{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0c24ca36-1782-4ce8-8094-6f6528dada19",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331,
          "referenced_widgets": [
            "3bdcbf053400426889ce191e8e631c34",
            "c436a45ebb964940a65f31c4c032e59a",
            "64b42b59dbdf419a811678e0343fa14f",
            "61f0d8a908d8467a833a01645bf4df93",
            "b3c7f82cdfe941ddb8a322b519f4f8d7",
            "30a0c3ad3d1643d2988ed6cb1e607f3c",
            "636b613ccc794d388f3b4859e058a39c",
            "193ba6195d6240a2be0ab007d617d7d5",
            "20d9a5b63fb1497aaab412e7165ed55b",
            "3abc169a4a6546c7b728cea12c6d566e",
            "09326715e33945359d64e8c981857eea",
            "4d20a4b6057a44238f382c015649842b",
            "89aa4177a076483fa584d032251b55ff",
            "bcfc20a2d38642ce97393c9d2ec2afbd",
            "bf696b1bb83f4268a60ebe493f8c820f",
            "5c44c227ee9449ffb035012568fb68b3",
            "1fcf6c9e4ab34f2985a35fe4693253cc"
          ]
        },
        "id": "0c24ca36-1782-4ce8-8094-6f6528dada19",
        "outputId": "aa64f424-eeb1-4436-9613-30a61e7d8a16"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3bdcbf053400426889ce191e8e631c34",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co./front/assets/huggingface_logo-noborder.sv…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "REqA0fPKcTST",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "REqA0fPKcTST",
        "outputId": "9cc3b5e0-742c-4950-a55f-986b82b770c7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "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-6ox6dh2t\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/unslothai/unsloth.git /tmp/pip-req-build-6ox6dh2t\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=c12b855cab97d9ad14eba95512736444bc87eb17675e9901e7a85b287329f4f4\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-jmm7lbvf/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 unsloth\n",
        "!pip install --force-reinstall --no-cache-dir --no-deps git+https://github.com/unslothai/unsloth.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "50190966-b741-47c4-915e-43c81fd1413a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "50190966-b741-47c4-915e-43c81fd1413a",
        "outputId": "3f3c3201-7e91-4fda-e648-fa00fd511f64"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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) (25.1.0)\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-qusvkvpa\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/unslothai/unsloth.git /tmp/pip-req-build-qusvkvpa\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=a4d066cafb63394d66b3d00fb3d64317b9792855fcc85dea5ca314da4fab7f71\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-r6tvmroq/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": null,
      "id": "fbc9900d-28d2-4bda-9848-b572fbe778d2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "b0eaa4eebd7647f19f0509e54d1771f5",
            "ab6a8171c33345489e0f57c4dbf5fe37",
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            "e2f0f3c9d0f446ce84bc30f562e63aac",
            "9bd89ecc06214af3a1018701fa3e8758",
            "d35ba0d1406c48f2862b8989c1c3a2a2",
            "def55d121c2341b789e777d3ee41a65d",
            "3cd55085208f4f09b9442d9cc6635935",
            "1cdec6fd6ccf415888a1cd7275d07e00",
            "c43792fa6b9b4d51ac11ead6f6f67f47",
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        },
        "id": "fbc9900d-28d2-4bda-9848-b572fbe778d2",
        "outputId": "d57e8d82-0ee7-4736-aa1b-174881a5ca80"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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: If you want to finetune Gemma 2, install flash-attn to make it faster!\n",
            "To install flash-attn, do the below:\n",
            "\n",
            "pip install --no-deps --upgrade \"flash-attn>=2.6.3\"\n",
            "==((====))==  Unsloth 2025.1.7: Fast Gemma2 patching. Transformers: 4.47.1.\n",
            "   \\\\   /|    GPU: NVIDIA A100-SXM4-40GB. Max memory: 39.557 GB. Platform: Linux.\n",
            "O^O/ \\_/ \\    Torch: 2.5.1+cu124. CUDA: 8.0. CUDA Toolkit: 12.4. 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"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b0eaa4eebd7647f19f0509e54d1771f5",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "model.safetensors.index.json:   0%|          | 0.00/39.1k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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        {
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          "metadata": {},
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        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Unsloth 2025.1.7 patched 42 layers with 42 QKV layers, 42 O layers and 42 MLP layers.\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "PeftModelForCausalLM(\n",
              "  (base_model): LoraModel(\n",
              "    (model): Gemma2ForCausalLM(\n",
              "      (model): Gemma2Model(\n",
              "        (embed_tokens): Embedding(256000, 3584, padding_idx=0)\n",
              "        (layers): ModuleList(\n",
              "          (0-41): 42 x Gemma2DecoderLayer(\n",
              "            (self_attn): Gemma2Attention(\n",
              "              (q_proj): lora.Linear(\n",
              "                (base_layer): Linear(in_features=3584, out_features=4096, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Identity()\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=3584, out_features=16, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=16, out_features=4096, 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=3584, out_features=2048, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Identity()\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=3584, out_features=16, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=16, out_features=2048, 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=3584, out_features=2048, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Identity()\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=3584, out_features=16, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=16, out_features=2048, 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=4096, out_features=3584, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Identity()\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=4096, out_features=16, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=16, out_features=3584, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "                (lora_magnitude_vector): ModuleDict()\n",
              "              )\n",
              "              (rotary_emb): GemmaFixedRotaryEmbedding()\n",
              "            )\n",
              "            (mlp): Gemma2MLP(\n",
              "              (gate_proj): lora.Linear(\n",
              "                (base_layer): Linear(in_features=3584, out_features=14336, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Identity()\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=3584, out_features=16, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=16, out_features=14336, 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=3584, out_features=14336, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Identity()\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=3584, out_features=16, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=16, out_features=14336, 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=14336, out_features=3584, bias=False)\n",
              "                (lora_dropout): ModuleDict(\n",
              "                  (default): Identity()\n",
              "                )\n",
              "                (lora_A): ModuleDict(\n",
              "                  (default): Linear(in_features=14336, out_features=16, bias=False)\n",
              "                )\n",
              "                (lora_B): ModuleDict(\n",
              "                  (default): Linear(in_features=16, out_features=3584, bias=False)\n",
              "                )\n",
              "                (lora_embedding_A): ParameterDict()\n",
              "                (lora_embedding_B): ParameterDict()\n",
              "                (lora_magnitude_vector): ModuleDict()\n",
              "              )\n",
              "              (act_fn): PytorchGELUTanh()\n",
              "            )\n",
              "            (input_layernorm): Gemma2RMSNorm((3584,), eps=1e-06)\n",
              "            (post_attention_layernorm): Gemma2RMSNorm((3584,), eps=1e-06)\n",
              "            (pre_feedforward_layernorm): Gemma2RMSNorm((3584,), eps=1e-06)\n",
              "            (post_feedforward_layernorm): Gemma2RMSNorm((3584,), eps=1e-06)\n",
              "          )\n",
              "        )\n",
              "        (norm): Gemma2RMSNorm((3584,), eps=1e-06)\n",
              "      )\n",
              "      (lm_head): Linear(in_features=3584, out_features=256000, bias=False)\n",
              "    )\n",
              "  )\n",
              ")"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "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 = \"DrishtiSharma/GEMMA-9B-B60\"\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",
      "id": "FICHwqm5aLUV",
      "metadata": {
        "id": "FICHwqm5aLUV"
      },
      "source": [
        "##**SINGLE TEST CASE**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "r1dozae-gO5B",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r1dozae-gO5B",
        "outputId": "7da92a7e-6427-44f3-8ed2-4dfc000dae27"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "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"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Generated Response (20 tokens):\n",
            "The correct answer is **B) 700,003**.  Here's how to solve it:\n",
            "\n",
            "```\n",
            "  46,911\n",
            "+ 653,092\n",
            "----------\n",
            "  700,003 \n",
            "```\n",
            "\n",
            "Probability of 'A' at token 1: 0.0118\n",
            "Probability of 'B' at token 1: 0.0056\n",
            "Probability of 'C' at token 1: 0.0005\n",
            "Probability of 'D' at token 1: 0.0004\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"
          ]
        }
      ],
      "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}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7Al9PZfU2bhu",
      "metadata": {
        "id": "7Al9PZfU2bhu"
      },
      "source": [
        "#**ARC CHALLENGE ENGLISH**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1749c745-d1fb-430b-9469-4913bb2a6cb5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1749c745-d1fb-430b-9469-4913bb2a6cb5",
        "outputId": "6932819b-5ed1-4d0f-85b1-4a2eed1cf259"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1172\n",
            "Average 'tok' value: 70.26706484641639\n",
            "Max 'tok' value: 197\n",
            "Output\n",
            "B    311\n",
            "C    310\n",
            "D    285\n",
            "A    266\n",
            "Name: count, dtype: int64\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 1172/1172 [04:40<00:00,  4.18it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "ANS\n",
            "C    312\n",
            "B    308\n",
            "D    277\n",
            "A    275\n",
            "Name: count, dtype: int64\n",
            "Accuracy: 0.8831\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "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",
      "id": "PubN4p-32_EC",
      "metadata": {
        "id": "PubN4p-32_EC"
      },
      "source": [
        "#**ARC CHALLENGE HINDI**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "mPFAiosJ3jzD",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mPFAiosJ3jzD",
        "outputId": "da6c0aa5-2f23-4d14-f6ba-86b118581da5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1172\n",
            "Average 'tok' value: 114.31313993174061\n",
            "Max 'tok' value: 389\n",
            "Output\n",
            "B    311\n",
            "C    310\n",
            "D    285\n",
            "A    266\n",
            "Name: count, dtype: int64\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 1172/1172 [04:45<00:00,  4.11it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "ANS\n",
            "C    303\n",
            "B    301\n",
            "A    298\n",
            "D    270\n",
            "Name: count, dtype: int64\n",
            "Accuracy: 0.7841\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "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}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cT9I3npw43AP",
      "metadata": {
        "id": "cT9I3npw43AP"
      },
      "source": [
        "#**ARC EASY ENGLISH**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6vmG3Z92410E",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6vmG3Z92410E",
        "outputId": "6ed0582e-673d-491a-e54a-c57c854f8002"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2376\n",
            "Average 'tok' value: 60.46590909090909\n",
            "Max 'tok' value: 193\n",
            "Output\n",
            "C    633\n",
            "A    596\n",
            "B    585\n",
            "D    561\n",
            "E      1\n",
            "Name: count, dtype: int64\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 2376/2376 [09:30<00:00,  4.16it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "ANS\n",
            "C    635\n",
            "A    608\n",
            "B    576\n",
            "D    557\n",
            "Name: count, dtype: int64\n",
            "Accuracy: 0.9575\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "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}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "A5dtJYX05T5v",
      "metadata": {
        "id": "A5dtJYX05T5v"
      },
      "source": [
        "#**ARC EASY HINDI**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "aFPK7wPX5TN7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aFPK7wPX5TN7",
        "outputId": "edc55cbc-1c60-408c-e6a2-b6b01ad57e42"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2376\n",
            "Average 'tok' value: 99.67003367003367\n",
            "Max 'tok' value: 650\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"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 2376/2376 [09:31<00:00,  4.16it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "ANS\n",
            "A    643\n",
            "C    616\n",
            "B    573\n",
            "D    544\n",
            "Name: count, dtype: int64\n",
            "Accuracy: 0.9066\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "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}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "pFRbbDbE4Qui",
      "metadata": {
        "id": "pFRbbDbE4Qui"
      },
      "source": [
        "#**MMLU ENGLISH**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "FtThThQC8hs2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FtThThQC8hs2",
        "outputId": "80c6b942-f523-40c2-9cf0-090976b2c566"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "14042\n",
            "Average 'tok' value: 106.63509471585245\n",
            "Max 'tok' value: 1009\n",
            "Output\n",
            "D    3776\n",
            "C    3582\n",
            "B    3462\n",
            "A    3222\n",
            "Name: count, dtype: int64\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 14042/14042 [56:37<00:00,  4.13it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "ANS\n",
            "B    3696\n",
            "D    3486\n",
            "A    3461\n",
            "C    3399\n",
            "Name: count, dtype: int64\n",
            "Accuracy: 0.6863\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "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}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "FeK3WGqS85al",
      "metadata": {
        "id": "FeK3WGqS85al"
      },
      "source": [
        "#**MMMLU HINDI**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "wDxU0TXK85G7",
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "wDxU0TXK85G7",
        "outputId": "8b300950-d0c3-4674-f40d-45a7ee21aff1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "14042\n",
            "Average 'tok' value: 180.577624270047\n",
            "Max 'tok' value: 1917\n",
            "Output\n",
            "D    3776\n",
            "C    3582\n",
            "B    3462\n",
            "A    3222\n",
            "Name: count, dtype: int64\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 14042/14042 [56:28<00:00,  4.14it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "ANS\n",
            "A    3935\n",
            "B    3680\n",
            "D    3391\n",
            "C    3036\n",
            "Name: count, dtype: int64\n",
            "Accuracy: 0.5419\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "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}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4aC98L-5Gi9D",
      "metadata": {
        "id": "4aC98L-5Gi9D"
      },
      "source": [
        "#**BOOLQ ENG**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "m7ayEga9Ghkd",
      "metadata": {
        "id": "m7ayEga9Ghkd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ea86ed57-d039-4a6d-97f6-f7dbcfeb4e58"
      },
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "3270\n",
            "Average 'tok' value: 138.53669724770643\n",
            "Max 'tok' value: 1248\n",
            "Output\n",
            "True     2033\n",
            "False    1237\n",
            "Name: count, dtype: int64\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 93%|█████████▎| 3045/3270 [12:14<00:54,  4.16it/s]"
          ]
        }
      ],
      "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}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "uAhhi93PHZ40",
      "metadata": {
        "id": "uAhhi93PHZ40"
      },
      "source": [
        "#**BOOLQ HINDI**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "GOHy6uE285AB",
      "metadata": {
        "id": "GOHy6uE285AB"
      },
      "outputs": [],
      "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}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1ugA-oyeReI9",
      "metadata": {
        "id": "1ugA-oyeReI9"
      },
      "source": [
        "#**Context MCQ ENGLISH**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "K4gKxj8ZRdYS",
      "metadata": {
        "id": "K4gKxj8ZRdYS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "44e932d2-a26a-4e04-e83d-57a4fef16e09"
      },
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "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 [04:05<00:00,  4.07it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ANS\n",
            "C    267\n",
            "A    265\n",
            "B    235\n",
            "D    233\n",
            "Name: count, dtype: int64\n",
            "Accuracy: 0.8800\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ],
      "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}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "JVW_cii1SR3c",
      "metadata": {
        "id": "JVW_cii1SR3c"
      },
      "source": [
        "#**Context MCQ HINDI**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "HrB5mDcf842y",
      "metadata": {
        "id": "HrB5mDcf842y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "78c3db34-863f-4232-fb08-2a1c501cb305"
      },
      "outputs": [
        {
          "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 [04:09<00:00,  4.00it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ANS\n",
            "D    273\n",
            "C    270\n",
            "A    245\n",
            "B    212\n",
            "Name: count, dtype: int64\n",
            "Accuracy: 0.7890\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ],
      "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}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "JqYw49CH3gfX",
      "metadata": {
        "id": "JqYw49CH3gfX"
      },
      "source": [
        "#**END**"
      ]
    },
    {
      "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 \")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gI14YhuEPPQD",
        "outputId": "3a2318bb-10d9-4f40-e8dd-e924cab91fe4"
      },
      "id": "gI14YhuEPPQD",
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BOOLQ ENGLISH :  0.8681957186544342\n",
            "BOOLQ HINDI   :  0.8519424900581217\n",
            "C-MCQ ENGLISH :  0.88\n",
            "C-MCQ HINDI   :  0.789\n",
            "MMMLU ENGLISH :  0.6862982481128045\n",
            "MMMLU HINDI   :  0.5418743768693918\n",
            "ARC-E ENGLISH :  0.9574915824915825\n",
            "ARC-E HINDI   :  0.9065656565656566\n",
            "ARC-C ENGLISH :  0.8831058020477816\n",
            "ARC-C HINDI   :  0.7841296928327645\n",
            "AVG SCORE : HINDI   :  0.774702443265187\n",
            "AVG SCORE : ENGLISH :  0.8550182702613206\n",
            "TOT AVG SCORE :  0.8148603567632537\n",
            "CLICK CTRl+S and wait for 2 sec\n",
            "1) NOTEBOOK NAME SHOULD BE :  GEMMA-9B-B60.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"
          ]
        }
      ]
    },
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        "id": "ZTTdwadzMMB3"
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    }
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