{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "0c24ca36-1782-4ce8-8094-6f6528dada19",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "8470541e8354458ba0400701dda96e94",
            "862c213779884cff964df1cb1f4b4d7f",
            "d5f508118bab42f9a634b3d7446d5e7d",
            "e6f6245252df48939b7e3d15d369268b",
            "732eefd307b84772b659833097fe58a1",
            "53498c8fb8bc4e629434a5ad8a5374a7",
            "c63316d877f1428a99de77dc1a097674",
            "361eb2c0d44940a9b7485fe75d972dbf",
            "f0ff2fe20d4949c983e2c88e5624a77f",
            "907244c3291a4c92a76499d869e22d67",
            "6664309661dc4727b9623445d921ff20",
            "9f370a4a79fb44c080bf0e64310bc7f8",
            "942b90a6d9e4412ab45ded8ae54200e3",
            "8d34cf271cd84bba87814d61484c6fc4",
            "cab52e8f14a9433bb9fe0d48d334925b",
            "67b084a0b9e6415b85bbbc0c3ec26d0b",
            "b103100bfba042eb94792c6c55d439b9",
            "b6c06a77b24741f3896d9bf48c710b65",
            "942dc298c9ec40cd83abf9aec89953c3",
            "e3e0dc48f1cf4eddabaa06b53eac9477"
          ]
        },
        "id": "0c24ca36-1782-4ce8-8094-6f6528dada19",
        "outputId": "eeb86517-22e4-4e32-bf26-6a0d2d16962a"
      },
      "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": "8470541e8354458ba0400701dda96e94"
            }
          },
          "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/"
        },
        "outputId": "206fedc6-14ec-4d66-a9f9-042b2384ffb0"
      },
      "id": "REqA0fPKcTST",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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-s2w1fk9x\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/unslothai/unsloth.git /tmp/pip-req-build-s2w1fk9x\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=15d585afc0604b6ebfb439641914e3734fa9e8f5073d48f5376ed494c1340748\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-mw5k7gz3/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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "50190966-b741-47c4-915e-43c81fd1413a",
      "metadata": {
        "id": "50190966-b741-47c4-915e-43c81fd1413a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "962a4b88-4e15-4465-8517-61dcb7cb5f60"
      },
      "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-qerh5fta\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/unslothai/unsloth.git /tmp/pip-req-build-qerh5fta\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=cdd4a67a688eeb74cd44e5e491d7f855bae1dc180a10c2e030501c20a4bc70e2\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-79v2l9op/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": 6,
      "id": "fbc9900d-28d2-4bda-9848-b572fbe778d2",
      "metadata": {
        "id": "fbc9900d-28d2-4bda-9848-b572fbe778d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "27be8877314d4db98821f695fd425a28",
            "5efeb154e8204265881f3fa1e52ad9dc",
            "b6284b8659104c90a649f794018450fd",
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            "b46b22acbc424f5e9e8a176960fb5a38",
            "ae3ec930bd30432b8bcb680a0e81820e",
            "b9e9d94b7b6d4a94b2c59e7aee57d88a",
            "20c1c640988c4dfb97a9dc8938958d37",
            "50ac2e86a23c4ab09120d947019a644b",
            "e2f1450dfcfc42c7ab904aff81bd9875",
            "f2a4d90a4a1f427f96eedc57982a2511",
            "52fc437fecba43dd8ff9de8a374d712c",
            "658ae5303f7a4d8bac60af820c98ccaf"
          ]
        },
        "outputId": "27f49367-1714-417b-802a-36fbaa6fba74"
      },
      "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": 6
        }
      ],
      "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-B50\"\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": "25a0f170-a32d-450e-c468-79de2fa0c3fd"
      },
      "id": "r1dozae-gO5B",
      "execution_count": 7,
      "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, हम इसे इस प्रकार कर सकते हैं:\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: 0.0019\n",
            "Probability of 'B' at token 1: 0.0008\n",
            "Probability of 'C' at token 1: 0.0002\n",
            "Probability of 'D' at token 1: 0.0006\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": 8,
      "id": "1749c745-d1fb-430b-9469-4913bb2a6cb5",
      "metadata": {
        "id": "1749c745-d1fb-430b-9469-4913bb2a6cb5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376,
          "referenced_widgets": [
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            "bdf65757c0be457c80d0f85c4ad6bae5",
            "1974f69da08642e2a143c7f08fe5df66",
            "519234d3a15545b5b00142f175094c0e"
          ]
        },
        "outputId": "ae5be05a-126f-4449-865f-cb7f97ed31f7"
      },
      "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": "0266377e35a9488a9db6d991eabe9aba"
            }
          },
          "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": "e9f0bcb8d9e2410a8d90afef78726c49"
            }
          },
          "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 [04:08<00:00,  4.72it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ANS\n",
            "B    313\n",
            "C    306\n",
            "D    282\n",
            "A    271\n",
            "Name: count, dtype: int64\n",
            "Accuracy: 0.9317\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": [
            "548a540b6e614ad3a4f0ddb81c9bcff7",
            "70507e82fdfd4947a14e87d4491cf13b",
            "420e167b269a4022905423bb1eb65844",
            "7ff04894617641849122df81012981e3",
            "e7d1d053aba8460cbe03e0aa6fa25673",
            "97e6b6b14e344859af23b2c30e3d8b40",
            "25c9ad0b624045b2bd0e9f9363db162d",
            "ed55b87ec77246df921d686f7e86caaf",
            "fc1eb2e83ce3463bb6ec44dd46a80f85",
            "af393a5ec870474ab60a925c3d25b408",
            "d1df0b751d4a41e29adcf58414426249",
            "cfed71f34c0b4ab981499de9fd665d95",
            "f877477ccce746d2964a6824f4f52d5a",
            "306177d1f7cc4bed9f7700cccc7eec43",
            "73e369beb7d843e5bb814de1253cc124",
            "6afee0c927a643d5bd56af6f50e512c4",
            "6e3ad86f20994baabea43279751478bd",
            "1f3778ec3c0b4d798bfe94b88e31b035",
            "697a446e6724447e92b1b684b89e8809",
            "4069e1c6d3ed40858fba525fde8e8ff5",
            "f7e8833947634e2cba6dd0eff837917e",
            "861ec85cb1e9400e9ef66e1664818e53"
          ]
        },
        "id": "mPFAiosJ3jzD",
        "outputId": "9eb6f981-c940-4e82-d354-059e6daa7eac"
      },
      "id": "mPFAiosJ3jzD",
      "execution_count": 9,
      "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": "548a540b6e614ad3a4f0ddb81c9bcff7"
            }
          },
          "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": "cfed71f34c0b4ab981499de9fd665d95"
            }
          },
          "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 [04:59<00:00,  3.91it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ANS\n",
            "B    329\n",
            "C    303\n",
            "D    273\n",
            "A    267\n",
            "Name: count, dtype: int64\n",
            "Accuracy: 0.8294\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\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": [
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            "19a0d9a536ca4a55b930811e8508a742",
            "c8ad2019ac4e4e3c9edfb7a1ed47f687",
            "5de513717e674e719a1842656f602463",
            "dd6e9cdd9a6b470c8bbfefbfcb54f022",
            "71b825746dcb48b1a9a365636a0227bc",
            "6521d0505cd7422f8de89862c8370a43",
            "e0324a1d1c724cb890114339b789b7bb",
            "ff5ff541de6d492daf295bab582cf619",
            "b467800d8ff646cdbc41bf03d65aeb97",
            "7ed6355c6a4c4b52ad6847e0fa31f594",
            "a1ebe039bb634445b1606fa00fb82fc0"
          ]
        },
        "id": "6vmG3Z92410E",
        "outputId": "4af326bd-7d3c-45aa-b65e-f2a6a3bd759b"
      },
      "id": "6vmG3Z92410E",
      "execution_count": 10,
      "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": "91fc49c70e9041d193c47544bd22a801"
            }
          },
          "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": "19a0d9a536ca4a55b930811e8508a742"
            }
          },
          "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 [08:11<00:00,  4.83it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ANS\n",
            "C    620\n",
            "A    598\n",
            "B    596\n",
            "D    562\n",
            "Name: count, dtype: int64\n",
            "Accuracy: 0.9785\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": [
            "5585f95991f04383ba94a2dae34402d2",
            "d645d163a7ed4d1794e6dfaec4d8f9bb",
            "434dd801dfbb4cc4b6b9c1da658a5522",
            "095f890e29c6421b94d4a7dc169782cf",
            "49f8cd02b251489ab29bfba3d4d62bbf",
            "4f03fcb67b1844c09200488cf9e1bde0",
            "44427edad93b4955848d4d63893087af",
            "41b255419cbb431e91735128d4382ebe",
            "26b7491e683f45a09a742e8c31630ee7",
            "e6626d26e3e84227a7c11445b500620f",
            "e76abd19e5a34af4bd7f45a639ec027b",
            "d9489a904f8a4ffdb29658218977467f",
            "30fc769b033e41469f06f489a30a0c4e",
            "949f665567a94df6adb59e9d39f11410",
            "21d4e42f2e1f4902939bbaeea9e9f66f",
            "af600b16a840473e9f204277e079d965",
            "37936cc9312a4baf8289792455dc16db",
            "a3e0f7a23bcd4ff1b787316e58bddf49",
            "a24fa7f21de2426785db2e6497351f2a",
            "06821f7c76a8453d8be472fadeccfa43",
            "7369871807fe4da4a95c177ceb5256ef",
            "28ca5ffeecb54136918805010979b927"
          ]
        },
        "id": "aFPK7wPX5TN7",
        "outputId": "229564c9-0ee4-42fc-a210-b5e3e9eedfaa"
      },
      "id": "aFPK7wPX5TN7",
      "execution_count": 11,
      "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": "5585f95991f04383ba94a2dae34402d2"
            }
          },
          "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": "d9489a904f8a4ffdb29658218977467f"
            }
          },
          "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    614\n",
            "C    607\n",
            "B    606\n",
            "D    549\n",
            "Name: count, dtype: int64\n",
            "Accuracy: 0.9108\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": [
            "3b5db172f7324ed9a96baeb898417527",
            "e651d143f5aa45a5bf8745157da704c2",
            "e808136e0ddf4e5b8dce5bc24b1a78cd",
            "cc7e6e405f064407a66a7b1245f98f01",
            "9a5c8b7b6e0048069127fb8b4adea83a",
            "8fa71d4752fe490f81cc440abc088ae8",
            "b22f2d0ed22d484cbd11d7f4e8ecf851",
            "5af925aade7d463cbda1ce02a6f2decd",
            "377205fcfd704f0c81abda86060dc223",
            "8e104f3a21a44f39bf693b500ec7cc98",
            "13ebde15c5384fa8999e26be8f98765b",
            "e24026385e1945f4ae713c4b0e341dd4",
            "de0d1259fba64832a6e5da9a2772e5de",
            "e6a43dcb6c4147368d1ee5e6c23a69e4",
            "e782f13a49184a608e12c17e163f91f3",
            "b16dc2c6ec9346bca3b4c45b9fd1e3ce",
            "a108b183cd104f08b013460b303bbabe",
            "3f56bb2d693947f98408a24bcfd777ad",
            "7c514b42bde145018e70345781a5adac",
            "b997e5b5548d47c49b1d1f5f6b1f79e1",
            "bee09f1a8626467e9948d4a5c795ee40",
            "20895c87312a42458ee35b191abb54cd"
          ]
        },
        "id": "FtThThQC8hs2",
        "outputId": "8aa30eac-c262-4a34-aede-78d620d49c4b"
      },
      "id": "FtThThQC8hs2",
      "execution_count": 12,
      "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": "3b5db172f7324ed9a96baeb898417527"
            }
          },
          "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": "e24026385e1945f4ae713c4b0e341dd4"
            }
          },
          "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 [1:00:06<00:00,  3.89it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ANS\n",
            "B    3888\n",
            "C    3583\n",
            "D    3495\n",
            "A    3076\n",
            "Name: count, dtype: int64\n",
            "Accuracy: 0.7652\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": [
            "a300c7ccf5a04e3a930e2f00ff28b11a",
            "226860b8b2724b7387d52685737d8c0d",
            "29c6e4a389a841f0bc32d755bafe751a",
            "c91c4cce4d234a34a6656bf7e1f04dfe",
            "b451df6d6ff549899076714e88e4d564",
            "68ed5e756d5045c4a77538d54390f4c2",
            "851a7d63bc33464d923e3103fb67979a",
            "617d54a0f99b4b54ab8e8baf30a4bf8b",
            "029f690f6a61443292066378f1c67821",
            "6b8bd438d16848b1ace54d74e67da3fe",
            "e3d319dad00145b988a7268ae44f44e1",
            "4806ead599da4578b3519d16e11fcab1",
            "a634aedb53db48d181cb4227ac2371cc",
            "e521a1c23d1245d19553f7c866e67993",
            "60d91935a5f946dd94ea706d5391572b",
            "a129698556ab4aa18e9bb02b5b939fed",
            "8b0c43c84ec34302a6e655f0f5312e7d",
            "ece5e18311a741e6926fe23fbbb65f84",
            "8a159edd4696432cb10dd882d33c1445",
            "97359e17a16d47d0ac6a38e0d3a598aa",
            "9f24803ae754469686d5dfacc753e1d7",
            "413cc6a3156042f68e39f2c31f13b153"
          ]
        },
        "outputId": "ae2bd102-ebf1-4b3d-cdaa-1ec2f0b2984c"
      },
      "id": "wDxU0TXK85G7",
      "execution_count": 13,
      "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": "a300c7ccf5a04e3a930e2f00ff28b11a"
            }
          },
          "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": "4806ead599da4578b3519d16e11fcab1"
            }
          },
          "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 [1:04:52<00:00,  3.61it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ANS\n",
            "B    3966\n",
            "A    3541\n",
            "D    3490\n",
            "C    3045\n",
            "Name: count, dtype: int64\n",
            "Accuracy: 0.5790\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": [
            "8b85658c9c804caf85f61861a5ec9d9d",
            "9f9272e9d0a446c3b4f67ba8ea9cdac0",
            "3bec4149c9f94a2b86182952aed3aea8",
            "1765a7aa974a4ae6a010fa5f85ba316b",
            "038d8ca394894192916a29c81decd80a",
            "fca881c0e3c940f99e3b0ec1870d117b",
            "1a1c2d0f39df4596ad037f8091902565",
            "4d3a6b0d0bc44ff0a764dde0fba88687",
            "bf89610851be47f5a6064f312546d3ab",
            "0b17e85fb8dd406bbdc40c1a4bd09b8a",
            "caf7cd2a6421414c905f9c4ef9d40002",
            "602a2bc18d5f44f58da9f4a29d8056a6",
            "e7475ae73f88423b9ff1a37c5dcf7469",
            "14bcf274ed51497d9db44f3e84b85652",
            "3d90d5b1a1a64adb857b59043efb13b7",
            "8f05471620404fa4a9c0502b2dc276d0",
            "159189681c7f4907a2d41d33f2ff83fd",
            "d193f5d471924015a857a89bf15ffe13",
            "2b064424339f4cbfbac856a99a3a1eb3",
            "85a99cfba4ec4ba4bf0475b0408004b3",
            "5dcd02f0449141668f33ae55eb2e887d",
            "6c139030bbca4fe9971702ffe986216b"
          ]
        },
        "outputId": "5df053d5-53e0-4b6e-b2ec-2cb00cb713b1"
      },
      "id": "m7ayEga9Ghkd",
      "execution_count": 14,
      "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": "8b85658c9c804caf85f61861a5ec9d9d"
            }
          },
          "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": "602a2bc18d5f44f58da9f4a29d8056a6"
            }
          },
          "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 [10:31<00:00,  5.18it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ANS\n",
            "True     1927\n",
            "False    1343\n",
            "Name: count, dtype: int64\n",
            "Accuracy: 0.8832\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": [
            "ba4052c88ec24b908269c91199734ec6",
            "151d15d1ae364643a2b5722b189bf43b",
            "a2012b1d0ab74727a15c63d7a8d0af18",
            "37b2820a6001436994754b2ad8ed7398",
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            "1b36c5e617504bef9f2e5c4f0bf232be",
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            "a866d00ba3bc47b091cb56846b14235c",
            "7869336a7d69471a94e6d2915118d8ed",
            "08ea7a03dab24935aeb9a6720c0f9d10",
            "7729ded18e8040a1a4203d065ee4caab",
            "2377a284d30c4ceca309217fae1d0556",
            "dc9dae28516d4aed882bec5c535a86a1",
            "a717b550ae3049e0af1c66186495c4bc",
            "310447f896a045adb2970de9d91fe87f",
            "80e6dc4d4b35454fbe5800f473bad0ee",
            "d81a9e28f97e4448a1414ebddd6e3bf1",
            "c0202b036220433cb445a8685c81ba82",
            "fee0542ebf744d42be67af5d6d839dab",
            "c48a81f5b52147f39be9910c9977f84c"
          ]
        },
        "outputId": "4cebc8d5-0f74-4987-fff0-7c50c134ec05"
      },
      "id": "GOHy6uE285AB",
      "execution_count": 15,
      "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": "ba4052c88ec24b908269c91199734ec6"
            }
          },
          "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": "08ea7a03dab24935aeb9a6720c0f9d10"
            }
          },
          "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 [15:07<00:00,  3.60it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ANS\n",
            "True     2171\n",
            "False    1098\n",
            "Name: count, dtype: int64\n",
            "Accuracy: 0.8522\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": [
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        "outputId": "d2c91fcb-9e9e-45b9-8f42-107cabfce3be"
      },
      "id": "K4gKxj8ZRdYS",
      "execution_count": 16,
      "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": "9bd3592146b747948a906750ffe35d87"
            }
          },
          "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": "78364898fc6a404bb743daa93f1a64b6"
            }
          },
          "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 [04:18<00:00,  3.86it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ANS\n",
            "C    268\n",
            "B    253\n",
            "A    252\n",
            "D    227\n",
            "Name: count, dtype: int64\n",
            "Accuracy: 0.8710\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": [
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        "outputId": "4b26627d-36d1-4a4c-e965-8b4babeb9fff"
      },
      "id": "HrB5mDcf842y",
      "execution_count": 17,
      "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": "cdff65f549904d829f65f45b47c97ef3"
            }
          },
          "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": "ea1d6994d524408ba18356eb758c8b6d"
            }
          },
          "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 [06:43<00:00,  2.48it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ANS\n",
            "B    263\n",
            "C    255\n",
            "A    248\n",
            "D    234\n",
            "Name: count, dtype: int64\n",
            "Accuracy: 0.7870\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": "YI3SR_t1Vk2s",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a23742a1-2c75-47bc-f8bc-443b649a81a8"
      },
      "id": "YI3SR_t1Vk2s",
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BOOLQ ENGLISH :  0.8831804281345565\n",
            "BOOLQ HINDI   :  0.8522483940042827\n",
            "C-MCQ ENGLISH :  0.871\n",
            "C-MCQ HINDI   :  0.787\n",
            "MMMLU ENGLISH :  0.7652043868394816\n",
            "MMMLU HINDI   :  0.5789773536533257\n",
            "ARC-E ENGLISH :  0.9785353535353535\n",
            "ARC-E HINDI   :  0.9107744107744108\n",
            "ARC-C ENGLISH :  0.931740614334471\n",
            "ARC-C HINDI   :  0.8293515358361775\n",
            "AVG SCORE : HINDI   :  0.7916703388536395\n",
            "AVG SCORE : ENGLISH :  0.8859321565687726\n",
            "TOT AVG SCORE :  0.838801247711206\n",
            "CLICK CTRl+S and wait for 2 sec\n",
            "1) NOTEBOOK NAME SHOULD BE :  PHI-4-B50.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": [],
      "metadata": {
        "id": "ljShzEUtnaDH"
      },
      "id": "ljShzEUtnaDH",
      "execution_count": 18,
      "outputs": []
    }
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