diff --git "a/PHI_4_B40.ipynb" "b/PHI_4_B40.ipynb"
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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "0c24ca36-1782-4ce8-8094-6f6528dada19",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 17,
+ "referenced_widgets": [
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+ ]
+ },
+ "id": "0c24ca36-1782-4ce8-8094-6f6528dada19",
+ "outputId": "b65916f8-e6a8-42b5-fd77-6edb6d49ebe3"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
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+ "Downloading cut_cross_entropy-25.1.1-py3-none-any.whl (22 kB)\n",
+ "Downloading xxhash-3.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.8/194.8 kB\u001b[0m \u001b[31m16.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hInstalling collected packages: xxhash, shtab, protobuf, hf_transfer, fsspec, dill, multiprocess, tyro, xformers, datasets, cut_cross_entropy, bitsandbytes, trl, unsloth_zoo, unsloth\n",
+ " Attempting uninstall: protobuf\n",
+ " Found existing installation: protobuf 4.25.6\n",
+ " Uninstalling protobuf-4.25.6:\n",
+ " Successfully uninstalled protobuf-4.25.6\n",
+ " Attempting uninstall: fsspec\n",
+ " Found existing installation: fsspec 2024.10.0\n",
+ " Uninstalling fsspec-2024.10.0:\n",
+ " Successfully uninstalled fsspec-2024.10.0\n",
+ "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+ "grpcio-status 1.62.3 requires protobuf>=4.21.6, but you have protobuf 3.20.3 which is incompatible.\n",
+ "tensorflow-metadata 1.16.1 requires protobuf<6.0.0dev,>=4.25.2; python_version >= \"3.11\", but you have protobuf 3.20.3 which is incompatible.\n",
+ "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
+ "\u001b[0mSuccessfully installed bitsandbytes-0.45.1 cut_cross_entropy-25.1.1 datasets-3.2.0 dill-0.3.8 fsspec-2024.9.0 hf_transfer-0.1.9 multiprocess-0.70.16 protobuf-3.20.3 shtab-1.7.1 trl-0.13.0 tyro-0.9.13 unsloth-2025.1.6 unsloth_zoo-2025.1.5 xformers-0.0.29.post1 xxhash-3.5.0\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.colab-display-data+json": {
+ "pip_warning": {
+ "packages": [
+ "google"
+ ]
+ },
+ "id": "a6fa01fa61274498b3b20b05fc26a356"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Collecting git+https://github.com/unslothai/unsloth.git\n",
+ " Cloning https://github.com/unslothai/unsloth.git to /tmp/pip-req-build-x8rzc8cf\n",
+ " Running command git clone --filter=blob:none --quiet https://github.com/unslothai/unsloth.git /tmp/pip-req-build-x8rzc8cf\n",
+ " Resolved https://github.com/unslothai/unsloth.git to commit bdf0cd6033595be4e7ed23d0d002bb176d343152\n",
+ " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+ " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+ " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+ "Building wheels for collected packages: unsloth\n",
+ " Building wheel for unsloth (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+ " Created wheel for unsloth: filename=unsloth-2025.1.7-py3-none-any.whl size=174896 sha256=45a380b0ad1ecfc4224d614d81bf885af821fade2e2f4fc53da58ba8198d09ca\n",
+ " Stored in directory: /tmp/pip-ephem-wheel-cache-0ogxw7i6/wheels/d1/17/05/850ab10c33284a4763b0595cd8ea9d01fce6e221cac24b3c01\n",
+ "Successfully built unsloth\n",
+ "Installing collected packages: unsloth\n",
+ " Attempting uninstall: unsloth\n",
+ " Found existing installation: unsloth 2025.1.6\n",
+ " Uninstalling unsloth-2025.1.6:\n",
+ " Successfully uninstalled unsloth-2025.1.6\n",
+ "Successfully installed unsloth-2025.1.7\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "50190966-b741-47c4-915e-43c81fd1413a",
+ "metadata": {
+ "id": "50190966-b741-47c4-915e-43c81fd1413a",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "408ea58d-715f-4c09-d6be-fe259fa50a0d"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Requirement already satisfied: datasets in /usr/local/lib/python3.11/dist-packages (3.2.0)\n",
+ "Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (4.67.1)\n",
+ "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from datasets) (3.17.0)\n",
+ "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.11/dist-packages (from datasets) (1.26.4)\n",
+ "Requirement already satisfied: pyarrow>=15.0.0 in /usr/local/lib/python3.11/dist-packages (from datasets) (17.0.0)\n",
+ "Requirement already satisfied: dill<0.3.9,>=0.3.0 in /usr/local/lib/python3.11/dist-packages (from datasets) (0.3.8)\n",
+ "Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (from datasets) (2.2.2)\n",
+ "Requirement already satisfied: requests>=2.32.2 in /usr/local/lib/python3.11/dist-packages (from datasets) (2.32.3)\n",
+ "Requirement already satisfied: xxhash in /usr/local/lib/python3.11/dist-packages (from datasets) (3.5.0)\n",
+ "Requirement already satisfied: multiprocess<0.70.17 in /usr/local/lib/python3.11/dist-packages (from datasets) (0.70.16)\n",
+ "Requirement already satisfied: fsspec<=2024.9.0,>=2023.1.0 in /usr/local/lib/python3.11/dist-packages (from fsspec[http]<=2024.9.0,>=2023.1.0->datasets) (2024.9.0)\n",
+ "Requirement already satisfied: aiohttp in /usr/local/lib/python3.11/dist-packages (from datasets) (3.11.11)\n",
+ "Requirement already satisfied: huggingface-hub>=0.23.0 in /usr/local/lib/python3.11/dist-packages (from datasets) (0.27.1)\n",
+ "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from datasets) (24.2)\n",
+ "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.11/dist-packages (from datasets) (6.0.2)\n",
+ "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (2.4.4)\n",
+ "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (1.3.2)\n",
+ "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (24.3.0)\n",
+ "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (1.5.0)\n",
+ "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (6.1.0)\n",
+ "Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (0.2.1)\n",
+ "Requirement already satisfied: yarl<2.0,>=1.17.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (1.18.3)\n",
+ "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.11/dist-packages (from huggingface-hub>=0.23.0->datasets) (4.12.2)\n",
+ "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests>=2.32.2->datasets) (3.4.1)\n",
+ "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests>=2.32.2->datasets) (3.10)\n",
+ "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests>=2.32.2->datasets) (2.3.0)\n",
+ "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests>=2.32.2->datasets) (2024.12.14)\n",
+ "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas->datasets) (2.8.2)\n",
+ "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas->datasets) (2024.2)\n",
+ "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas->datasets) (2025.1)\n",
+ "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.17.0)\n",
+ "Requirement already satisfied: unsloth in /usr/local/lib/python3.11/dist-packages (2025.1.7)\n",
+ "Collecting git+https://github.com/unslothai/unsloth.git\n",
+ " Cloning https://github.com/unslothai/unsloth.git to /tmp/pip-req-build-1608rmeh\n",
+ " Running command git clone --filter=blob:none --quiet https://github.com/unslothai/unsloth.git /tmp/pip-req-build-1608rmeh\n",
+ " Resolved https://github.com/unslothai/unsloth.git to commit bdf0cd6033595be4e7ed23d0d002bb176d343152\n",
+ " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+ " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+ " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+ "Building wheels for collected packages: unsloth\n",
+ " Building wheel for unsloth (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+ " Created wheel for unsloth: filename=unsloth-2025.1.7-py3-none-any.whl size=174896 sha256=87dd520b64e6228f1fc6b01bc10a63919387ef51bfa62043ac48e5ef8574ece3\n",
+ " Stored in directory: /tmp/pip-ephem-wheel-cache-hu99o3ny/wheels/d1/17/05/850ab10c33284a4763b0595cd8ea9d01fce6e221cac24b3c01\n",
+ "Successfully built unsloth\n",
+ "Installing collected packages: unsloth\n",
+ " Attempting uninstall: unsloth\n",
+ " Found existing installation: unsloth 2025.1.7\n",
+ " Uninstalling unsloth-2025.1.7:\n",
+ " Successfully uninstalled unsloth-2025.1.7\n",
+ "Successfully installed unsloth-2025.1.7\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install datasets tqdm\n",
+ "!pip install unsloth\n",
+ "!pip install --force-reinstall --no-cache-dir --no-deps git+https://github.com/unslothai/unsloth.git"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "fbc9900d-28d2-4bda-9848-b572fbe778d2",
+ "metadata": {
+ "id": "fbc9900d-28d2-4bda-9848-b572fbe778d2",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000,
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+ "ef555d92f7344f9b805ef6bf6a3c6447"
+ ]
+ },
+ "outputId": "7c330a09-fc7d-46ed-fe30-a41ab41428bb"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
+ "🦥 Unsloth Zoo will now patch everything to make training faster!\n",
+ "==((====))== Unsloth 2025.1.7: Fast Llama patching. Transformers: 4.47.1.\n",
+ " \\\\ /| GPU: NVIDIA A100-SXM4-40GB. Max memory: 39.564 GB. Platform: Linux.\n",
+ "O^O/ \\_/ \\ Torch: 2.5.1+cu121. CUDA: 8.0. CUDA Toolkit: 12.1. Triton: 3.1.0\n",
+ "\\ / Bfloat16 = TRUE. FA [Xformers = 0.0.29.post1. FA2 = False]\n",
+ " \"-____-\" Free Apache license: http://github.com/unslothai/unsloth\n",
+ "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
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+ ],
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+ ],
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+ }
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+ },
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+ "output_type": "display_data",
+ "data": {
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+ ],
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+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "Unsloth 2025.1.7 patched 40 layers with 40 QKV layers, 40 O layers and 40 MLP layers.\n"
+ ]
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "PeftModelForCausalLM(\n",
+ " (base_model): LoraModel(\n",
+ " (model): LlamaForCausalLM(\n",
+ " (model): LlamaModel(\n",
+ " (embed_tokens): Embedding(100352, 5120, padding_idx=100351)\n",
+ " (layers): ModuleList(\n",
+ " (0-39): 40 x LlamaDecoderLayer(\n",
+ " (self_attn): LlamaAttention(\n",
+ " (q_proj): lora.Linear(\n",
+ " (base_layer): Linear(in_features=5120, out_features=5120, bias=False)\n",
+ " (lora_dropout): ModuleDict(\n",
+ " (default): Identity()\n",
+ " )\n",
+ " (lora_A): ModuleDict(\n",
+ " (default): Linear(in_features=5120, out_features=16, bias=False)\n",
+ " )\n",
+ " (lora_B): ModuleDict(\n",
+ " (default): Linear(in_features=16, out_features=5120, bias=False)\n",
+ " )\n",
+ " (lora_embedding_A): ParameterDict()\n",
+ " (lora_embedding_B): ParameterDict()\n",
+ " (lora_magnitude_vector): ModuleDict()\n",
+ " )\n",
+ " (k_proj): lora.Linear(\n",
+ " (base_layer): Linear(in_features=5120, out_features=1280, bias=False)\n",
+ " (lora_dropout): ModuleDict(\n",
+ " (default): Identity()\n",
+ " )\n",
+ " (lora_A): ModuleDict(\n",
+ " (default): Linear(in_features=5120, out_features=16, bias=False)\n",
+ " )\n",
+ " (lora_B): ModuleDict(\n",
+ " (default): Linear(in_features=16, out_features=1280, bias=False)\n",
+ " )\n",
+ " (lora_embedding_A): ParameterDict()\n",
+ " (lora_embedding_B): ParameterDict()\n",
+ " (lora_magnitude_vector): ModuleDict()\n",
+ " )\n",
+ " (v_proj): lora.Linear(\n",
+ " (base_layer): Linear(in_features=5120, out_features=1280, bias=False)\n",
+ " (lora_dropout): ModuleDict(\n",
+ " (default): Identity()\n",
+ " )\n",
+ " (lora_A): ModuleDict(\n",
+ " (default): Linear(in_features=5120, out_features=16, bias=False)\n",
+ " )\n",
+ " (lora_B): ModuleDict(\n",
+ " (default): Linear(in_features=16, out_features=1280, bias=False)\n",
+ " )\n",
+ " (lora_embedding_A): ParameterDict()\n",
+ " (lora_embedding_B): ParameterDict()\n",
+ " (lora_magnitude_vector): ModuleDict()\n",
+ " )\n",
+ " (o_proj): lora.Linear(\n",
+ " (base_layer): Linear(in_features=5120, out_features=5120, bias=False)\n",
+ " (lora_dropout): ModuleDict(\n",
+ " (default): Identity()\n",
+ " )\n",
+ " (lora_A): ModuleDict(\n",
+ " (default): Linear(in_features=5120, out_features=16, bias=False)\n",
+ " )\n",
+ " (lora_B): ModuleDict(\n",
+ " (default): Linear(in_features=16, out_features=5120, bias=False)\n",
+ " )\n",
+ " (lora_embedding_A): ParameterDict()\n",
+ " (lora_embedding_B): ParameterDict()\n",
+ " (lora_magnitude_vector): ModuleDict()\n",
+ " )\n",
+ " (rotary_emb): LlamaRotaryEmbedding()\n",
+ " )\n",
+ " (mlp): LlamaMLP(\n",
+ " (gate_proj): lora.Linear(\n",
+ " (base_layer): Linear(in_features=5120, out_features=17920, bias=False)\n",
+ " (lora_dropout): ModuleDict(\n",
+ " (default): Identity()\n",
+ " )\n",
+ " (lora_A): ModuleDict(\n",
+ " (default): Linear(in_features=5120, out_features=16, bias=False)\n",
+ " )\n",
+ " (lora_B): ModuleDict(\n",
+ " (default): Linear(in_features=16, out_features=17920, bias=False)\n",
+ " )\n",
+ " (lora_embedding_A): ParameterDict()\n",
+ " (lora_embedding_B): ParameterDict()\n",
+ " (lora_magnitude_vector): ModuleDict()\n",
+ " )\n",
+ " (up_proj): lora.Linear(\n",
+ " (base_layer): Linear(in_features=5120, out_features=17920, bias=False)\n",
+ " (lora_dropout): ModuleDict(\n",
+ " (default): Identity()\n",
+ " )\n",
+ " (lora_A): ModuleDict(\n",
+ " (default): Linear(in_features=5120, out_features=16, bias=False)\n",
+ " )\n",
+ " (lora_B): ModuleDict(\n",
+ " (default): Linear(in_features=16, out_features=17920, bias=False)\n",
+ " )\n",
+ " (lora_embedding_A): ParameterDict()\n",
+ " (lora_embedding_B): ParameterDict()\n",
+ " (lora_magnitude_vector): ModuleDict()\n",
+ " )\n",
+ " (down_proj): lora.Linear(\n",
+ " (base_layer): Linear(in_features=17920, out_features=5120, bias=False)\n",
+ " (lora_dropout): ModuleDict(\n",
+ " (default): Identity()\n",
+ " )\n",
+ " (lora_A): ModuleDict(\n",
+ " (default): Linear(in_features=17920, out_features=16, bias=False)\n",
+ " )\n",
+ " (lora_B): ModuleDict(\n",
+ " (default): Linear(in_features=16, out_features=5120, bias=False)\n",
+ " )\n",
+ " (lora_embedding_A): ParameterDict()\n",
+ " (lora_embedding_B): ParameterDict()\n",
+ " (lora_magnitude_vector): ModuleDict()\n",
+ " )\n",
+ " (act_fn): SiLU()\n",
+ " )\n",
+ " (input_layernorm): LlamaRMSNorm((5120,), eps=1e-05)\n",
+ " (post_attention_layernorm): LlamaRMSNorm((5120,), eps=1e-05)\n",
+ " )\n",
+ " )\n",
+ " (norm): LlamaRMSNorm((5120,), eps=1e-05)\n",
+ " (rotary_emb): LlamaRotaryEmbedding()\n",
+ " )\n",
+ " (lm_head): Linear(in_features=5120, out_features=100352, bias=False)\n",
+ " )\n",
+ " )\n",
+ ")"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 4
+ }
+ ],
+ "source": [
+ "from unsloth import FastLanguageModel\n",
+ "import pandas as pd\n",
+ "from datasets import load_dataset\n",
+ "import os\n",
+ "import torch\n",
+ "import torch.nn.functional as F\n",
+ "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
+ "from tqdm import tqdm\n",
+ "tqdm.pandas()\n",
+ "max_seq_length = 2048\n",
+ "load_in_4bit = False\n",
+ "name = \"1024m/PHI-4-B40\"\n",
+ "model, tokenizer = FastLanguageModel.from_pretrained(model_name = name, max_seq_length = max_seq_length, load_in_4bit = load_in_4bit,)\n",
+ "model = FastLanguageModel.get_peft_model( model, r = 16, target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\",], lora_alpha = 16, lora_dropout = 0, bias = \"none\", use_gradient_checkpointing = \"unsloth\", random_state = 3407, use_rslora = False, loftq_config = None,)\n",
+ "FastLanguageModel.for_inference(model)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "##**SINGLE TEST CASE**"
+ ],
+ "metadata": {
+ "id": "FICHwqm5aLUV"
+ },
+ "id": "FICHwqm5aLUV"
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "input_text = \"जोड़ें 46,911 + 653,092 ### A) 699,903 B) 700,003 C) 913,203 D) 1,122,202 ### MCQ ###\"\n",
+ "prompt = f\"### INPUT : {input_text} RESPONSE : \"\n",
+ "message = [{\"role\": \"user\", \"content\": prompt}]\n",
+ "inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n",
+ "outputs = model.generate(input_ids=inputs, max_new_tokens=200, use_cache=True, temperature=0.1, min_p=0.1, pad_token_id=tokenizer.eos_token_id)\n",
+ "response = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
+ "processed_response = response.split(\"### RESPONSE :\\nmodel\")[-1].strip()\n",
+ "print(f\"Generated Response (20 tokens):\\n{processed_response}\\n\")\n",
+ "with torch.no_grad():\n",
+ " outputs = model.generate(input_ids=inputs, max_new_tokens=3, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n",
+ " scores = outputs.scores\n",
+ "token_ids_a = tokenizer.encode('A', add_special_tokens=False)[0]\n",
+ "token_ids_b = tokenizer.encode('B', add_special_tokens=False)[0]\n",
+ "token_ids_c = tokenizer.encode('C', add_special_tokens=False)[0]\n",
+ "token_ids_d = tokenizer.encode('D', add_special_tokens=False)[0]\n",
+ "for i, score in enumerate(scores, 1):\n",
+ " probs = F.softmax(score, dim=-1)\n",
+ " prob_a = probs[0, token_ids_a].item()\n",
+ " prob_b = probs[0, token_ids_b].item()\n",
+ " prob_c = probs[0, token_ids_c].item()\n",
+ " prob_d = probs[0, token_ids_d].item()\n",
+ " print(f\"Probability of 'A' at token {i}: {prob_a:.4f}\")\n",
+ " print(f\"Probability of 'B' at token {i}: {prob_b:.4f}\")\n",
+ " print(f\"Probability of 'C' at token {i}: {prob_c:.4f}\")\n",
+ " print(f\"Probability of 'D' at token {i}: {prob_d:.4f}\")"
+ ],
+ "metadata": {
+ "id": "r1dozae-gO5B",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "f072b695-2824-4ea0-ae0e-9c89b2fd61fa"
+ },
+ "id": "r1dozae-gO5B",
+ "execution_count": 5,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Generated Response (20 tokens):\n",
+ "user### INPUT : जोड़ें 46,911 + 653,092 ### A) 699,903 B) 700,003 C) 913,203 D) 1,122,202 ### MCQ ### RESPONSE :assistantजोड़ने के लिए 46,911 और 653,092:\n",
+ "\n",
+ "```\n",
+ " 46,911\n",
+ "+653,092\n",
+ "---------\n",
+ " 700,003\n",
+ "```\n",
+ "\n",
+ "इसलिए, सही उत्तर है B) 700,003.\n",
+ "\n",
+ "Probability of 'A' at token 1: 0.0004\n",
+ "Probability of 'B' at token 1: 0.0002\n",
+ "Probability of 'C' at token 1: 0.0000\n",
+ "Probability of 'D' at token 1: 0.0001\n",
+ "Probability of 'A' at token 2: 0.0000\n",
+ "Probability of 'B' at token 2: 0.0000\n",
+ "Probability of 'C' at token 2: 0.0000\n",
+ "Probability of 'D' at token 2: 0.0000\n",
+ "Probability of 'A' at token 3: 0.0000\n",
+ "Probability of 'B' at token 3: 0.0000\n",
+ "Probability of 'C' at token 3: 0.0000\n",
+ "Probability of 'D' at token 3: 0.0000\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#**ARC CHALLENGE ENGLISH**"
+ ],
+ "metadata": {
+ "id": "7Al9PZfU2bhu"
+ },
+ "id": "7Al9PZfU2bhu"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "1749c745-d1fb-430b-9469-4913bb2a6cb5",
+ "metadata": {
+ "id": "1749c745-d1fb-430b-9469-4913bb2a6cb5",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 376,
+ "referenced_widgets": [
+ "df1fb9504d14452592cbf15434d2bcc7",
+ "439b7e33bfa6456c8c74c8b336c5bbf1",
+ "9dd65531453a431cb52d8a14912841e3",
+ "1eab052b7aee4ab79d19ea726d9db982",
+ "fba95c42e535442cb44401af023da530",
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+ "e66ce370de514e86bb705f9e958f7e02",
+ "17f16952ba9c41e2ad3b0f665152985e"
+ ]
+ },
+ "outputId": "73814751-662f-40ea-baef-23ae76e4c4de"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "ARC_Challenge_E.csv: 0%| | 0.00/367k [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "df1fb9504d14452592cbf15434d2bcc7"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Generating train split: 0 examples [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "ad9660960bf040dea70eb68604d6b0cb"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "1172\n",
+ "Average 'tok' value: 70.65870307167235\n",
+ "Max 'tok' value: 198\n",
+ "Output\n",
+ "B 311\n",
+ "C 310\n",
+ "D 285\n",
+ "A 266\n",
+ "Name: count, dtype: int64\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 1172/1172 [04:36<00:00, 4.23it/s]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "ANS\n",
+ "B 315\n",
+ "C 303\n",
+ "D 278\n",
+ "A 276\n",
+ "Name: count, dtype: int64\n",
+ "Accuracy: 0.9266\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "dataset = load_dataset(\"1-800-LLMs/Test-Collection\", data_files=\"ARC_Challenge_E.csv\", split=\"train\")\n",
+ "df = dataset.to_pandas()\n",
+ "print(len(df))\n",
+ "df['tok'] = df['Input'].apply(lambda x: len(tokenizer.encode(x)))\n",
+ "print(f\"Average 'tok' value: {df['tok'].mean()}\")\n",
+ "print(f\"Max 'tok' value: {df['tok'].max()}\")\n",
+ "df = df.sort_values('tok', ascending=False)\n",
+ "df['Output'] = df['Output'].replace({'1': 'A', '2': 'B', '3': 'C', '4': 'D'})\n",
+ "print(df['Output'].value_counts())\n",
+ "responses = []\n",
+ "prob_a1_list = []\n",
+ "prob_a2_list = []\n",
+ "prob_a3_list = []\n",
+ "prob_b1_list = []\n",
+ "prob_b2_list = []\n",
+ "prob_b3_list = []\n",
+ "prob_c1_list = []\n",
+ "prob_c2_list = []\n",
+ "prob_c3_list = []\n",
+ "prob_d1_list = []\n",
+ "prob_d2_list = []\n",
+ "prob_d3_list = []\n",
+ "batch_size = 1\n",
+ "for start in tqdm(range(0, len(df), batch_size)):\n",
+ " batch_texts = df['Input'][start:start+batch_size].tolist()\n",
+ " for input_text in batch_texts:\n",
+ " prompt = f\"### INPUT : {input_text} Respond with just one letter based on these options : \"\n",
+ " message = [{\"role\": \"user\", \"content\": prompt}]\n",
+ " inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n",
+ " with torch.no_grad():\n",
+ " outputs = model.generate(input_ids=inputs, max_new_tokens=3, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n",
+ " scores = outputs.scores # tuple of [batch_size, vocab_size] for each token\n",
+ " token_ids_a = tokenizer.encode('A', add_special_tokens=False)[0]\n",
+ " token_ids_b = tokenizer.encode('B', add_special_tokens=False)[0]\n",
+ " token_ids_c = tokenizer.encode('C', add_special_tokens=False)[0]\n",
+ " token_ids_d = tokenizer.encode('D', add_special_tokens=False)[0]\n",
+ " for i in range(3):\n",
+ " if i < len(scores):\n",
+ " probs = F.softmax(scores[i], dim=-1)\n",
+ " prob_a = probs[0, token_ids_a].item()\n",
+ " prob_b = probs[0, token_ids_b].item()\n",
+ " prob_c = probs[0, token_ids_c].item()\n",
+ " prob_d = probs[0, token_ids_d].item()\n",
+ " else:\n",
+ " prob_a, prob_b, prob_c, prob_d = 0.0, 0.0, 0.0, 0.0\n",
+ " if i == 0:\n",
+ " prob_a1_list.append(prob_a)\n",
+ " prob_b1_list.append(prob_b)\n",
+ " prob_c1_list.append(prob_c)\n",
+ " prob_d1_list.append(prob_d)\n",
+ " elif i == 1:\n",
+ " prob_a2_list.append(prob_a)\n",
+ " prob_b2_list.append(prob_b)\n",
+ " prob_c2_list.append(prob_c)\n",
+ " prob_d2_list.append(prob_d)\n",
+ " elif i == 2:\n",
+ " prob_a3_list.append(prob_a)\n",
+ " prob_b3_list.append(prob_b)\n",
+ " prob_c3_list.append(prob_c)\n",
+ " prob_d3_list.append(prob_d)\n",
+ "df['A1'] = prob_a1_list\n",
+ "df['A2'] = prob_a2_list\n",
+ "df['A3'] = prob_a3_list\n",
+ "df['B1'] = prob_b1_list\n",
+ "df['B2'] = prob_b2_list\n",
+ "df['B3'] = prob_b3_list\n",
+ "df['C1'] = prob_c1_list\n",
+ "df['C2'] = prob_c2_list\n",
+ "df['C3'] = prob_c3_list\n",
+ "df['D1'] = prob_d1_list\n",
+ "df['D2'] = prob_d2_list\n",
+ "df['D3'] = prob_d3_list\n",
+ "df['A'] = df['A1'] + df['A2'] + df['A3']\n",
+ "df['B'] = df['B1'] + df['B2'] + df['B3']\n",
+ "df['C'] = df['C1'] + df['C2'] + df['C3']\n",
+ "df['D'] = df['D1'] + df['D2'] + df['D3']\n",
+ "df['ANS'] = df[['A', 'B', 'C', 'D']].idxmax(axis=1)\n",
+ "print(df['ANS'].value_counts())\n",
+ "accuracy_arc_c_eng = (df['Output'] == df['ANS']).mean()\n",
+ "print(f\"Accuracy: {accuracy_arc_c_eng:.4f}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#**ARC CHALLENGE HINDI**"
+ ],
+ "metadata": {
+ "id": "PubN4p-32_EC"
+ },
+ "id": "PubN4p-32_EC"
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "dataset = load_dataset(\"1-800-LLMs/Test-Collection\", data_files=\"ARC_Challenge_H.csv\", split=\"train\")\n",
+ "df = dataset.to_pandas()\n",
+ "print(len(df))\n",
+ "df['tok'] = df['Input'].apply(lambda x: len(tokenizer.encode(x)))\n",
+ "print(f\"Average 'tok' value: {df['tok'].mean()}\")\n",
+ "print(f\"Max 'tok' value: {df['tok'].max()}\")\n",
+ "df = df.sort_values('tok', ascending=False)\n",
+ "df['Output'] = df['Output'].replace({'1': 'A', '2': 'B', '3': 'C', '4': 'D'})\n",
+ "print(df['Output'].value_counts())\n",
+ "responses = []\n",
+ "prob_a1_list = []\n",
+ "prob_a2_list = []\n",
+ "prob_a3_list = []\n",
+ "prob_b1_list = []\n",
+ "prob_b2_list = []\n",
+ "prob_b3_list = []\n",
+ "prob_c1_list = []\n",
+ "prob_c2_list = []\n",
+ "prob_c3_list = []\n",
+ "prob_d1_list = []\n",
+ "prob_d2_list = []\n",
+ "prob_d3_list = []\n",
+ "batch_size = 1\n",
+ "for start in tqdm(range(0, len(df), batch_size)):\n",
+ " batch_texts = df['Input'][start:start+batch_size].tolist()\n",
+ " for input_text in batch_texts:\n",
+ " prompt = f\"### INPUT : {input_text} Respond with just one letter based on these options : \"\n",
+ " message = [{\"role\": \"user\", \"content\": prompt}]\n",
+ " inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n",
+ " with torch.no_grad():\n",
+ " outputs = model.generate(input_ids=inputs, max_new_tokens=3, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n",
+ " scores = outputs.scores # tuple of [batch_size, vocab_size] for each token\n",
+ " token_ids_a = tokenizer.encode('A', add_special_tokens=False)[0]\n",
+ " token_ids_b = tokenizer.encode('B', add_special_tokens=False)[0]\n",
+ " token_ids_c = tokenizer.encode('C', add_special_tokens=False)[0]\n",
+ " token_ids_d = tokenizer.encode('D', add_special_tokens=False)[0]\n",
+ " for i in range(3):\n",
+ " if i < len(scores):\n",
+ " probs = F.softmax(scores[i], dim=-1)\n",
+ " prob_a = probs[0, token_ids_a].item()\n",
+ " prob_b = probs[0, token_ids_b].item()\n",
+ " prob_c = probs[0, token_ids_c].item()\n",
+ " prob_d = probs[0, token_ids_d].item()\n",
+ " else:\n",
+ " prob_a, prob_b, prob_c, prob_d = 0.0, 0.0, 0.0, 0.0\n",
+ " if i == 0:\n",
+ " prob_a1_list.append(prob_a)\n",
+ " prob_b1_list.append(prob_b)\n",
+ " prob_c1_list.append(prob_c)\n",
+ " prob_d1_list.append(prob_d)\n",
+ " elif i == 1:\n",
+ " prob_a2_list.append(prob_a)\n",
+ " prob_b2_list.append(prob_b)\n",
+ " prob_c2_list.append(prob_c)\n",
+ " prob_d2_list.append(prob_d)\n",
+ " elif i == 2:\n",
+ " prob_a3_list.append(prob_a)\n",
+ " prob_b3_list.append(prob_b)\n",
+ " prob_c3_list.append(prob_c)\n",
+ " prob_d3_list.append(prob_d)\n",
+ "df['A1'] = prob_a1_list\n",
+ "df['A2'] = prob_a2_list\n",
+ "df['A3'] = prob_a3_list\n",
+ "df['B1'] = prob_b1_list\n",
+ "df['B2'] = prob_b2_list\n",
+ "df['B3'] = prob_b3_list\n",
+ "df['C1'] = prob_c1_list\n",
+ "df['C2'] = prob_c2_list\n",
+ "df['C3'] = prob_c3_list\n",
+ "df['D1'] = prob_d1_list\n",
+ "df['D2'] = prob_d2_list\n",
+ "df['D3'] = prob_d3_list\n",
+ "df['A'] = df['A1'] + df['A2'] + df['A3']\n",
+ "df['B'] = df['B1'] + df['B2'] + df['B3']\n",
+ "df['C'] = df['C1'] + df['C2'] + df['C3']\n",
+ "df['D'] = df['D1'] + df['D2'] + df['D3']\n",
+ "df['ANS'] = df[['A', 'B', 'C', 'D']].idxmax(axis=1)\n",
+ "print(df['ANS'].value_counts())\n",
+ "accuracy_arc_c_hin = (df['Output'] == df['ANS']).mean()\n",
+ "print(f\"Accuracy: {accuracy_arc_c_hin:.4f}\")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 376,
+ "referenced_widgets": [
+ "e67b27d2113c4f3c9f55c144e2953550",
+ "61de01b154fd42fd891946e4f57ef062",
+ "c8605e5555194411a861471e9a79a8dc",
+ "44a45ce71f2a4fe083f9de004c8b2e37",
+ "6fec2129351c4b0da653622894661fbe",
+ "aee433f3ad7e413a849e87dd160cd6c2",
+ "b750082a1cc543bd9d6bdc5efce2ab6d",
+ "c0332da7d3484f2180affaa56fd0c1c3",
+ "93a374326f024fc9baa4031b2beee515",
+ "740f1c0e77ce4c6a81d2070e4cdba3d2",
+ "0bfaff7a38c64724be71d7e8e27943e7",
+ "832ea8ddf5e34aa1a41a0c45cb7b54aa",
+ "b417a2f4269f4b26845bb51c3c55c5df",
+ "819915d795c2442283743ccdb98efa9d",
+ "13954dc7ec4d4b0dad204301fbbb188b",
+ "fa4cf95db7fa443c8e3d5e788dd4e071",
+ "35c062f4fa02494eb3f277925fe1254f",
+ "85cca05edb244ff281a4fc17d337fd55",
+ "b9c3c58ffb31449b98f28a9ac630583f",
+ "16ca6a100d564b458fc4b4f132374cf2",
+ "1094ebc9aebd45df9f64619cdc36e6bf",
+ "3d59e7a488074a5ebc82c502967362ce"
+ ]
+ },
+ "id": "mPFAiosJ3jzD",
+ "outputId": "7ac10110-9109-4f2a-bfe4-b5382c0f8ddc"
+ },
+ "id": "mPFAiosJ3jzD",
+ "execution_count": 7,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "ARC_Challenge_H.csv: 0%| | 0.00/822k [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "e67b27d2113c4f3c9f55c144e2953550"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Generating train split: 0 examples [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "832ea8ddf5e34aa1a41a0c45cb7b54aa"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "1172\n",
+ "Average 'tok' value: 264.9129692832764\n",
+ "Max 'tok' value: 957\n",
+ "Output\n",
+ "B 311\n",
+ "C 310\n",
+ "D 285\n",
+ "A 266\n",
+ "Name: count, dtype: int64\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 1172/1172 [04:58<00:00, 3.93it/s]\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "ANS\n",
+ "B 329\n",
+ "C 303\n",
+ "A 291\n",
+ "D 249\n",
+ "Name: count, dtype: int64\n",
+ "Accuracy: 0.8225\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#**ARC EASY ENGLISH**"
+ ],
+ "metadata": {
+ "id": "cT9I3npw43AP"
+ },
+ "id": "cT9I3npw43AP"
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "dataset = load_dataset(\"1-800-LLMs/Test-Collection\", data_files=\"ARC_Easy_E.csv\", split=\"train\")\n",
+ "df = dataset.to_pandas()\n",
+ "print(len(df))\n",
+ "df['tok'] = df['Input'].apply(lambda x: len(tokenizer.encode(x)))\n",
+ "print(f\"Average 'tok' value: {df['tok'].mean()}\")\n",
+ "print(f\"Max 'tok' value: {df['tok'].max()}\")\n",
+ "df = df.sort_values('tok', ascending=False)\n",
+ "df['Output'] = df['Output'].replace({'1': 'A', '2': 'B', '3': 'C', '4': 'D'})\n",
+ "print(df['Output'].value_counts())\n",
+ "responses = []\n",
+ "prob_a1_list = []\n",
+ "prob_a2_list = []\n",
+ "prob_a3_list = []\n",
+ "prob_b1_list = []\n",
+ "prob_b2_list = []\n",
+ "prob_b3_list = []\n",
+ "prob_c1_list = []\n",
+ "prob_c2_list = []\n",
+ "prob_c3_list = []\n",
+ "prob_d1_list = []\n",
+ "prob_d2_list = []\n",
+ "prob_d3_list = []\n",
+ "batch_size = 1\n",
+ "for start in tqdm(range(0, len(df), batch_size)):\n",
+ " batch_texts = df['Input'][start:start+batch_size].tolist()\n",
+ " for input_text in batch_texts:\n",
+ " prompt = f\"### INPUT : {input_text} Respond with just one letter based on these options : \"\n",
+ " message = [{\"role\": \"user\", \"content\": prompt}]\n",
+ " inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n",
+ " with torch.no_grad():\n",
+ " outputs = model.generate(input_ids=inputs, max_new_tokens=3, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n",
+ " scores = outputs.scores # tuple of [batch_size, vocab_size] for each token\n",
+ " token_ids_a = tokenizer.encode('A', add_special_tokens=False)[0]\n",
+ " token_ids_b = tokenizer.encode('B', add_special_tokens=False)[0]\n",
+ " token_ids_c = tokenizer.encode('C', add_special_tokens=False)[0]\n",
+ " token_ids_d = tokenizer.encode('D', add_special_tokens=False)[0]\n",
+ " for i in range(3):\n",
+ " if i < len(scores):\n",
+ " probs = F.softmax(scores[i], dim=-1)\n",
+ " prob_a = probs[0, token_ids_a].item()\n",
+ " prob_b = probs[0, token_ids_b].item()\n",
+ " prob_c = probs[0, token_ids_c].item()\n",
+ " prob_d = probs[0, token_ids_d].item()\n",
+ " else:\n",
+ " prob_a, prob_b, prob_c, prob_d = 0.0, 0.0, 0.0, 0.0\n",
+ " if i == 0:\n",
+ " prob_a1_list.append(prob_a)\n",
+ " prob_b1_list.append(prob_b)\n",
+ " prob_c1_list.append(prob_c)\n",
+ " prob_d1_list.append(prob_d)\n",
+ " elif i == 1:\n",
+ " prob_a2_list.append(prob_a)\n",
+ " prob_b2_list.append(prob_b)\n",
+ " prob_c2_list.append(prob_c)\n",
+ " prob_d2_list.append(prob_d)\n",
+ " elif i == 2:\n",
+ " prob_a3_list.append(prob_a)\n",
+ " prob_b3_list.append(prob_b)\n",
+ " prob_c3_list.append(prob_c)\n",
+ " prob_d3_list.append(prob_d)\n",
+ "df['A1'] = prob_a1_list\n",
+ "df['A2'] = prob_a2_list\n",
+ "df['A3'] = prob_a3_list\n",
+ "df['B1'] = prob_b1_list\n",
+ "df['B2'] = prob_b2_list\n",
+ "df['B3'] = prob_b3_list\n",
+ "df['C1'] = prob_c1_list\n",
+ "df['C2'] = prob_c2_list\n",
+ "df['C3'] = prob_c3_list\n",
+ "df['D1'] = prob_d1_list\n",
+ "df['D2'] = prob_d2_list\n",
+ "df['D3'] = prob_d3_list\n",
+ "df['A'] = df['A1'] + df['A2'] + df['A3']\n",
+ "df['B'] = df['B1'] + df['B2'] + df['B3']\n",
+ "df['C'] = df['C1'] + df['C2'] + df['C3']\n",
+ "df['D'] = df['D1'] + df['D2'] + df['D3']\n",
+ "df['ANS'] = df[['A', 'B', 'C', 'D']].idxmax(axis=1)\n",
+ "print(df['ANS'].value_counts())\n",
+ "accuracy_arc_e_eng = (df['Output'] == df['ANS']).mean()\n",
+ "print(f\"Accuracy: {accuracy_arc_e_eng:.4f}\")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 393,
+ "referenced_widgets": [
+ "e825dcfbe3aa478bbe61387314173e7e",
+ "047da05da6bb4f77bb17aa1b735559a9",
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+ "d4690579650444c2b249820e30ccf614",
+ "1c57b673a6574ad0b3f700a64a82f05a",
+ "b2d30e3cff21468faa47985819a7460d",
+ "a3132eccb78c416881af63e18f5421f5",
+ "edf765127ac541c3b51505f19e45e972",
+ "24633b0490a64f6a956038a9e6c0bf9a",
+ "6c78b7a26f844040b122902d16912db5",
+ "bda23c868fc74904970d75d6855a2b7b",
+ "08254691ddec458c8ae406999b1f43a0",
+ "5bc8f98d5c1547b7bb749dfddecc1ca5",
+ "86323c6783cb431c83f9e3fc5cd6ce37"
+ ]
+ },
+ "id": "6vmG3Z92410E",
+ "outputId": "2c0f2a2a-8eeb-41df-ca32-8ca31c78e2e9"
+ },
+ "id": "6vmG3Z92410E",
+ "execution_count": 8,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "ARC_Easy_E.csv: 0%| | 0.00/627k [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "e825dcfbe3aa478bbe61387314173e7e"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Generating train split: 0 examples [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "d4690579650444c2b249820e30ccf614"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "2376\n",
+ "Average 'tok' value: 61.25968013468013\n",
+ "Max 'tok' value: 197\n",
+ "Output\n",
+ "C 633\n",
+ "A 596\n",
+ "B 585\n",
+ "D 561\n",
+ "E 1\n",
+ "Name: count, dtype: int64\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 2376/2376 [09:01<00:00, 4.39it/s]\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "ANS\n",
+ "C 621\n",
+ "A 598\n",
+ "B 593\n",
+ "D 564\n",
+ "Name: count, dtype: int64\n",
+ "Accuracy: 0.9777\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#**ARC EASY HINDI**"
+ ],
+ "metadata": {
+ "id": "A5dtJYX05T5v"
+ },
+ "id": "A5dtJYX05T5v"
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "dataset = load_dataset(\"1-800-LLMs/Test-Collection\", data_files=\"ARC_Easy_H.csv\", split=\"train\")\n",
+ "df = dataset.to_pandas()\n",
+ "print(len(df))\n",
+ "df['tok'] = df['Input'].apply(lambda x: len(tokenizer.encode(x)))\n",
+ "print(f\"Average 'tok' value: {df['tok'].mean()}\")\n",
+ "print(f\"Max 'tok' value: {df['tok'].max()}\")\n",
+ "df = df.sort_values('tok', ascending=False)\n",
+ "print(df['Output'].value_counts())\n",
+ "df['Output'] = df['Output'].replace({'1': 'A', '2': 'B', '3': 'C', '4': 'D'})\n",
+ "responses = []\n",
+ "prob_a1_list = []\n",
+ "prob_a2_list = []\n",
+ "prob_a3_list = []\n",
+ "prob_b1_list = []\n",
+ "prob_b2_list = []\n",
+ "prob_b3_list = []\n",
+ "prob_c1_list = []\n",
+ "prob_c2_list = []\n",
+ "prob_c3_list = []\n",
+ "prob_d1_list = []\n",
+ "prob_d2_list = []\n",
+ "prob_d3_list = []\n",
+ "batch_size = 1\n",
+ "for start in tqdm(range(0, len(df), batch_size)):\n",
+ " batch_texts = df['Input'][start:start+batch_size].tolist()\n",
+ " for input_text in batch_texts:\n",
+ " prompt = f\"### INPUT : {input_text} Respond with just one letter based on these options : \"\n",
+ " message = [{\"role\": \"user\", \"content\": prompt}]\n",
+ " inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n",
+ " with torch.no_grad():\n",
+ " outputs = model.generate(input_ids=inputs, max_new_tokens=3, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n",
+ " scores = outputs.scores # tuple of [batch_size, vocab_size] for each token\n",
+ " token_ids_a = tokenizer.encode('A', add_special_tokens=False)[0]\n",
+ " token_ids_b = tokenizer.encode('B', add_special_tokens=False)[0]\n",
+ " token_ids_c = tokenizer.encode('C', add_special_tokens=False)[0]\n",
+ " token_ids_d = tokenizer.encode('D', add_special_tokens=False)[0]\n",
+ " for i in range(3):\n",
+ " if i < len(scores):\n",
+ " probs = F.softmax(scores[i], dim=-1)\n",
+ " prob_a = probs[0, token_ids_a].item()\n",
+ " prob_b = probs[0, token_ids_b].item()\n",
+ " prob_c = probs[0, token_ids_c].item()\n",
+ " prob_d = probs[0, token_ids_d].item()\n",
+ " else:\n",
+ " prob_a, prob_b, prob_c, prob_d = 0.0, 0.0, 0.0, 0.0\n",
+ " if i == 0:\n",
+ " prob_a1_list.append(prob_a)\n",
+ " prob_b1_list.append(prob_b)\n",
+ " prob_c1_list.append(prob_c)\n",
+ " prob_d1_list.append(prob_d)\n",
+ " elif i == 1:\n",
+ " prob_a2_list.append(prob_a)\n",
+ " prob_b2_list.append(prob_b)\n",
+ " prob_c2_list.append(prob_c)\n",
+ " prob_d2_list.append(prob_d)\n",
+ " elif i == 2:\n",
+ " prob_a3_list.append(prob_a)\n",
+ " prob_b3_list.append(prob_b)\n",
+ " prob_c3_list.append(prob_c)\n",
+ " prob_d3_list.append(prob_d)\n",
+ "df['A1'] = prob_a1_list\n",
+ "df['A2'] = prob_a2_list\n",
+ "df['A3'] = prob_a3_list\n",
+ "df['B1'] = prob_b1_list\n",
+ "df['B2'] = prob_b2_list\n",
+ "df['B3'] = prob_b3_list\n",
+ "df['C1'] = prob_c1_list\n",
+ "df['C2'] = prob_c2_list\n",
+ "df['C3'] = prob_c3_list\n",
+ "df['D1'] = prob_d1_list\n",
+ "df['D2'] = prob_d2_list\n",
+ "df['D3'] = prob_d3_list\n",
+ "df['A'] = df['A1'] + df['A2'] + df['A3']\n",
+ "df['B'] = df['B1'] + df['B2'] + df['B3']\n",
+ "df['C'] = df['C1'] + df['C2'] + df['C3']\n",
+ "df['D'] = df['D1'] + df['D2'] + df['D3']\n",
+ "df['ANS'] = df[['A', 'B', 'C', 'D']].idxmax(axis=1)\n",
+ "print(df['ANS'].value_counts())\n",
+ "accuracy_arc_e_hin = (df['Output'] == df['ANS']).mean()\n",
+ "print(f\"Accuracy: {accuracy_arc_e_hin:.4f}\")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 463,
+ "referenced_widgets": [
+ "b6acffad53b949c9b8b2c05b2f65ac1d",
+ "b70a6af2e43e40e69923dbc065a3bcb4",
+ "fd6bda1349bf4e34ab65e215b27e237a",
+ "a14e94ca6fc74725a3a56bc547b07a17",
+ "13791a5b50574759993068d0a5950149",
+ "2495ab4cb9e0426597ec43bd21b106bf",
+ "90ee545ac5e14bfd80e81f745424192a",
+ "ba4f11948a2140fc9f127863ef84a270",
+ "112adbbd804e4da9a463e25c1242346d",
+ "b62f7b2f63114f7ca04121c8212d6e49",
+ "578381c0e74e4f49b1622f48ed3c1db3",
+ "a5481b05e34e4dca8b2f4bbd4067d0ef",
+ "4ef8dd30106a49568463bfaa5cf8d2d6",
+ "84bbe66a0ec44e3db96926f382e99c74",
+ "a56a77b2a8e44c2b95573c127b9c010d",
+ "66ecfccca6fd4a9aa0d15a040740ba66",
+ "8738fdbfc3a9415a984ccc2a75243ce6",
+ "1f1b8612300942629d8885fad93a3e41",
+ "34f23a7ce77f46b3a9c207ea55753257",
+ "08c3a730a87245debfe8f0e61eda3e6a",
+ "4592901898d14001972fd748b4488cf6",
+ "8eb2f551c26345268a170f7bdf51f599"
+ ]
+ },
+ "id": "aFPK7wPX5TN7",
+ "outputId": "d498fd1c-edd9-455a-9677-177472907c15"
+ },
+ "id": "aFPK7wPX5TN7",
+ "execution_count": 9,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "ARC_Easy_H.csv: 0%| | 0.00/1.41M [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "b6acffad53b949c9b8b2c05b2f65ac1d"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Generating train split: 0 examples [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "a5481b05e34e4dca8b2f4bbd4067d0ef"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "2376\n",
+ "Average 'tok' value: 224.7478956228956\n",
+ "Max 'tok' value: 963\n",
+ "Output\n",
+ "C 610\n",
+ "A 570\n",
+ "B 563\n",
+ "D 535\n",
+ "4 26\n",
+ "1 26\n",
+ "3 23\n",
+ "2 22\n",
+ "E 1\n",
+ "Name: count, dtype: int64\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 2376/2376 [10:03<00:00, 3.94it/s]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "ANS\n",
+ "A 632\n",
+ "B 616\n",
+ "C 604\n",
+ "D 524\n",
+ "Name: count, dtype: int64\n",
+ "Accuracy: 0.9036\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#**MMLU ENGLISH**"
+ ],
+ "metadata": {
+ "id": "pFRbbDbE4Qui"
+ },
+ "id": "pFRbbDbE4Qui"
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "dataset = load_dataset(\"1-800-LLMs/Test-Collection\", data_files=\"MMMLU_E.csv\", split=\"train\")\n",
+ "df = dataset.to_pandas()\n",
+ "print(len(df))\n",
+ "df['tok'] = df['Input'].apply(lambda x: len(tokenizer.encode(x)))\n",
+ "print(f\"Average 'tok' value: {df['tok'].mean()}\")\n",
+ "print(f\"Max 'tok' value: {df['tok'].max()}\")\n",
+ "df = df.sort_values('tok', ascending=False)\n",
+ "print(df['Output'].value_counts())\n",
+ "responses = []\n",
+ "prob_a1_list = []\n",
+ "prob_a2_list = []\n",
+ "prob_a3_list = []\n",
+ "prob_b1_list = []\n",
+ "prob_b2_list = []\n",
+ "prob_b3_list = []\n",
+ "prob_c1_list = []\n",
+ "prob_c2_list = []\n",
+ "prob_c3_list = []\n",
+ "prob_d1_list = []\n",
+ "prob_d2_list = []\n",
+ "prob_d3_list = []\n",
+ "batch_size = 1\n",
+ "for start in tqdm(range(0, len(df), batch_size)):\n",
+ " batch_texts = df['Input'][start:start+batch_size].tolist()\n",
+ " for input_text in batch_texts:\n",
+ " prompt = f\"### INPUT : {input_text} Respond with just one letter based on these options : \"\n",
+ " message = [{\"role\": \"user\", \"content\": prompt}]\n",
+ " inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n",
+ " with torch.no_grad():\n",
+ " outputs = model.generate(input_ids=inputs, max_new_tokens=3, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n",
+ " scores = outputs.scores # tuple of [batch_size, vocab_size] for each token\n",
+ " token_ids_a = tokenizer.encode('A', add_special_tokens=False)[0]\n",
+ " token_ids_b = tokenizer.encode('B', add_special_tokens=False)[0]\n",
+ " token_ids_c = tokenizer.encode('C', add_special_tokens=False)[0]\n",
+ " token_ids_d = tokenizer.encode('D', add_special_tokens=False)[0]\n",
+ " for i in range(3):\n",
+ " if i < len(scores):\n",
+ " probs = F.softmax(scores[i], dim=-1)\n",
+ " prob_a = probs[0, token_ids_a].item()\n",
+ " prob_b = probs[0, token_ids_b].item()\n",
+ " prob_c = probs[0, token_ids_c].item()\n",
+ " prob_d = probs[0, token_ids_d].item()\n",
+ " else:\n",
+ " prob_a, prob_b, prob_c, prob_d = 0.0, 0.0, 0.0, 0.0\n",
+ " if i == 0:\n",
+ " prob_a1_list.append(prob_a)\n",
+ " prob_b1_list.append(prob_b)\n",
+ " prob_c1_list.append(prob_c)\n",
+ " prob_d1_list.append(prob_d)\n",
+ " elif i == 1:\n",
+ " prob_a2_list.append(prob_a)\n",
+ " prob_b2_list.append(prob_b)\n",
+ " prob_c2_list.append(prob_c)\n",
+ " prob_d2_list.append(prob_d)\n",
+ " elif i == 2:\n",
+ " prob_a3_list.append(prob_a)\n",
+ " prob_b3_list.append(prob_b)\n",
+ " prob_c3_list.append(prob_c)\n",
+ " prob_d3_list.append(prob_d)\n",
+ "df['A1'] = prob_a1_list\n",
+ "df['A2'] = prob_a2_list\n",
+ "df['A3'] = prob_a3_list\n",
+ "df['B1'] = prob_b1_list\n",
+ "df['B2'] = prob_b2_list\n",
+ "df['B3'] = prob_b3_list\n",
+ "df['C1'] = prob_c1_list\n",
+ "df['C2'] = prob_c2_list\n",
+ "df['C3'] = prob_c3_list\n",
+ "df['D1'] = prob_d1_list\n",
+ "df['D2'] = prob_d2_list\n",
+ "df['D3'] = prob_d3_list\n",
+ "df['A'] = df['A1'] + df['A2'] + df['A3']\n",
+ "df['B'] = df['B1'] + df['B2'] + df['B3']\n",
+ "df['C'] = df['C1'] + df['C2'] + df['C3']\n",
+ "df['D'] = df['D1'] + df['D2'] + df['D3']\n",
+ "df['ANS'] = df[['A', 'B', 'C', 'D']].idxmax(axis=1)\n",
+ "print(df['ANS'].value_counts())\n",
+ "accuracy_mmmlu_eng = (df['Output'] == df['ANS']).mean()\n",
+ "print(f\"Accuracy: {accuracy_mmmlu_eng:.4f}\")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 376,
+ "referenced_widgets": [
+ "3d0291d10fe14190b35db3b04b623d60",
+ "d42de3a5998e4a44944e9386588e1969",
+ "0ee5850c744841359b3c33bb47146c56",
+ "f5988fcbdaec436a9513f1168aa74189",
+ "b964405007364feb8266d471df6f84c9",
+ "550ad34eb7ee468b9300ecbad92736b8",
+ "b6ccaaa3882a4ea3afc855da58cc60a8",
+ "3532bde0e63e4e62970486fbaed99c1a",
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+ "2587772c418d425d8ddddd001df5f066",
+ "ff29743075bd48e993a81b5af4d089da",
+ "10597fdac1c648d1a9cf62860e4a2b5c",
+ "544c6afce9024fd395ac6ab79a251a1e",
+ "03da2a33667b4ed189199999993e1b30",
+ "655b983deea84118858d89d6fe62170d",
+ "84c5aabe7a674165babe86825dfb530c",
+ "36d845a630ee48af8b3b7836f518e885",
+ "4974ba564a8e438bb23d46929fd6c332",
+ "d900b22d23ce41889d478de4aafc6a1d",
+ "bb148b9aeffd4d7e8b995c4a535c1b03",
+ "e977226e69d14d3da3ac70c5bc7c515a",
+ "264593d733a24a8688c7805dc110acc5"
+ ]
+ },
+ "id": "FtThThQC8hs2",
+ "outputId": "feed1f45-0996-4ff6-b9c8-623841668515"
+ },
+ "id": "FtThThQC8hs2",
+ "execution_count": 10,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "MMMLU_E.csv: 0%| | 0.00/7.07M [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "3d0291d10fe14190b35db3b04b623d60"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Generating train split: 0 examples [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "10597fdac1c648d1a9cf62860e4a2b5c"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "14042\n",
+ "Average 'tok' value: 106.00548354935195\n",
+ "Max 'tok' value: 974\n",
+ "Output\n",
+ "D 3776\n",
+ "C 3582\n",
+ "B 3462\n",
+ "A 3222\n",
+ "Name: count, dtype: int64\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 14042/14042 [59:20<00:00, 3.94it/s]\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "ANS\n",
+ "B 3854\n",
+ "C 3616\n",
+ "D 3301\n",
+ "A 3271\n",
+ "Name: count, dtype: int64\n",
+ "Accuracy: 0.7587\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#**MMMLU HINDI**"
+ ],
+ "metadata": {
+ "id": "FeK3WGqS85al"
+ },
+ "id": "FeK3WGqS85al"
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "dataset = load_dataset(\"1-800-LLMs/Test-Collection\", data_files=\"MMMLU_H.csv\", split=\"train\")\n",
+ "df = dataset.to_pandas()\n",
+ "print(len(df))\n",
+ "df['tok'] = df['Input'].apply(lambda x: len(tokenizer.encode(x)))\n",
+ "print(f\"Average 'tok' value: {df['tok'].mean()}\")\n",
+ "print(f\"Max 'tok' value: {df['tok'].max()}\")\n",
+ "df = df.sort_values('tok', ascending=False)\n",
+ "print(df['Output'].value_counts())\n",
+ "responses = []\n",
+ "prob_a1_list = []\n",
+ "prob_a2_list = []\n",
+ "prob_a3_list = []\n",
+ "prob_b1_list = []\n",
+ "prob_b2_list = []\n",
+ "prob_b3_list = []\n",
+ "prob_c1_list = []\n",
+ "prob_c2_list = []\n",
+ "prob_c3_list = []\n",
+ "prob_d1_list = []\n",
+ "prob_d2_list = []\n",
+ "prob_d3_list = []\n",
+ "batch_size = 1\n",
+ "for start in tqdm(range(0, len(df), batch_size)):\n",
+ " batch_texts = df['Input'][start:start+batch_size].tolist()\n",
+ " for input_text in batch_texts:\n",
+ " prompt = f\"### INPUT : {input_text} Respond with just one letter based on these options : \"\n",
+ " message = [{\"role\": \"user\", \"content\": prompt}]\n",
+ " inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n",
+ " with torch.no_grad():\n",
+ " outputs = model.generate(input_ids=inputs, max_new_tokens=3, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n",
+ " scores = outputs.scores # tuple of [batch_size, vocab_size] for each token\n",
+ " token_ids_a = tokenizer.encode('A', add_special_tokens=False)[0]\n",
+ " token_ids_b = tokenizer.encode('B', add_special_tokens=False)[0]\n",
+ " token_ids_c = tokenizer.encode('C', add_special_tokens=False)[0]\n",
+ " token_ids_d = tokenizer.encode('D', add_special_tokens=False)[0]\n",
+ " for i in range(3):\n",
+ " if i < len(scores):\n",
+ " probs = F.softmax(scores[i], dim=-1)\n",
+ " prob_a = probs[0, token_ids_a].item()\n",
+ " prob_b = probs[0, token_ids_b].item()\n",
+ " prob_c = probs[0, token_ids_c].item()\n",
+ " prob_d = probs[0, token_ids_d].item()\n",
+ " else:\n",
+ " prob_a, prob_b, prob_c, prob_d = 0.0, 0.0, 0.0, 0.0\n",
+ " if i == 0:\n",
+ " prob_a1_list.append(prob_a)\n",
+ " prob_b1_list.append(prob_b)\n",
+ " prob_c1_list.append(prob_c)\n",
+ " prob_d1_list.append(prob_d)\n",
+ " elif i == 1:\n",
+ " prob_a2_list.append(prob_a)\n",
+ " prob_b2_list.append(prob_b)\n",
+ " prob_c2_list.append(prob_c)\n",
+ " prob_d2_list.append(prob_d)\n",
+ " elif i == 2:\n",
+ " prob_a3_list.append(prob_a)\n",
+ " prob_b3_list.append(prob_b)\n",
+ " prob_c3_list.append(prob_c)\n",
+ " prob_d3_list.append(prob_d)\n",
+ "df['A1'] = prob_a1_list\n",
+ "df['A2'] = prob_a2_list\n",
+ "df['A3'] = prob_a3_list\n",
+ "df['B1'] = prob_b1_list\n",
+ "df['B2'] = prob_b2_list\n",
+ "df['B3'] = prob_b3_list\n",
+ "df['C1'] = prob_c1_list\n",
+ "df['C2'] = prob_c2_list\n",
+ "df['C3'] = prob_c3_list\n",
+ "df['D1'] = prob_d1_list\n",
+ "df['D2'] = prob_d2_list\n",
+ "df['D3'] = prob_d3_list\n",
+ "df['A'] = df['A1'] + df['A2'] + df['A3']\n",
+ "df['B'] = df['B1'] + df['B2'] + df['B3']\n",
+ "df['C'] = df['C1'] + df['C2'] + df['C3']\n",
+ "df['D'] = df['D1'] + df['D2'] + df['D3']\n",
+ "df['ANS'] = df[['A', 'B', 'C', 'D']].idxmax(axis=1)\n",
+ "print(df['ANS'].value_counts())\n",
+ "accuracy_mmmlu_hin = (df['Output'] == df['ANS']).mean()\n",
+ "print(f\"Accuracy: {accuracy_mmmlu_hin:.4f}\")"
+ ],
+ "metadata": {
+ "id": "wDxU0TXK85G7",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 376,
+ "referenced_widgets": [
+ "58b357e3545b4518a9825631ccd640d0",
+ "a49966b229cc49e59c64da6463c23480",
+ "c2b556acdd354b3e9bc78ead0ef072b1",
+ "76e1235bbfa5418bb80d7718d1f6810b",
+ "1049068e703a413598116ff3fc179300",
+ "90034e24b3354355b39354a1266efdbf",
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+ "4084c75f5efa497d8e59c083ae10476d",
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+ "8cfed9c91ab44c0abe2be44fb2b9e397",
+ "0e297b20f6234d1080f11c93f112df77",
+ "6086830483f54c008413a48e044eac39",
+ "3cfd3b2453434eaf9b9ff79ca4a54f59",
+ "fc8e0f64afa74bef910f5984378bae47",
+ "bbc2d1ec6cc14a5883fd901a142850fa",
+ "4f6cbf2b903b41ccb261c45e1692edef"
+ ]
+ },
+ "outputId": "9855369e-7222-400f-b144-e77b90122064"
+ },
+ "id": "wDxU0TXK85G7",
+ "execution_count": 11,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "MMMLU_H.csv: 0%| | 0.00/16.6M [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "58b357e3545b4518a9825631ccd640d0"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Generating train split: 0 examples [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "25ae1f19b0ca42b99e26f9e61ed490ac"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "14042\n",
+ "Average 'tok' value: 441.8879789203817\n",
+ "Max 'tok' value: 4926\n",
+ "Output\n",
+ "D 3776\n",
+ "C 3582\n",
+ "B 3462\n",
+ "A 3222\n",
+ "Name: count, dtype: int64\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 14042/14042 [1:03:32<00:00, 3.68it/s]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "ANS\n",
+ "A 4504\n",
+ "B 3736\n",
+ "C 2992\n",
+ "D 2810\n",
+ "Name: count, dtype: int64\n",
+ "Accuracy: 0.5632\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#**BOOLQ ENG**"
+ ],
+ "metadata": {
+ "id": "4aC98L-5Gi9D"
+ },
+ "id": "4aC98L-5Gi9D"
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "dataset = load_dataset(\"1-800-LLMs/Test-Collection\", data_files=\"BoolQ_E.csv\", split=\"train\")\n",
+ "df = dataset.to_pandas()\n",
+ "print(len(df))\n",
+ "df['tok'] = df['Input'].apply(lambda x: len(tokenizer.encode(x)))\n",
+ "print(f\"Average 'tok' value: {df['tok'].mean()}\")\n",
+ "print(f\"Max 'tok' value: {df['tok'].max()}\")\n",
+ "df = df.sort_values('tok', ascending=False)\n",
+ "print(df['Output'].value_counts())\n",
+ "responses = []\n",
+ "prob_a1_list = []\n",
+ "prob_a2_list = []\n",
+ "prob_a3_list = []\n",
+ "prob_b1_list = []\n",
+ "prob_b2_list = []\n",
+ "prob_b3_list = []\n",
+ "batch_size = 1\n",
+ "for start in tqdm(range(0, len(df), batch_size)):\n",
+ " batch_texts = df['Input'][start:start+batch_size].tolist()\n",
+ " for input_text in batch_texts:\n",
+ " prompt = f\"### INPUT : {input_text} Respond with just one word based on these options : \"\n",
+ " message = [{\"role\": \"user\", \"content\": prompt}]\n",
+ " inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n",
+ " with torch.no_grad():\n",
+ " outputs = model.generate(input_ids=inputs, max_new_tokens=3, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n",
+ " scores = outputs.scores # tuple of [batch_size, vocab_size] for each token\n",
+ " token_ids_a = tokenizer.encode('True', add_special_tokens=False)[0]\n",
+ " token_ids_b = tokenizer.encode('False', add_special_tokens=False)[0]\n",
+ " for i in range(3):\n",
+ " if i < len(scores):\n",
+ " probs = F.softmax(scores[i], dim=-1)\n",
+ " prob_a = probs[0, token_ids_a].item()\n",
+ " prob_b = probs[0, token_ids_b].item()\n",
+ " else:\n",
+ " prob_a, prob_b = 0.0, 0.0\n",
+ " if i == 0:\n",
+ " prob_a1_list.append(prob_a)\n",
+ " prob_b1_list.append(prob_b)\n",
+ " elif i == 1:\n",
+ " prob_a2_list.append(prob_a)\n",
+ " prob_b2_list.append(prob_b)\n",
+ " elif i == 2:\n",
+ " prob_a3_list.append(prob_a)\n",
+ " prob_b3_list.append(prob_b)\n",
+ "df['A1'] = prob_a1_list\n",
+ "df['A2'] = prob_a2_list\n",
+ "df['A3'] = prob_a3_list\n",
+ "df['B1'] = prob_b1_list\n",
+ "df['B2'] = prob_b2_list\n",
+ "df['B3'] = prob_b3_list\n",
+ "df['A'] = df['A1'] + df['A2'] + df['A3']\n",
+ "df['B'] = df['B1'] + df['B2'] + df['B3']\n",
+ "df['ANS'] = df[['A', 'B']].idxmax(axis=1)\n",
+ "df['ANS'] = df['ANS'].replace({'A': 'True', 'B': 'False'})\n",
+ "df['ANS'] = df['ANS'].astype(str)\n",
+ "df['Output'] = df['Output'].astype(str)\n",
+ "print(df['ANS'].value_counts())\n",
+ "accuracy_boolq_eng = (df['Output'] == df['ANS']).mean()\n",
+ "print(f\"Accuracy: {accuracy_boolq_eng:.4f}\")"
+ ],
+ "metadata": {
+ "id": "m7ayEga9Ghkd",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 307,
+ "referenced_widgets": [
+ "793b520b031d40af85b8f35c0fc79ff6",
+ "a960febc889b48db9f56c78e694c9256",
+ "2a4472fff8454d2883264a23142ba634",
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+ "2b4219da5f734caea4991224544d8498",
+ "a095f869c5aa4b83b61d20add2fce739",
+ "aa1f0b0da25947d0a649b4cdb798a668",
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+ "ee4c453fb47a432e9025e38d08d926ef",
+ "add3e9d3c56241aca98c3472da790713"
+ ]
+ },
+ "outputId": "4dd80755-6adc-4d7f-b558-a59b761d293d"
+ },
+ "id": "m7ayEga9Ghkd",
+ "execution_count": 12,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "BoolQ_E.csv: 0%| | 0.00/2.09M [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "793b520b031d40af85b8f35c0fc79ff6"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Generating train split: 0 examples [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "cc6592f54ecb4632a34a3f848e96a8c3"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "3270\n",
+ "Average 'tok' value: 136.15474006116207\n",
+ "Max 'tok' value: 1156\n",
+ "Output\n",
+ "True 2033\n",
+ "False 1237\n",
+ "Name: count, dtype: int64\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 3270/3270 [10:20<00:00, 5.27it/s]\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "ANS\n",
+ "True 1940\n",
+ "False 1330\n",
+ "Name: count, dtype: int64\n",
+ "Accuracy: 0.8865\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#**BOOLQ HINDI**"
+ ],
+ "metadata": {
+ "id": "uAhhi93PHZ40"
+ },
+ "id": "uAhhi93PHZ40"
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "dataset = load_dataset(\"1-800-LLMs/Test-Collection\", data_files=\"BoolQ_H.csv\", split=\"train\")\n",
+ "df = dataset.to_pandas()\n",
+ "print(len(df))\n",
+ "df['tok'] = df['Input'].apply(lambda x: len(tokenizer.encode(x)))\n",
+ "print(f\"Average 'tok' value: {df['tok'].mean()}\")\n",
+ "print(f\"Max 'tok' value: {df['tok'].max()}\")\n",
+ "df = df.sort_values('tok', ascending=False)\n",
+ "print(df['Output'].value_counts())\n",
+ "df = df[1:]\n",
+ "responses = []\n",
+ "prob_a1_list = []\n",
+ "prob_a2_list = []\n",
+ "prob_a3_list = []\n",
+ "prob_b1_list = []\n",
+ "prob_b2_list = []\n",
+ "prob_b3_list = []\n",
+ "batch_size = 1\n",
+ "for start in tqdm(range(0, len(df), batch_size)):\n",
+ " batch_texts = df['Input'][start:start+batch_size].tolist()\n",
+ " for input_text in batch_texts:\n",
+ " prompt = f\"### INPUT : {input_text} Respond with just one word based on these options : \"\n",
+ " message = [{\"role\": \"user\", \"content\": prompt}]\n",
+ " inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n",
+ " with torch.no_grad():\n",
+ " outputs = model.generate(input_ids=inputs, max_new_tokens=3, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n",
+ " scores = outputs.scores # tuple of [batch_size, vocab_size] for each token\n",
+ " token_ids_a = tokenizer.encode('True', add_special_tokens=False)[0]\n",
+ " token_ids_b = tokenizer.encode('False', add_special_tokens=False)[0]\n",
+ " for i in range(3):\n",
+ " if i < len(scores):\n",
+ " probs = F.softmax(scores[i], dim=-1)\n",
+ " prob_a = probs[0, token_ids_a].item()\n",
+ " prob_b = probs[0, token_ids_b].item()\n",
+ " else:\n",
+ " prob_a, prob_b = 0.0, 0.0\n",
+ " if i == 0:\n",
+ " prob_a1_list.append(prob_a)\n",
+ " prob_b1_list.append(prob_b)\n",
+ " elif i == 1:\n",
+ " prob_a2_list.append(prob_a)\n",
+ " prob_b2_list.append(prob_b)\n",
+ " elif i == 2:\n",
+ " prob_a3_list.append(prob_a)\n",
+ " prob_b3_list.append(prob_b)\n",
+ "df['A1'] = prob_a1_list\n",
+ "df['A2'] = prob_a2_list\n",
+ "df['A3'] = prob_a3_list\n",
+ "df['B1'] = prob_b1_list\n",
+ "df['B2'] = prob_b2_list\n",
+ "df['B3'] = prob_b3_list\n",
+ "df['A'] = df['A1'] + df['A2'] + df['A3']\n",
+ "df['B'] = df['B1'] + df['B2'] + df['B3']\n",
+ "df['ANS'] = df[['A', 'B']].idxmax(axis=1)\n",
+ "df['ANS'] = df['ANS'].replace({'A': 'True', 'B': 'False'})\n",
+ "df['ANS'] = df['ANS'].astype(str)\n",
+ "df['Output'] = df['Output'].astype(str)\n",
+ "print(df['ANS'].value_counts())\n",
+ "accuracy_boolq_hin = (df['Output'] == df['ANS']).mean()\n",
+ "print(f\"Accuracy: {accuracy_boolq_hin:.4f}\")"
+ ],
+ "metadata": {
+ "id": "GOHy6uE285AB",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 307,
+ "referenced_widgets": [
+ "e1ad3f36b2ac4a8aa790da8ce449c327",
+ "651427b3a9844e04811356910ee6cbda",
+ "b2669f878d3048d7905180c3db1cc7b5",
+ "f9a836d6616343628991b500e3652720",
+ "b03cceb9236e49dd8f16ab1d2eec92b0",
+ "352c36826d6745b2964fc70c3162989e",
+ "33bebfa4124a4560a7cbb13bb6548494",
+ "f7bd3331422846838525ac6b74429aec",
+ "8ef1da28f75d46dba61b519a2880dda1",
+ "a3df9b99b11f480eae64c677455eb382",
+ "37d8800a0c294bc4b36854c8f0925ff3",
+ "b2d1fe4e68b1426b8b1580d83006a96d",
+ "9e93690f9c51475dabdf8291f0992467",
+ "728b100838444954bc54edf12d8a25c7",
+ "011f67b6bbef4678ae6df30ce6318c90",
+ "8f8e118c44c44c93975f34c1d44b4f4d",
+ "7152093623dc4c188d4dfd46ce3c1f5a",
+ "bd96f1e0aabd42c089d1d0a24e5b3e9e",
+ "abafa26e88944c46b9bd0b82da2534c8",
+ "17a707b9c52b4da6b6a847892e48bbee",
+ "90be16b07b504e90a886fb78e425a1d3",
+ "bfae50f1546846f3b063d65a27f974ba"
+ ]
+ },
+ "outputId": "26a7f78b-c433-449f-e707-908335a98327"
+ },
+ "id": "GOHy6uE285AB",
+ "execution_count": 13,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "BoolQ_H.csv: 0%| | 0.00/5.28M [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "e1ad3f36b2ac4a8aa790da8ce449c327"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Generating train split: 0 examples [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "b2d1fe4e68b1426b8b1580d83006a96d"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "3270\n",
+ "Average 'tok' value: 611.0498470948012\n",
+ "Max 'tok' value: 62241\n",
+ "Output\n",
+ "True 2033\n",
+ "False 1237\n",
+ "Name: count, dtype: int64\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 3269/3269 [14:50<00:00, 3.67it/s]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "ANS\n",
+ "True 2132\n",
+ "False 1137\n",
+ "Name: count, dtype: int64\n",
+ "Accuracy: 0.8593\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#**Context MCQ ENGLISH**"
+ ],
+ "metadata": {
+ "id": "1ugA-oyeReI9"
+ },
+ "id": "1ugA-oyeReI9"
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "dataset = load_dataset(\"1-800-LLMs/Test-Collection\", data_files=\"ContextMCQ_E.csv\", split=\"train\")\n",
+ "df = dataset.to_pandas()\n",
+ "print(len(df))\n",
+ "print(df['Output'].value_counts())\n",
+ "responses = []\n",
+ "prob_a1_list = []\n",
+ "prob_a2_list = []\n",
+ "prob_a3_list = []\n",
+ "prob_b1_list = []\n",
+ "prob_b2_list = []\n",
+ "prob_b3_list = []\n",
+ "prob_c1_list = []\n",
+ "prob_c2_list = []\n",
+ "prob_c3_list = []\n",
+ "prob_d1_list = []\n",
+ "prob_d2_list = []\n",
+ "prob_d3_list = []\n",
+ "batch_size = 1\n",
+ "for start in tqdm(range(0, len(df), batch_size)):\n",
+ " batch_texts = df['Input'][start:start+batch_size].tolist()\n",
+ " for input_text in batch_texts:\n",
+ " prompt = f\"### INPUT : {input_text} Respond with just one letter based on these options : \"\n",
+ " message = [{\"role\": \"user\", \"content\": prompt}]\n",
+ " inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n",
+ " with torch.no_grad():\n",
+ " outputs = model.generate(input_ids=inputs, max_new_tokens=3, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n",
+ " scores = outputs.scores # tuple of [batch_size, vocab_size] for each token\n",
+ " token_ids_a = tokenizer.encode('A', add_special_tokens=False)[0]\n",
+ " token_ids_b = tokenizer.encode('B', add_special_tokens=False)[0]\n",
+ " token_ids_c = tokenizer.encode('C', add_special_tokens=False)[0]\n",
+ " token_ids_d = tokenizer.encode('D', add_special_tokens=False)[0]\n",
+ " for i in range(3):\n",
+ " if i < len(scores):\n",
+ " probs = F.softmax(scores[i], dim=-1)\n",
+ " prob_a = probs[0, token_ids_a].item()\n",
+ " prob_b = probs[0, token_ids_b].item()\n",
+ " prob_c = probs[0, token_ids_c].item()\n",
+ " prob_d = probs[0, token_ids_d].item()\n",
+ " else:\n",
+ " prob_a, prob_b, prob_c, prob_d = 0.0, 0.0, 0.0, 0.0\n",
+ " if i == 0:\n",
+ " prob_a1_list.append(prob_a)\n",
+ " prob_b1_list.append(prob_b)\n",
+ " prob_c1_list.append(prob_c)\n",
+ " prob_d1_list.append(prob_d)\n",
+ " elif i == 1:\n",
+ " prob_a2_list.append(prob_a)\n",
+ " prob_b2_list.append(prob_b)\n",
+ " prob_c2_list.append(prob_c)\n",
+ " prob_d2_list.append(prob_d)\n",
+ " elif i == 2:\n",
+ " prob_a3_list.append(prob_a)\n",
+ " prob_b3_list.append(prob_b)\n",
+ " prob_c3_list.append(prob_c)\n",
+ " prob_d3_list.append(prob_d)\n",
+ "df['A1'] = prob_a1_list\n",
+ "df['A2'] = prob_a2_list\n",
+ "df['A3'] = prob_a3_list\n",
+ "df['B1'] = prob_b1_list\n",
+ "df['B2'] = prob_b2_list\n",
+ "df['B3'] = prob_b3_list\n",
+ "df['C1'] = prob_c1_list\n",
+ "df['C2'] = prob_c2_list\n",
+ "df['C3'] = prob_c3_list\n",
+ "df['D1'] = prob_d1_list\n",
+ "df['D2'] = prob_d2_list\n",
+ "df['D3'] = prob_d3_list\n",
+ "df['A'] = df['A1'] + df['A2'] + df['A3']\n",
+ "df['B'] = df['B1'] + df['B2'] + df['B3']\n",
+ "df['C'] = df['C1'] + df['C2'] + df['C3']\n",
+ "df['D'] = df['D1'] + df['D2'] + df['D3']\n",
+ "df['ANS'] = df[['A', 'B', 'C', 'D']].idxmax(axis=1)\n",
+ "print(df['ANS'].value_counts())\n",
+ "accuracy_mcq_eng = (df['Output'] == df['ANS']).mean()\n",
+ "print(f\"Accuracy: {accuracy_mcq_eng:.4f}\")"
+ ],
+ "metadata": {
+ "id": "K4gKxj8ZRdYS",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 341,
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+ "execution_count": 14,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "ContextMCQ_E.csv: 0%| | 0.00/1.63M [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "be02b6973f90474ebed602189ace07d8"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Generating train split: 0 examples [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "49d9cf42898d42d1880c95fa7f1903e6"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "1000\n",
+ "Output\n",
+ "C 280\n",
+ "B 244\n",
+ "D 241\n",
+ "A 235\n",
+ "Name: count, dtype: int64\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 1000/1000 [04:17<00:00, 3.88it/s]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "ANS\n",
+ "C 265\n",
+ "A 259\n",
+ "B 251\n",
+ "D 225\n",
+ "Name: count, dtype: int64\n",
+ "Accuracy: 0.8670\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#**Context MCQ HINDI**"
+ ],
+ "metadata": {
+ "id": "JVW_cii1SR3c"
+ },
+ "id": "JVW_cii1SR3c"
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "dataset = load_dataset(\"1-800-LLMs/Test-Collection\", data_files=\"ContextMCQ_H.csv\", split=\"train\")\n",
+ "df = dataset.to_pandas()\n",
+ "print(len(df))\n",
+ "print(df['Output'].value_counts())\n",
+ "responses = []\n",
+ "prob_a1_list = []\n",
+ "prob_a2_list = []\n",
+ "prob_a3_list = []\n",
+ "prob_b1_list = []\n",
+ "prob_b2_list = []\n",
+ "prob_b3_list = []\n",
+ "prob_c1_list = []\n",
+ "prob_c2_list = []\n",
+ "prob_c3_list = []\n",
+ "prob_d1_list = []\n",
+ "prob_d2_list = []\n",
+ "prob_d3_list = []\n",
+ "batch_size = 1\n",
+ "for start in tqdm(range(0, len(df), batch_size)):\n",
+ " batch_texts = df['Input'][start:start+batch_size].tolist()\n",
+ " for input_text in batch_texts:\n",
+ " prompt = f\"### INPUT : {input_text} Respond with just one letter based on these options : \"\n",
+ " message = [{\"role\": \"user\", \"content\": prompt}]\n",
+ " inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n",
+ " with torch.no_grad():\n",
+ " outputs = model.generate(input_ids=inputs, max_new_tokens=3, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n",
+ " scores = outputs.scores\n",
+ " token_ids_a = tokenizer.encode('A', add_special_tokens=False)[0]\n",
+ " token_ids_b = tokenizer.encode('B', add_special_tokens=False)[0]\n",
+ " token_ids_c = tokenizer.encode('C', add_special_tokens=False)[0]\n",
+ " token_ids_d = tokenizer.encode('D', add_special_tokens=False)[0]\n",
+ " for i in range(3):\n",
+ " if i < len(scores):\n",
+ " probs = F.softmax(scores[i], dim=-1)\n",
+ " prob_a = probs[0, token_ids_a].item()\n",
+ " prob_b = probs[0, token_ids_b].item()\n",
+ " prob_c = probs[0, token_ids_c].item()\n",
+ " prob_d = probs[0, token_ids_d].item()\n",
+ " else:\n",
+ " prob_a, prob_b, prob_c, prob_d = 0.0, 0.0, 0.0, 0.0\n",
+ " if i == 0:\n",
+ " prob_a1_list.append(prob_a)\n",
+ " prob_b1_list.append(prob_b)\n",
+ " prob_c1_list.append(prob_c)\n",
+ " prob_d1_list.append(prob_d)\n",
+ " elif i == 1:\n",
+ " prob_a2_list.append(prob_a)\n",
+ " prob_b2_list.append(prob_b)\n",
+ " prob_c2_list.append(prob_c)\n",
+ " prob_d2_list.append(prob_d)\n",
+ " elif i == 2:\n",
+ " prob_a3_list.append(prob_a)\n",
+ " prob_b3_list.append(prob_b)\n",
+ " prob_c3_list.append(prob_c)\n",
+ " prob_d3_list.append(prob_d)\n",
+ "df['A1'] = prob_a1_list\n",
+ "df['A2'] = prob_a2_list\n",
+ "df['A3'] = prob_a3_list\n",
+ "df['B1'] = prob_b1_list\n",
+ "df['B2'] = prob_b2_list\n",
+ "df['B3'] = prob_b3_list\n",
+ "df['C1'] = prob_c1_list\n",
+ "df['C2'] = prob_c2_list\n",
+ "df['C3'] = prob_c3_list\n",
+ "df['D1'] = prob_d1_list\n",
+ "df['D2'] = prob_d2_list\n",
+ "df['D3'] = prob_d3_list\n",
+ "df['A'] = df['A1'] + df['A2'] + df['A3']\n",
+ "df['B'] = df['B1'] + df['B2'] + df['B3']\n",
+ "df['C'] = df['C1'] + df['C2'] + df['C3']\n",
+ "df['D'] = df['D1'] + df['D2'] + df['D3']\n",
+ "df['ANS'] = df[['A', 'B', 'C', 'D']].idxmax(axis=1)\n",
+ "print(df['ANS'].value_counts())\n",
+ "accuracy_mcq_hin = (df['Output'] == df['ANS']).mean()\n",
+ "print(f\"Accuracy: {accuracy_mcq_hin:.4f}\")"
+ ],
+ "metadata": {
+ "id": "HrB5mDcf842y",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 341,
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+ },
+ "id": "HrB5mDcf842y",
+ "execution_count": 15,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "ContextMCQ_H.csv: 0%| | 0.00/4.14M [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "4f4b31a126f347d8ad79f45f5cfbc315"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Generating train split: 0 examples [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "d8f89f33f7544211b97cb75b1f99ae85"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "1000\n",
+ "Output\n",
+ "C 280\n",
+ "B 244\n",
+ "D 241\n",
+ "A 235\n",
+ "Name: count, dtype: int64\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 1000/1000 [06:39<00:00, 2.50it/s]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "ANS\n",
+ "A 270\n",
+ "B 262\n",
+ "C 254\n",
+ "D 214\n",
+ "Name: count, dtype: int64\n",
+ "Accuracy: 0.7830\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#**END**"
+ ],
+ "metadata": {
+ "id": "JqYw49CH3gfX"
+ },
+ "id": "JqYw49CH3gfX"
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "print(\"BOOLQ ENGLISH : \" ,accuracy_boolq_eng)\n",
+ "print(\"BOOLQ HINDI : \" ,accuracy_boolq_hin)\n",
+ "print(\"C-MCQ ENGLISH : \" ,accuracy_mcq_eng)\n",
+ "print(\"C-MCQ HINDI : \" ,accuracy_mcq_hin)\n",
+ "print(\"MMMLU ENGLISH : \" ,accuracy_mmmlu_eng)\n",
+ "print(\"MMMLU HINDI : \" ,accuracy_mmmlu_hin)\n",
+ "print(\"ARC-E ENGLISH : \" ,accuracy_arc_e_eng)\n",
+ "print(\"ARC-E HINDI : \" ,accuracy_arc_e_hin)\n",
+ "print(\"ARC-C ENGLISH : \" ,accuracy_arc_c_eng)\n",
+ "print(\"ARC-C HINDI : \" ,accuracy_arc_c_hin)\n",
+ "avg_hin_acc = (accuracy_boolq_hin + accuracy_mcq_hin + accuracy_mmmlu_hin + accuracy_arc_e_hin + accuracy_arc_c_hin)/5\n",
+ "avg_eng_acc = (accuracy_boolq_eng + accuracy_mcq_eng + accuracy_mmmlu_eng + accuracy_arc_e_eng + accuracy_arc_c_eng)/5\n",
+ "print(\"AVG SCORE : HINDI : \" ,avg_hin_acc)\n",
+ "print(\"AVG SCORE : ENGLISH : \" ,avg_eng_acc)\n",
+ "avg_tot_acc = (avg_hin_acc + avg_eng_acc)/2\n",
+ "print(\"TOT AVG SCORE : \" ,avg_tot_acc)\n",
+ "print(\"CLICK CTRl+S and wait for 2 sec\")\n",
+ "name = name.split('/')[-1]\n",
+ "name = name + \".ipynb\"\n",
+ "print(\"1) NOTEBOOK NAME SHOULD BE : \", name)\n",
+ "print(\"2) ADD THE CODE TO GITHUB @ https://github.com/1-800-SHARED-TASKS/New-Language-Adaptation/tree/main/Our-Evals/ALL-EVALS/ \")\n",
+ "print(\"3) UPDATE THE GOOGLE SHEET WITH THE SCORES \")"
+ ],
+ "metadata": {
+ "id": "ljShzEUtnaDH",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "f0d25fe7-ff5e-42f9-a597-ec48ca43f58a"
+ },
+ "id": "ljShzEUtnaDH",
+ "execution_count": 16,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "BOOLQ ENGLISH : 0.8865443425076452\n",
+ "BOOLQ HINDI : 0.8592841847659834\n",
+ "C-MCQ ENGLISH : 0.867\n",
+ "C-MCQ HINDI : 0.783\n",
+ "MMMLU ENGLISH : 0.758652613587808\n",
+ "MMMLU HINDI : 0.5632388548639795\n",
+ "ARC-E ENGLISH : 0.9776936026936027\n",
+ "ARC-E HINDI : 0.9036195286195287\n",
+ "ARC-C ENGLISH : 0.9266211604095563\n",
+ "ARC-C HINDI : 0.8225255972696246\n",
+ "AVG SCORE : HINDI : 0.7863336331038232\n",
+ "AVG SCORE : ENGLISH : 0.8833023438397225\n",
+ "TOT AVG SCORE : 0.8348179884717728\n",
+ "CLICK CTRl+S and wait for 2 sec\n",
+ "1) NOTEBOOK NAME SHOULD BE : PHI-4-B40.ipynb\n",
+ "2) ADD THE CODE TO GITHUB @ https://github.com/1-800-SHARED-TASKS/New-Language-Adaptation/tree/main/Our-Evals/ALL-EVALS/ \n",
+ "3) UPDATE THE GOOGLE SHEET WITH THE SCORES \n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [],
+ "metadata": {
+ "id": "7f8knA0zwnDF"
+ },
+ "id": "7f8knA0zwnDF",
+ "execution_count": 16,
+ "outputs": []
+ }
+ ],
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+ "display_name": "Python 3",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
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