False)[0]\n",
        "        for i in range(3):\n",
        "            if i < len(scores):\n",
        "                probs = F.softmax(scores[i], dim=-1)\n",
        "                prob_a = probs[0, token_ids_a].item()\n",
        "                prob_b = probs[0, token_ids_b].item()\n",
        "                prob_c = probs[0, token_ids_c].item()\n",
        "                prob_d = probs[0, token_ids_d].item()\n",
        "            else:\n",
        "                prob_a, prob_b, prob_c, prob_d = 0.0, 0.0, 0.0, 0.0\n",
        "            if i == 0:\n",
        "                prob_a1_list.append(prob_a)\n",
        "                prob_b1_list.append(prob_b)\n",
        "                prob_c1_list.append(prob_c)\n",
        "                prob_d1_list.append(prob_d)\n",
        "            elif i == 1:\n",
        "                prob_a2_list.append(prob_a)\n",
        "                prob_b2_list.append(prob_b)\n",
        "                prob_c2_list.append(prob_c)\n",
        "                prob_d2_list.append(prob_d)\n",
        "            elif i == 2:\n",
        "                prob_a3_list.append(prob_a)\n",
        "                prob_b3_list.append(prob_b)\n",
        "                prob_c3_list.append(prob_c)\n",
        "                prob_d3_list.append(prob_d)\n",
        "df['A1'] = prob_a1_list\n",
        "df['A2'] = prob_a2_list\n",
        "df['A3'] = prob_a3_list\n",
        "df['B1'] = prob_b1_list\n",
        "df['B2'] = prob_b2_list\n",
        "df['B3'] = prob_b3_list\n",
        "df['C1'] = prob_c1_list\n",
        "df['C2'] = prob_c2_list\n",
        "df['C3'] = prob_c3_list\n",
        "df['D1'] = prob_d1_list\n",
        "df['D2'] = prob_d2_list\n",
        "df['D3'] = prob_d3_list\n",
        "df['A'] = df['A1'] + df['A2'] + df['A3']\n",
        "df['B'] = df['B1'] + df['B2'] + df['B3']\n",
        "df['C'] = df['C1'] + df['C2'] + df['C3']\n",
        "df['D'] = df['D1'] + df['D2'] + df['D3']\n",
        "df['ANS'] = df[['A', 'B', 'C', 'D']].idxmax(axis=1)\n",
        "print(df['ANS'].value_counts())\n",
        "accuracy_arc_e_eng = (df['Output'] == df['ANS']).mean()\n",
        "print(f\"Accuracy: {accuracy_arc_e_eng:.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393,
          "referenced_widgets": [
            "e825dcfbe3aa478bbe61387314173e7e",
            "047da05da6bb4f77bb17aa1b735559a9",
            "db67995519b2415ea9f485fd72e8ee51",
            "c1261e3a76dd418d9c29310b775327f4",
            "fdd1f83af39a466a95799ccb37681b71",
            "b8e0b143b00840f29f12e1ceb7a2e95c",
            "8ce44602777143d79c0bf05b340828c1",
            "244ef866f5c0401681a63f0a06429e19",
            "c412d5b69e3c48828306e8e186dd0d5d",
            "783b9be106ee4cfbbc2b0e6cb5d9a0f4",
            "763983fb7bc74e1abaa0003ad05e08fb",
            "d4690579650444c2b249820e30ccf614",
            "1c57b673a6574ad0b3f700a64a82f05a",
            "b2d30e3cff21468faa47985819a7460d",
            "a3132eccb78c416881af63e18f5421f5",
            "edf765127ac541c3b51505f19e45e972",
            "24633b0490a64f6a956038a9e6c0bf9a",
            "6c78b7a26f844040b122902d16912db5",
            "bda23c868fc74904970d75d6855a2b7b",
            "08254691ddec458c8ae406999b1f43a0",
            "5bc8f98d5c1547b7bb749dfddecc1ca5",
            "86323c6783cb431c83f9e3fc5cd6ce37"
          ]
        },
        "id": "6vmG3Z92410E",
        "outputId": "2c0f2a2a-8eeb-41df-ca32-8ca31c78e2e9"
      },
      "id": "6vmG3Z92410E",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "ARC_Easy_E.csv:   0%|          | 0.00/627k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e825dcfbe3aa478bbe61387314173e7e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d4690579650444c2b249820e30ccf614"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2376\n",
            "Average 'tok' value: 61.25968013468013\n",
            "Max 'tok' value: 197\n",
            "Output\n",
            "C    633\n",
            "A    596\n",
            "B    585\n",
            "D    561\n",
            "E      1\n",
            "Name: count, dtype: int64\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2376/2376 [09:01<00:00,  4.39it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ANS\n",
            "C    621\n",
            "A    598\n",
            "B    593\n",
            "D    564\n",
            "Name: count, dtype: int64\n",
            "Accuracy: 0.9777\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**ARC EASY HINDI**"
      ],
      "metadata": {
        "id": "A5dtJYX05T5v"
      },
      "id": "A5dtJYX05T5v"
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = load_dataset(\"1-800-LLMs/Test-Collection\", data_files=\"ARC_Easy_H.csv\", split=\"train\")\n",
        "df = dataset.to_pandas()\n",
        "print(len(df))\n",
        "df['tok'] = df['Input'].apply(lambda x: len(tokenizer.encode(x)))\n",
        "print(f\"Average 'tok' value: {df['tok'].mean()}\")\n",
        "print(f\"Max 'tok' value: {df['tok'].max()}\")\n",
        "df = df.sort_values('tok', ascending=False)\n",
        "print(df['Output'].value_counts())\n",
        "df['Output'] = df['Output'].replace({'1': 'A', '2': 'B', '3': 'C', '4': 'D'})\n",
        "responses = []\n",
        "prob_a1_list = []\n",
        "prob_a2_list = []\n",
        "prob_a3_list = []\n",
        "prob_b1_list = []\n",
        "prob_b2_list = []\n",
        "prob_b3_list = []\n",
        "prob_c1_list = []\n",
        "prob_c2_list = []\n",
        "prob_c3_list = []\n",
        "prob_d1_list = []\n",
        "prob_d2_list = []\n",
        "prob_d3_list = []\n",
        "batch_size = 1\n",
        "for start in tqdm(range(0, len(df), batch_size)):\n",
        "    batch_texts = df['Input'][start:start+batch_size].tolist()\n",
        "    for input_text in batch_texts:\n",
        "        prompt = f\"### INPUT : {input_text} Respond with just one letter based on these options : \"\n",
        "        message = [{\"role\": \"user\", \"content\": prompt}]\n",
        "        inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n",
        "        with torch.no_grad():\n",
        "            outputs = model.generate(input_ids=inputs, max_new_tokens=3, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n",
        "            scores = outputs.scores  # tuple of [batch_size, vocab_size] for each token\n",
        "        token_ids_a = tokenizer.encode('A', add_special_tokens=False)[0]\n",
        "        token_ids_b = tokenizer.encode('B', add_special_tokens=False)[0]\n",
        "        token_ids_c = tokenizer.encode('C', add_special_tokens=False)[0]\n",
        "        token_ids_d = tokenizer.encode('D', add_special_tokens=False)[0]\n",
        "        for i in range(3):\n",
        "            if i < len(scores):\n",
        "                probs = F.softmax(scores[i], dim=-1)\n",
        "                prob_a = probs[0, token_ids_a].item()\n",
        "                prob_b = probs[0, token_ids_b].item()\n",
        "                prob_c = probs[0, token_ids_c].item()\n",
        "                prob_d = probs[0, token_ids_d].item()\n",
        "            else:\n",
        "                prob_a, prob_b, prob_c, prob_d = 0.0, 0.0, 0.0, 0.0\n",
        "            if i == 0:\n",
        "                prob_a1_list.append(prob_a)\n",
        "                prob_b1_list.append(prob_b)\n",
        "                prob_c1_list.append(prob_c)\n",
        "                prob_d1_list.append(prob_d)\n",
        "            elif i == 1:\n",
        "                prob_a2_list.append(prob_a)\n",
        "                prob_b2_list.append(prob_b)\n",
        "                prob_c2_list.append(prob_c)\n",
        "                prob_d2_list.append(prob_d)\n",
        "            elif i == 2:\n",
        "                prob_a3_list.append(prob_a)\n",
        "                prob_b3_list.append(prob_b)\n",
        "                prob_c3_list.append(prob_c)\n",
        "                prob_d3_list.append(prob_d)\n",
        "df['A1'] = prob_a1_list\n",
        "df['A2'] = prob_a2_list\n",
        "df['A3'] = prob_a3_list\n",
        "df['B1'] = prob_b1_list\n",
        "df['B2'] = prob_b2_list\n",
        "df['B3'] = prob_b3_list\n",
        "df['C1'] = prob_c1_list\n",
        "df['C2'] = prob_c2_list\n",
        "df['C3'] = prob_c3_list\n",
        "df['D1'] = prob_d1_list\n",
        "df['D2'] = prob_d2_list\n",
        "df['D3'] = prob_d3_list\n",
        "df['A'] = df['A1'] + df['A2'] + df['A3']\n",
        "df['B'] = df['B1'] + df['B2'] + df['B3']\n",
        "df['C'] = df['C1'] + df['C2'] + df['C3']\n",
        "df['D'] = df['D1'] + df['D2'] + df['D3']\n",
        "df['ANS'] = df[['A', 'B', 'C', 'D']].idxmax(axis=1)\n",
        "print(df['ANS'].value_counts())\n",
        "accuracy_arc_e_hin = (df['Output'] == df['ANS']).mean()\n",
        "print(f\"Accuracy: {accuracy_arc_e_hin:.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463,
          "referenced_widgets": [
            "b6acffad53b949c9b8b2c05b2f65ac1d",
            "b70a6af2e43e40e69923dbc065a3bcb4",
            "fd6bda1349bf4e34ab65e215b27e237a",
            "a14e94ca6fc74725a3a56bc547b07a17",
            "13791a5b50574759993068d0a5950149",
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            "90ee545ac5e14bfd80e81f745424192a",
            "ba4f11948a2140fc9f127863ef84a270",
            "112adbbd804e4da9a463e25c1242346d",
            "b62f7b2f63114f7ca04121c8212d6e49",
            "578381c0e74e4f49b1622f48ed3c1db3",
            "a5481b05e34e4dca8b2f4bbd4067d0ef",
            "4ef8dd30106a49568463bfaa5cf8d2d6",
            "84bbe66a0ec44e3db96926f382e99c74",
            "a56a77b2a8e44c2b95573c127b9c010d",
            "66ecfccca6fd4a9aa0d15a040740ba66",
            "8738fdbfc3a9415a984ccc2a75243ce6",
            "1f1b8612300942629d8885fad93a3e41",
            "34f23a7ce77f46b3a9c207ea55753257",
            "08c3a730a87245debfe8f0e61eda3e6a",
            "4592901898d14001972fd748b4488cf6",
            "8eb2f551c26345268a170f7bdf51f599"
          ]
        },
        "id": "aFPK7wPX5TN7",
        "outputId": "d498fd1c-edd9-455a-9677-177472907c15"
      },
      "id": "aFPK7wPX5TN7",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "ARC_Easy_H.csv:   0%|          | 0.00/1.41M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b6acffad53b949c9b8b2c05b2f65ac1d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a5481b05e34e4dca8b2f4bbd4067d0ef"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2376\n",
            "Average 'tok' value: 224.7478956228956\n",
            "Max 'tok' value: 963\n",
            "Output\n",
            "C    610\n",
            "A    570\n",
            "B    563\n",
            "D    535\n",
            "4     26\n",
            "1     26\n",
            "3     23\n",
            "2     22\n",
            "E      1\n",
            "Name: count, dtype: int64\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2376/2376 [10:03<00:00,  3.94it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ANS\n",
            "A    632\n",
            "B    616\n",
            "C    604\n",
            "D    524\n",
            "Name: count, dtype: int64\n",
            "Accuracy: 0.9036\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**MMLU ENGLISH**"
      ],
      "metadata": {
        "id": "pFRbbDbE4Qui"
      },
      "id": "pFRbbDbE4Qui"
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = load_dataset(\"1-800-LLMs/Test-Collection\", data_files=\"MMMLU_E.csv\", split=\"train\")\n",
        "df = dataset.to_pandas()\n",
        "print(len(df))\n",
        "df['tok'] = df['Input'].apply(lambda x: len(tokenizer.encode(x)))\n",
        "print(f\"Average 'tok' value: {df['tok'].mean()}\")\n",
        "print(f\"Max 'tok' value: {df['tok'].max()}\")\n",
        "df = df.sort_values('tok', ascending=False)\n",
        "print(df['Output'].value_counts())\n",
        "responses = []\n",
        "prob_a1_list = []\n",
        "prob_a2_list = []\n",
        "prob_a3_list = []\n",
        "prob_b1_list = []\n",
        "prob_b2_list = []\n",
        "prob_b3_list = []\n",
        "prob_c1_list = []\n",
        "prob_c2_list = []\n",
        "prob_c3_list = []\n",
        "prob_d1_list = []\n",
        "prob_d2_list = []\n",
        "prob_d3_list = []\n",
        "batch_size = 1\n",
        "for start in tqdm(range(0, len(df), batch_size)):\n",
        "    batch_texts = df['Input'][start:start+batch_size].tolist()\n",
        "    for input_text in batch_texts:\n",
        "        prompt = f\"### INPUT : {input_text} Respond with just one letter based on these options : \"\n",
        "        message = [{\"role\": \"user\", \"content\": prompt}]\n",
        "        inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n",
        "        with torch.no_grad():\n",
        "            outputs = model.generate(input_ids=inputs, max_new_tokens=3, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n",
        "            scores = outputs.scores  # tuple of [batch_size, vocab_size] for each token\n",
        "        token_ids_a = tokenizer.encode('A', add_special_tokens=False)[0]\n",
        "        token_ids_b = tokenizer.encode('B', add_special_tokens=False)[0]\n",
        "        token_ids_c = tokenizer.encode('C', add_special_tokens=False)[0]\n",
        "        token_ids_d = tokenizer.encode('D', add_special_tokens=False)[0]\n",
        "        for i in range(3):\n",
        "            if i < len(scores):\n",
        "                probs = F.softmax(scores[i], dim=-1)\n",
        "                prob_a = probs[0, token_ids_a].item()\n",
        "                prob_b = probs[0, token_ids_b].item()\n",
        "                prob_c = probs[0, token_ids_c].item()\n",
        "                prob_d = probs[0, token_ids_d].item()\n",
        "            else:\n",
        "                prob_a, prob_b, prob_c, prob_d = 0.0, 0.0, 0.0, 0.0\n",
        "            if i == 0:\n",
        "                prob_a1_list.append(prob_a)\n",
        "                prob_b1_list.append(prob_b)\n",
        "                prob_c1_list.append(prob_c)\n",
        "                prob_d1_list.append(prob_d)\n",
        "            elif i == 1:\n",
        "                prob_a2_list.append(prob_a)\n",
        "                prob_b2_list.append(prob_b)\n",
        "                prob_c2_list.append(prob_c)\n",
        "                prob_d2_list.append(prob_d)\n",
        "            elif i == 2:\n",
        "                prob_a3_list.append(prob_a)\n",
        "                prob_b3_list.append(prob_b)\n",
        "                prob_c3_list.append(prob_c)\n",
        "                prob_d3_list.append(prob_d)\n",
        "df['A1'] = prob_a1_list\n",
        "df['A2'] = prob_a2_list\n",
        "df['A3'] = prob_a3_list\n",
        "df['B1'] = prob_b1_list\n",
        "df['B2'] = prob_b2_list\n",
        "df['B3'] = prob_b3_list\n",
        "df['C1'] = prob_c1_list\n",
        "df['C2'] = prob_c2_list\n",
        "df['C3'] = prob_c3_list\n",
        "df['D1'] = prob_d1_list\n",
        "df['D2'] = prob_d2_list\n",
        "df['D3'] = prob_d3_list\n",
        "df['A'] = df['A1'] + df['A2'] + df['A3']\n",
        "df['B'] = df['B1'] + df['B2'] + df['B3']\n",
        "df['C'] = df['C1'] + df['C2'] + df['C3']\n",
        "df['D'] = df['D1'] + df['D2'] + df['D3']\n",
        "df['ANS'] = df[['A', 'B', 'C', 'D']].idxmax(axis=1)\n",
        "print(df['ANS'].value_counts())\n",
        "accuracy_mmmlu_eng = (df['Output'] == df['ANS']).mean()\n",
        "print(f\"Accuracy: {accuracy_mmmlu_eng:.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376,
          "referenced_widgets": [
            "3d0291d10fe14190b35db3b04b623d60",
            "d42de3a5998e4a44944e9386588e1969",
            "0ee5850c744841359b3c33bb47146c56",
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            "ff29743075bd48e993a81b5af4d089da",
            "10597fdac1c648d1a9cf62860e4a2b5c",
            "544c6afce9024fd395ac6ab79a251a1e",
            "03da2a33667b4ed189199999993e1b30",
            "655b983deea84118858d89d6fe62170d",
            "84c5aabe7a674165babe86825dfb530c",
            "36d845a630ee48af8b3b7836f518e885",
            "4974ba564a8e438bb23d46929fd6c332",
            "d900b22d23ce41889d478de4aafc6a1d",
            "bb148b9aeffd4d7e8b995c4a535c1b03",
            "e977226e69d14d3da3ac70c5bc7c515a",
            "264593d733a24a8688c7805dc110acc5"
          ]
        },
        "id": "FtThThQC8hs2",
        "outputId": "feed1f45-0996-4ff6-b9c8-623841668515"
      },
      "id": "FtThThQC8hs2",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "MMMLU_E.csv:   0%|          | 0.00/7.07M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3d0291d10fe14190b35db3b04b623d60"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "10597fdac1c648d1a9cf62860e4a2b5c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "14042\n",
            "Average 'tok' value: 106.00548354935195\n",
            "Max 'tok' value: 974\n",
            "Output\n",
            "D    3776\n",
            "C    3582\n",
            "B    3462\n",
            "A    3222\n",
            "Name: count, dtype: int64\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 14042/14042 [59:20<00:00,  3.94it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ANS\n",
            "B    3854\n",
            "C    3616\n",
            "D    3301\n",
            "A    3271\n",
            "Name: count, dtype: int64\n",
            "Accuracy: 0.7587\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**MMMLU HINDI**"
      ],
      "metadata": {
        "id": "FeK3WGqS85al"
      },
      "id": "FeK3WGqS85al"
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = load_dataset(\"1-800-LLMs/Test-Collection\", data_files=\"MMMLU_H.csv\", split=\"train\")\n",
        "df = dataset.to_pandas()\n",
        "print(len(df))\n",
        "df['tok'] = df['Input'].apply(lambda x: len(tokenizer.encode(x)))\n",
        "print(f\"Average 'tok' value: {df['tok'].mean()}\")\n",
        "print(f\"Max 'tok' value: {df['tok'].max()}\")\n",
        "df = df.sort_values('tok', ascending=False)\n",
        "print(df['Output'].value_counts())\n",
        "responses = []\n",
        "prob_a1_list = []\n",
        "prob_a2_list = []\n",
        "prob_a3_list = []\n",
        "prob_b1_list = []\n",
        "prob_b2_list = []\n",
        "prob_b3_list = []\n",
        "prob_c1_list = []\n",
        "prob_c2_list = []\n",
        "prob_c3_list = []\n",
        "prob_d1_list = []\n",
        "prob_d2_list = []\n",
        "prob_d3_list = []\n",
        "batch_size = 1\n",
        "for start in tqdm(range(0, len(df), batch_size)):\n",
        "    batch_texts = df['Input'][start:start+batch_size].tolist()\n",
        "    for input_text in batch_texts:\n",
        "        prompt = f\"### INPUT : {input_text} Respond with just one letter based on these options : \"\n",
        "        message = [{\"role\": \"user\", \"content\": prompt}]\n",
        "        inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n",
        "        with torch.no_grad():\n",
        "            outputs = model.generate(input_ids=inputs, max_new_tokens=3, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n",
        "            scores = outputs.scores  # tuple of [batch_size, vocab_size] for each token\n",
        "        token_ids_a = tokenizer.encode('A', add_special_tokens=False)[0]\n",
        "        token_ids_b = tokenizer.encode('B', add_special_tokens=False)[0]\n",
        "        token_ids_c = tokenizer.encode('C', add_special_tokens=False)[0]\n",
        "        token_ids_d = tokenizer.encode('D', add_special_tokens=False)[0]\n",
        "        for i in range(3):\n",
        "            if i < len(scores):\n",
        "                probs = F.softmax(scores[i], dim=-1)\n",
        "                prob_a = probs[0, token_ids_a].item()\n",
        "                prob_b = probs[0, token_ids_b].item()\n",
        "                prob_c = probs[0, token_ids_c].item()\n",
        "                prob_d = probs[0, token_ids_d].item()\n",
        "            else:\n",
        "                prob_a, prob_b, prob_c, prob_d = 0.0, 0.0, 0.0, 0.0\n",
        "            if i == 0:\n",
        "                prob_a1_list.append(prob_a)\n",
        "                prob_b1_list.append(prob_b)\n",
        "                prob_c1_list.append(prob_c)\n",
        "                prob_d1_list.append(prob_d)\n",
        "            elif i == 1:\n",
        "                prob_a2_list.append(prob_a)\n",
        "                prob_b2_list.append(prob_b)\n",
        "                prob_c2_list.append(prob_c)\n",
        "                prob_d2_list.append(prob_d)\n",
        "            elif i == 2:\n",
        "                prob_a3_list.append(prob_a)\n",
        "                prob_b3_list.append(prob_b)\n",
        "                prob_c3_list.append(prob_c)\n",
        "                prob_d3_list.append(prob_d)\n",
        "df['A1'] = prob_a1_list\n",
        "df['A2'] = prob_a2_list\n",
        "df['A3'] = prob_a3_list\n",
        "df['B1'] = prob_b1_list\n",
        "df['B2'] = prob_b2_list\n",
        "df['B3'] = prob_b3_list\n",
        "df['C1'] = prob_c1_list\n",
        "df['C2'] = prob_c2_list\n",
        "df['C3'] = prob_c3_list\n",
        "df['D1'] = prob_d1_list\n",
        "df['D2'] = prob_d2_list\n",
        "df['D3'] = prob_d3_list\n",
        "df['A'] = df['A1'] + df['A2'] + df['A3']\n",
        "df['B'] = df['B1'] + df['B2'] + df['B3']\n",
        "df['C'] = df['C1'] + df['C2'] + df['C3']\n",
        "df['D'] = df['D1'] + df['D2'] + df['D3']\n",
        "df['ANS'] = df[['A', 'B', 'C', 'D']].idxmax(axis=1)\n",
        "print(df['ANS'].value_counts())\n",
        "accuracy_mmmlu_hin = (df['Output'] == df['ANS']).mean()\n",
        "print(f\"Accuracy: {accuracy_mmmlu_hin:.4f}\")"
      ],
      "metadata": {
        "id": "wDxU0TXK85G7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376,
          "referenced_widgets": [
            "58b357e3545b4518a9825631ccd640d0",
            "a49966b229cc49e59c64da6463c23480",
            "c2b556acdd354b3e9bc78ead0ef072b1",
            "76e1235bbfa5418bb80d7718d1f6810b",
            "1049068e703a413598116ff3fc179300",
            "90034e24b3354355b39354a1266efdbf",
            "890b355a16fd405787301e4aeb6c1e0c",
            "1c5b823db7644e088c8011a12c1763c7",
            "9833c268c3a14c4e87d5f295fa631dab",
            "10c9f4b26d5447319b29fe3a9211d84e",
            "4084c75f5efa497d8e59c083ae10476d",
            "25ae1f19b0ca42b99e26f9e61ed490ac",
            "a10aaae4a769411ea168e641034f8ea2",
            "bb57d4f1f12b49628d0542900a07fe6f",
            "6f6fd4be8b3f412a831831a19a7f0a24",
            "8cfed9c91ab44c0abe2be44fb2b9e397",
            "0e297b20f6234d1080f11c93f112df77",
            "6086830483f54c008413a48e044eac39",
            "3cfd3b2453434eaf9b9ff79ca4a54f59",
            "fc8e0f64afa74bef910f5984378bae47",
            "bbc2d1ec6cc14a5883fd901a142850fa",
            "4f6cbf2b903b41ccb261c45e1692edef"
          ]
        },
        "outputId": "9855369e-7222-400f-b144-e77b90122064"
      },
      "id": "wDxU0TXK85G7",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "MMMLU_H.csv:   0%|          | 0.00/16.6M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "58b357e3545b4518a9825631ccd640d0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "25ae1f19b0ca42b99e26f9e61ed490ac"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "14042\n",
            "Average 'tok' value: 441.8879789203817\n",
            "Max 'tok' value: 4926\n",
            "Output\n",
            "D    3776\n",
            "C    3582\n",
            "B    3462\n",
            "A    3222\n",
            "Name: count, dtype: int64\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 14042/14042 [1:03:32<00:00,  3.68it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ANS\n",
            "A    4504\n",
            "B    3736\n",
            "C    2992\n",
            "D    2810\n",
            "Name: count, dtype: int64\n",
            "Accuracy: 0.5632\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**BOOLQ ENG**"
      ],
      "metadata": {
        "id": "4aC98L-5Gi9D"
      },
      "id": "4aC98L-5Gi9D"
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = load_dataset(\"1-800-LLMs/Test-Collection\", data_files=\"BoolQ_E.csv\", split=\"train\")\n",
        "df = dataset.to_pandas()\n",
        "print(len(df))\n",
        "df['tok'] = df['Input'].apply(lambda x: len(tokenizer.encode(x)))\n",
        "print(f\"Average 'tok' value: {df['tok'].mean()}\")\n",
        "print(f\"Max 'tok' value: {df['tok'].max()}\")\n",
        "df = df.sort_values('tok', ascending=False)\n",
        "print(df['Output'].value_counts())\n",
        "responses = []\n",
        "prob_a1_list = []\n",
        "prob_a2_list = []\n",
        "prob_a3_list = []\n",
        "prob_b1_list = []\n",
        "prob_b2_list = []\n",
        "prob_b3_list = []\n",
        "batch_size = 1\n",
        "for start in tqdm(range(0, len(df), batch_size)):\n",
        "    batch_texts = df['Input'][start:start+batch_size].tolist()\n",
        "    for input_text in batch_texts:\n",
        "        prompt = f\"### INPUT : {input_text} Respond with just one word based on these options : \"\n",
        "        message = [{\"role\": \"user\", \"content\": prompt}]\n",
        "        inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n",
        "        with torch.no_grad():\n",
        "            outputs = model.generate(input_ids=inputs, max_new_tokens=3, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n",
        "            scores = outputs.scores  # tuple of [batch_size, vocab_size] for each token\n",
        "        token_ids_a = tokenizer.encode('True', add_special_tokens=False)[0]\n",
        "        token_ids_b = tokenizer.encode('False', add_special_tokens=False)[0]\n",
        "        for i in range(3):\n",
        "            if i < len(scores):\n",
        "                probs = F.softmax(scores[i], dim=-1)\n",
        "                prob_a = probs[0, token_ids_a].item()\n",
        "                prob_b = probs[0, token_ids_b].item()\n",
        "            else:\n",
        "                prob_a, prob_b = 0.0, 0.0\n",
        "            if i == 0:\n",
        "                prob_a1_list.append(prob_a)\n",
        "                prob_b1_list.append(prob_b)\n",
        "            elif i == 1:\n",
        "                prob_a2_list.append(prob_a)\n",
        "                prob_b2_list.append(prob_b)\n",
        "            elif i == 2:\n",
        "                prob_a3_list.append(prob_a)\n",
        "                prob_b3_list.append(prob_b)\n",
        "df['A1'] = prob_a1_list\n",
        "df['A2'] = prob_a2_list\n",
        "df['A3'] = prob_a3_list\n",
        "df['B1'] = prob_b1_list\n",
        "df['B2'] = prob_b2_list\n",
        "df['B3'] = prob_b3_list\n",
        "df['A'] = df['A1'] + df['A2'] + df['A3']\n",
        "df['B'] = df['B1'] + df['B2'] + df['B3']\n",
        "df['ANS'] = df[['A', 'B']].idxmax(axis=1)\n",
        "df['ANS'] = df['ANS'].replace({'A': 'True', 'B': 'False'})\n",
        "df['ANS'] = df['ANS'].astype(str)\n",
        "df['Output'] = df['Output'].astype(str)\n",
        "print(df['ANS'].value_counts())\n",
        "accuracy_boolq_eng = (df['Output'] == df['ANS']).mean()\n",
        "print(f\"Accuracy: {accuracy_boolq_eng:.4f}\")"
      ],
      "metadata": {
        "id": "m7ayEga9Ghkd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307,
          "referenced_widgets": [
            "793b520b031d40af85b8f35c0fc79ff6",
            "a960febc889b48db9f56c78e694c9256",
            "2a4472fff8454d2883264a23142ba634",
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            "735d6cba7d46459ba6d0c846517d9853",
            "b967e6adec634dc8bb7bdf6c6a6f66ca",
            "3860dd1867f546f0b65c56994f96a48b",
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            "be2e8e4833eb451aa567b19e7b2fa0e4",
            "5b09d1a0dd0c47469677fc9e88e6560d",
            "d7d8c15b2c07483a9232cc30089fb576",
            "cc6592f54ecb4632a34a3f848e96a8c3",
            "6c68863b23634d29a68b4cbb1f2fd8c1",
            "d5fca01dd6bc4f3db91f817bf083fee7",
            "e34d65c6903145019d75ec60c45fcbd8",
            "2b4219da5f734caea4991224544d8498",
            "a095f869c5aa4b83b61d20add2fce739",
            "aa1f0b0da25947d0a649b4cdb798a668",
            "532018f6eb1f4da18d0405ad6f696bc6",
            "4e365680c3fc4daab9915f700a2bc900",
            "ee4c453fb47a432e9025e38d08d926ef",
            "add3e9d3c56241aca98c3472da790713"
          ]
        },
        "outputId": "4dd80755-6adc-4d7f-b558-a59b761d293d"
      },
      "id": "m7ayEga9Ghkd",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "BoolQ_E.csv:   0%|          | 0.00/2.09M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "793b520b031d40af85b8f35c0fc79ff6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cc6592f54ecb4632a34a3f848e96a8c3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3270\n",
            "Average 'tok' value: 136.15474006116207\n",
            "Max 'tok' value: 1156\n",
            "Output\n",
            "True     2033\n",
            "False    1237\n",
            "Name: count, dtype: int64\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 3270/3270 [10:20<00:00,  5.27it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ANS\n",
            "True     1940\n",
            "False    1330\n",
            "Name: count, dtype: int64\n",
            "Accuracy: 0.8865\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**BOOLQ HINDI**"
      ],
      "metadata": {
        "id": "uAhhi93PHZ40"
      },
      "id": "uAhhi93PHZ40"
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = load_dataset(\"1-800-LLMs/Test-Collection\", data_files=\"BoolQ_H.csv\", split=\"train\")\n",
        "df = dataset.to_pandas()\n",
        "print(len(df))\n",
        "df['tok'] = df['Input'].apply(lambda x: len(tokenizer.encode(x)))\n",
        "print(f\"Average 'tok' value: {df['tok'].mean()}\")\n",
        "print(f\"Max 'tok' value: {df['tok'].max()}\")\n",
        "df = df.sort_values('tok', ascending=False)\n",
        "print(df['Output'].value_counts())\n",
        "df = df[1:]\n",
        "responses = []\n",
        "prob_a1_list = []\n",
        "prob_a2_list = []\n",
        "prob_a3_list = []\n",
        "prob_b1_list = []\n",
        "prob_b2_list = []\n",
        "prob_b3_list = []\n",
        "batch_size = 1\n",
        "for start in tqdm(range(0, len(df), batch_size)):\n",
        "    batch_texts = df['Input'][start:start+batch_size].tolist()\n",
        "    for input_text in batch_texts:\n",
        "        prompt = f\"### INPUT : {input_text} Respond with just one word based on these options : \"\n",
        "        message = [{\"role\": \"user\", \"content\": prompt}]\n",
        "        inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n",
        "        with torch.no_grad():\n",
        "            outputs = model.generate(input_ids=inputs, max_new_tokens=3, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n",
        "            scores = outputs.scores  # tuple of [batch_size, vocab_size] for each token\n",
        "        token_ids_a = tokenizer.encode('True', add_special_tokens=False)[0]\n",
        "        token_ids_b = tokenizer.encode('False', add_special_tokens=False)[0]\n",
        "        for i in range(3):\n",
        "            if i < len(scores):\n",
        "                probs = F.softmax(scores[i], dim=-1)\n",
        "                prob_a = probs[0, token_ids_a].item()\n",
        "                prob_b = probs[0, token_ids_b].item()\n",
        "            else:\n",
        "                prob_a, prob_b = 0.0, 0.0\n",
        "            if i == 0:\n",
        "                prob_a1_list.append(prob_a)\n",
        "                prob_b1_list.append(prob_b)\n",
        "            elif i == 1:\n",
        "                prob_a2_list.append(prob_a)\n",
        "                prob_b2_list.append(prob_b)\n",
        "            elif i == 2:\n",
        "                prob_a3_list.append(prob_a)\n",
        "                prob_b3_list.append(prob_b)\n",
        "df['A1'] = prob_a1_list\n",
        "df['A2'] = prob_a2_list\n",
        "df['A3'] = prob_a3_list\n",
        "df['B1'] = prob_b1_list\n",
        "df['B2'] = prob_b2_list\n",
        "df['B3'] = prob_b3_list\n",
        "df['A'] = df['A1'] + df['A2'] + df['A3']\n",
        "df['B'] = df['B1'] + df['B2'] + df['B3']\n",
        "df['ANS'] = df[['A', 'B']].idxmax(axis=1)\n",
        "df['ANS'] = df['ANS'].replace({'A': 'True', 'B': 'False'})\n",
        "df['ANS'] = df['ANS'].astype(str)\n",
        "df['Output'] = df['Output'].astype(str)\n",
        "print(df['ANS'].value_counts())\n",
        "accuracy_boolq_hin = (df['Output'] == df['ANS']).mean()\n",
        "print(f\"Accuracy: {accuracy_boolq_hin:.4f}\")"
      ],
      "metadata": {
        "id": "GOHy6uE285AB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307,
          "referenced_widgets": [
            "e1ad3f36b2ac4a8aa790da8ce449c327",
            "651427b3a9844e04811356910ee6cbda",
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            "b2d1fe4e68b1426b8b1580d83006a96d",
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            "8f8e118c44c44c93975f34c1d44b4f4d",
            "7152093623dc4c188d4dfd46ce3c1f5a",
            "bd96f1e0aabd42c089d1d0a24e5b3e9e",
            "abafa26e88944c46b9bd0b82da2534c8",
            "17a707b9c52b4da6b6a847892e48bbee",
            "90be16b07b504e90a886fb78e425a1d3",
            "bfae50f1546846f3b063d65a27f974ba"
          ]
        },
        "outputId": "26a7f78b-c433-449f-e707-908335a98327"
      },
      "id": "GOHy6uE285AB",
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "BoolQ_H.csv:   0%|          | 0.00/5.28M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e1ad3f36b2ac4a8aa790da8ce449c327"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b2d1fe4e68b1426b8b1580d83006a96d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3270\n",
            "Average 'tok' value: 611.0498470948012\n",
            "Max 'tok' value: 62241\n",
            "Output\n",
            "True     2033\n",
            "False    1237\n",
            "Name: count, dtype: int64\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 3269/3269 [14:50<00:00,  3.67it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ANS\n",
            "True     2132\n",
            "False    1137\n",
            "Name: count, dtype: int64\n",
            "Accuracy: 0.8593\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Context MCQ ENGLISH**"
      ],
      "metadata": {
        "id": "1ugA-oyeReI9"
      },
      "id": "1ugA-oyeReI9"
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = load_dataset(\"1-800-LLMs/Test-Collection\", data_files=\"ContextMCQ_E.csv\", split=\"train\")\n",
        "df = dataset.to_pandas()\n",
        "print(len(df))\n",
        "print(df['Output'].value_counts())\n",
        "responses = []\n",
        "prob_a1_list = []\n",
        "prob_a2_list = []\n",
        "prob_a3_list = []\n",
        "prob_b1_list = []\n",
        "prob_b2_list = []\n",
        "prob_b3_list = []\n",
        "prob_c1_list = []\n",
        "prob_c2_list = []\n",
        "prob_c3_list = []\n",
        "prob_d1_list = []\n",
        "prob_d2_list = []\n",
        "prob_d3_list = []\n",
        "batch_size = 1\n",
        "for start in tqdm(range(0, len(df), batch_size)):\n",
        "    batch_texts = df['Input'][start:start+batch_size].tolist()\n",
        "    for input_text in batch_texts:\n",
        "        prompt = f\"### INPUT : {input_text} Respond with just one letter based on these options : \"\n",
        "        message = [{\"role\": \"user\", \"content\": prompt}]\n",
        "        inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n",
        "        with torch.no_grad():\n",
        "            outputs = model.generate(input_ids=inputs, max_new_tokens=3, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n",
        "            scores = outputs.scores  # tuple of [batch_size, vocab_size] for each token\n",
        "        token_ids_a = tokenizer.encode('A', add_special_tokens=False)[0]\n",
        "        token_ids_b = tokenizer.encode('B', add_special_tokens=False)[0]\n",
        "        token_ids_c = tokenizer.encode('C', add_special_tokens=False)[0]\n",
        "        token_ids_d = tokenizer.encode('D', add_special_tokens=False)[0]\n",
        "        for i in range(3):\n",
        "            if i < len(scores):\n",
        "                probs = F.softmax(scores[i], dim=-1)\n",
        "                prob_a = probs[0, token_ids_a].item()\n",
        "                prob_b = probs[0, token_ids_b].item()\n",
        "                prob_c = probs[0, token_ids_c].item()\n",
        "                prob_d = probs[0, token_ids_d].item()\n",
        "            else:\n",
        "                prob_a, prob_b, prob_c, prob_d = 0.0, 0.0, 0.0, 0.0\n",
        "            if i == 0:\n",
        "                prob_a1_list.append(prob_a)\n",
        "                prob_b1_list.append(prob_b)\n",
        "                prob_c1_list.append(prob_c)\n",
        "                prob_d1_list.append(prob_d)\n",
        "            elif i == 1:\n",
        "                prob_a2_list.append(prob_a)\n",
        "                prob_b2_list.append(prob_b)\n",
        "                prob_c2_list.append(prob_c)\n",
        "                prob_d2_list.append(prob_d)\n",
        "            elif i == 2:\n",
        "                prob_a3_list.append(prob_a)\n",
        "                prob_b3_list.append(prob_b)\n",
        "                prob_c3_list.append(prob_c)\n",
        "                prob_d3_list.append(prob_d)\n",
        "df['A1'] = prob_a1_list\n",
        "df['A2'] = prob_a2_list\n",
        "df['A3'] = prob_a3_list\n",
        "df['B1'] = prob_b1_list\n",
        "df['B2'] = prob_b2_list\n",
        "df['B3'] = prob_b3_list\n",
        "df['C1'] = prob_c1_list\n",
        "df['C2'] = prob_c2_list\n",
        "df['C3'] = prob_c3_list\n",
        "df['D1'] = prob_d1_list\n",
        "df['D2'] = prob_d2_list\n",
        "df['D3'] = prob_d3_list\n",
        "df['A'] = df['A1'] + df['A2'] + df['A3']\n",
        "df['B'] = df['B1'] + df['B2'] + df['B3']\n",
        "df['C'] = df['C1'] + df['C2'] + df['C3']\n",
        "df['D'] = df['D1'] + df['D2'] + df['D3']\n",
        "df['ANS'] = df[['A', 'B', 'C', 'D']].idxmax(axis=1)\n",
        "print(df['ANS'].value_counts())\n",
        "accuracy_mcq_eng = (df['Output'] == df['ANS']).mean()\n",
        "print(f\"Accuracy: {accuracy_mcq_eng:.4f}\")"
      ],
      "metadata": {
        "id": "K4gKxj8ZRdYS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341,
          "referenced_widgets": [
            "be02b6973f90474ebed602189ace07d8",
            "a22cdfcd6e794b558558ba0ee551e3e5",
            "631277eecdc948a9b30de5c0940ee96b",
            "23d327bbfb864ab3916e5ab5e08c1192",
            "38b0810849634b5aadfdd5c69ee24bbf",
            "862d62f863f542738c5f825b690712e7",
            "52fa40aa74744c418c6867026c181892",
            "1f805b3a9a56484580c94561494d4746",
            "a7fbdc8aa2e34f1c92f20a5966f7fde5",
            "0b4343ea70704e8fb862f6772709c99c",
            "ff53938ec0054e05a8d337efb9ea30d8",
            "49d9cf42898d42d1880c95fa7f1903e6",
            "7fe25530a5f94c5b9810e817b1ec42e5",
            "b70d8c8fb0914765924f4773672d4a87",
            "cd3d2636e97c42b5a11abcd044735fd2",
            "90b4ec65a2c4486987874f8ed4698bcf",
            "288d9c74ea1b4b9986f28da85df6aa0c",
            "f5f59d888cac48cf9ba02707ed792e6f",
            "6e4ff282a6fa489285f70efd83932590",
            "9b03f9741ed0476bacefe9f95e1a4b93",
            "07949eb4cd8c4edaa6a3617df4253fc3",
            "a7f6f2d9149e4320a9ca24fcec62fbf3"
          ]
        },
        "outputId": "a91d65ae-d895-437e-b88e-d50ed0de50c3"
      },
      "id": "K4gKxj8ZRdYS",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "ContextMCQ_E.csv:   0%|          | 0.00/1.63M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "be02b6973f90474ebed602189ace07d8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "49d9cf42898d42d1880c95fa7f1903e6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1000\n",
            "Output\n",
            "C    280\n",
            "B    244\n",
            "D    241\n",
            "A    235\n",
            "Name: count, dtype: int64\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1000/1000 [04:17<00:00,  3.88it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ANS\n",
            "C    265\n",
            "A    259\n",
            "B    251\n",
            "D    225\n",
            "Name: count, dtype: int64\n",
            "Accuracy: 0.8670\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Context MCQ HINDI**"
      ],
      "metadata": {
        "id": "JVW_cii1SR3c"
      },
      "id": "JVW_cii1SR3c"
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = load_dataset(\"1-800-LLMs/Test-Collection\", data_files=\"ContextMCQ_H.csv\", split=\"train\")\n",
        "df = dataset.to_pandas()\n",
        "print(len(df))\n",
        "print(df['Output'].value_counts())\n",
        "responses = []\n",
        "prob_a1_list = []\n",
        "prob_a2_list = []\n",
        "prob_a3_list = []\n",
        "prob_b1_list = []\n",
        "prob_b2_list = []\n",
        "prob_b3_list = []\n",
        "prob_c1_list = []\n",
        "prob_c2_list = []\n",
        "prob_c3_list = []\n",
        "prob_d1_list = []\n",
        "prob_d2_list = []\n",
        "prob_d3_list = []\n",
        "batch_size = 1\n",
        "for start in tqdm(range(0, len(df), batch_size)):\n",
        "    batch_texts = df['Input'][start:start+batch_size].tolist()\n",
        "    for input_text in batch_texts:\n",
        "        prompt = f\"### INPUT : {input_text} Respond with just one letter based on these options : \"\n",
        "        message = [{\"role\": \"user\", \"content\": prompt}]\n",
        "        inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n",
        "        with torch.no_grad():\n",
        "            outputs = model.generate(input_ids=inputs, max_new_tokens=3, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n",
        "            scores = outputs.scores\n",
        "        token_ids_a = tokenizer.encode('A', add_special_tokens=False)[0]\n",
        "        token_ids_b = tokenizer.encode('B', add_special_tokens=False)[0]\n",
        "        token_ids_c = tokenizer.encode('C', add_special_tokens=False)[0]\n",
        "        token_ids_d = tokenizer.encode('D', add_special_tokens=False)[0]\n",
        "        for i in range(3):\n",
        "            if i < len(scores):\n",
        "                probs = F.softmax(scores[i], dim=-1)\n",
        "                prob_a = probs[0, token_ids_a].item()\n",
        "                prob_b = probs[0, token_ids_b].item()\n",
        "                prob_c = probs[0, token_ids_c].item()\n",
        "                prob_d = probs[0, token_ids_d].item()\n",
        "            else:\n",
        "                prob_a, prob_b, prob_c, prob_d = 0.0, 0.0, 0.0, 0.0\n",
        "            if i == 0:\n",
        "                prob_a1_list.append(prob_a)\n",
        "                prob_b1_list.append(prob_b)\n",
        "                prob_c1_list.append(prob_c)\n",
        "                prob_d1_list.append(prob_d)\n",
        "            elif i == 1:\n",
        "                prob_a2_list.append(prob_a)\n",
        "                prob_b2_list.append(prob_b)\n",
        "                prob_c2_list.append(prob_c)\n",
        "                prob_d2_list.append(prob_d)\n",
        "            elif i == 2:\n",
        "                prob_a3_list.append(prob_a)\n",
        "                prob_b3_list.append(prob_b)\n",
        "                prob_c3_list.append(prob_c)\n",
        "                prob_d3_list.append(prob_d)\n",
        "df['A1'] = prob_a1_list\n",
        "df['A2'] = prob_a2_list\n",
        "df['A3'] = prob_a3_list\n",
        "df['B1'] = prob_b1_list\n",
        "df['B2'] = prob_b2_list\n",
        "df['B3'] = prob_b3_list\n",
        "df['C1'] = prob_c1_list\n",
        "df['C2'] = prob_c2_list\n",
        "df['C3'] = prob_c3_list\n",
        "df['D1'] = prob_d1_list\n",
        "df['D2'] = prob_d2_list\n",
        "df['D3'] = prob_d3_list\n",
        "df['A'] = df['A1'] + df['A2'] + df['A3']\n",
        "df['B'] = df['B1'] + df['B2'] + df['B3']\n",
        "df['C'] = df['C1'] + df['C2'] + df['C3']\n",
        "df['D'] = df['D1'] + df['D2'] + df['D3']\n",
        "df['ANS'] = df[['A', 'B', 'C', 'D']].idxmax(axis=1)\n",
        "print(df['ANS'].value_counts())\n",
        "accuracy_mcq_hin = (df['Output'] == df['ANS']).mean()\n",
        "print(f\"Accuracy: {accuracy_mcq_hin:.4f}\")"
      ],
      "metadata": {
        "id": "HrB5mDcf842y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341,
          "referenced_widgets": [
            "4f4b31a126f347d8ad79f45f5cfbc315",
            "1798b5a65fc74c4cb5bf25981efb52fa",
            "88ce30d7f13a4507bbebeb13962e7847",
            "04f1c53577c145e9870ce5c8e71d5b67",
            "9b4f34314e40434d8ea08594c7918d1d",
            "cf78502cdeb34031b68ae7700369455c",
            "10e0c24170be43dba7c4158fbaedf3a0",
            "2a6534869a5c46b89635423ab69d4529",
            "70763cb1910144e3930cdb396ac239a0",
            "95fa8dc5658244ccafb26fed6b9d88ec",
            "4c832cd8c37f49649ceea26a0a33056d",
            "d8f89f33f7544211b97cb75b1f99ae85",
            "3f0628e27a4b4349b44d0707f952cdf4",
            "8f6d463d8c944f39b2f1120cd52c2a71",
            "d68e369d5f1b42a6bb11e6c405c1717e",
            "60d5a80f91cb4a4081c2bfb529c40489",
            "8d810e7f8b5242f7aea33f9730ddf10d",
            "adaa07a0e3af47b4a62c5d3eb3d9fd2c",
            "e8ad988da5944140bd98fce3b114d695",
            "b73a982b28a743cfb162f408fd9d3aa7",
            "20619623613643aaaf5039d4bb8d2f7f",
            "78a2910ac93b4196b0acb2df5baa1fb7"
          ]
        },
        "outputId": "ecbef93e-57b7-4125-f126-8aae482a2d12"
      },
      "id": "HrB5mDcf842y",
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "ContextMCQ_H.csv:   0%|          | 0.00/4.14M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4f4b31a126f347d8ad79f45f5cfbc315"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d8f89f33f7544211b97cb75b1f99ae85"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1000\n",
            "Output\n",
            "C    280\n",
            "B    244\n",
            "D    241\n",
            "A    235\n",
            "Name: count, dtype: int64\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1000/1000 [06:39<00:00,  2.50it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ANS\n",
            "A    270\n",
            "B    262\n",
            "C    254\n",
            "D    214\n",
            "Name: count, dtype: int64\n",
            "Accuracy: 0.7830\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**END**"
      ],
      "metadata": {
        "id": "JqYw49CH3gfX"
      },
      "id": "JqYw49CH3gfX"
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"BOOLQ ENGLISH : \" ,accuracy_boolq_eng)\n",
        "print(\"BOOLQ HINDI   : \" ,accuracy_boolq_hin)\n",
        "print(\"C-MCQ ENGLISH : \" ,accuracy_mcq_eng)\n",
        "print(\"C-MCQ HINDI   : \" ,accuracy_mcq_hin)\n",
        "print(\"MMMLU ENGLISH : \" ,accuracy_mmmlu_eng)\n",
        "print(\"MMMLU HINDI   : \" ,accuracy_mmmlu_hin)\n",
        "print(\"ARC-E ENGLISH : \" ,accuracy_arc_e_eng)\n",
        "print(\"ARC-E HINDI   : \" ,accuracy_arc_e_hin)\n",
        "print(\"ARC-C ENGLISH : \" ,accuracy_arc_c_eng)\n",
        "print(\"ARC-C HINDI   : \" ,accuracy_arc_c_hin)\n",
        "avg_hin_acc = (accuracy_boolq_hin + accuracy_mcq_hin + accuracy_mmmlu_hin + accuracy_arc_e_hin + accuracy_arc_c_hin)/5\n",
        "avg_eng_acc = (accuracy_boolq_eng + accuracy_mcq_eng + accuracy_mmmlu_eng + accuracy_arc_e_eng + accuracy_arc_c_eng)/5\n",
        "print(\"AVG SCORE : HINDI   : \" ,avg_hin_acc)\n",
        "print(\"AVG SCORE : ENGLISH : \" ,avg_eng_acc)\n",
        "avg_tot_acc = (avg_hin_acc + avg_eng_acc)/2\n",
        "print(\"TOT AVG SCORE : \" ,avg_tot_acc)\n",
        "print(\"CLICK CTRl+S and wait for 2 sec\")\n",
        "name = name.split('/')[-1]\n",
        "name = name + \".ipynb\"\n",
        "print(\"1) NOTEBOOK NAME SHOULD BE : \", name)\n",
        "print(\"2) ADD THE CODE TO GITHUB @ https://github.com/1-800-SHARED-TASKS/New-Language-Adaptation/tree/main/Our-Evals/ALL-EVALS/ \")\n",
        "print(\"3) UPDATE THE GOOGLE SHEET WITH THE SCORES \")"
      ],
      "metadata": {
        "id": "ljShzEUtnaDH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f0d25fe7-ff5e-42f9-a597-ec48ca43f58a"
      },
      "id": "ljShzEUtnaDH",
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BOOLQ ENGLISH :  0.8865443425076452\n",
            "BOOLQ HINDI   :  0.8592841847659834\n",
            "C-MCQ ENGLISH :  0.867\n",
            "C-MCQ HINDI   :  0.783\n",
            "MMMLU ENGLISH :  0.758652613587808\n",
            "MMMLU HINDI   :  0.5632388548639795\n",
            "ARC-E ENGLISH :  0.9776936026936027\n",
            "ARC-E HINDI   :  0.9036195286195287\n",
            "ARC-C ENGLISH :  0.9266211604095563\n",
            "ARC-C HINDI   :  0.8225255972696246\n",
            "AVG SCORE : HINDI   :  0.7863336331038232\n",
            "AVG SCORE : ENGLISH :  0.8833023438397225\n",
            "TOT AVG SCORE :  0.8348179884717728\n",
            "CLICK CTRl+S and wait for 2 sec\n",
            "1) NOTEBOOK NAME SHOULD BE :  PHI-4-B40.ipynb\n",
            "2) ADD THE CODE TO GITHUB @ https://github.com/1-800-SHARED-TASKS/New-Language-Adaptation/tree/main/Our-Evals/ALL-EVALS/ \n",
            "3) UPDATE THE GOOGLE SHEET WITH THE SCORES \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
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