diff --git "a/GEMMA_9B_B20_all_evals.ipynb" "b/GEMMA_9B_B20_all_evals.ipynb"
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+{
+ "cells": [
+ {
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+ "execution_count": 1,
+ "id": "0c24ca36-1782-4ce8-8094-6f6528dada19",
+ "metadata": {
+ "colab": {
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+ "height": 17,
+ "referenced_widgets": [
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+ "outputId": "eb96baed-4dd3-414c-ed08-657ec762563b"
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+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
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+ "\u001b[?25hDownloading nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n",
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+ "\u001b[?25hDownloading trl-0.14.0-py3-none-any.whl (313 kB)\n",
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+ "\u001b[?25hDownloading unsloth_zoo-2025.1.5-py3-none-any.whl (80 kB)\n",
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+ "\u001b[?25hDownloading xformers-0.0.29.post1-cp311-cp311-manylinux_2_28_x86_64.whl (15.3 MB)\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.3/15.3 MB\u001b[0m \u001b[31m84.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hDownloading bitsandbytes-0.45.1-py3-none-manylinux_2_24_x86_64.whl (69.7 MB)\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m69.7/69.7 MB\u001b[0m \u001b[31m29.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hDownloading hf_transfer-0.1.9-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.6 MB)\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.6/3.6 MB\u001b[0m \u001b[31m93.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hDownloading tyro-0.9.13-py3-none-any.whl (115 kB)\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.7/115.7 kB\u001b[0m \u001b[31m11.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hDownloading dill-0.3.8-py3-none-any.whl (116 kB)\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m11.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hDownloading fsspec-2024.9.0-py3-none-any.whl (179 kB)\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m179.3/179.3 kB\u001b[0m \u001b[31m15.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hDownloading multiprocess-0.70.16-py311-none-any.whl (143 kB)\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m143.5/143.5 kB\u001b[0m \u001b[31m13.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hDownloading shtab-1.7.1-py3-none-any.whl (14 kB)\n",
+ "Downloading cut_cross_entropy-25.1.1-py3-none-any.whl (22 kB)\n",
+ "Downloading xxhash-3.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.8/194.8 kB\u001b[0m \u001b[31m16.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hInstalling collected packages: xxhash, shtab, protobuf, nvidia-nvjitlink-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, hf_transfer, fsspec, dill, nvidia-cusparse-cu12, nvidia-cudnn-cu12, multiprocess, tyro, nvidia-cusolver-cu12, datasets, xformers, 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: nvidia-nvjitlink-cu12\n",
+ " Found existing installation: nvidia-nvjitlink-cu12 12.5.82\n",
+ " Uninstalling nvidia-nvjitlink-cu12-12.5.82:\n",
+ " Successfully uninstalled nvidia-nvjitlink-cu12-12.5.82\n",
+ " Attempting uninstall: nvidia-curand-cu12\n",
+ " Found existing installation: nvidia-curand-cu12 10.3.6.82\n",
+ " Uninstalling nvidia-curand-cu12-10.3.6.82:\n",
+ " Successfully uninstalled nvidia-curand-cu12-10.3.6.82\n",
+ " Attempting uninstall: nvidia-cufft-cu12\n",
+ " Found existing installation: nvidia-cufft-cu12 11.2.3.61\n",
+ " Uninstalling nvidia-cufft-cu12-11.2.3.61:\n",
+ " Successfully uninstalled nvidia-cufft-cu12-11.2.3.61\n",
+ " Attempting uninstall: nvidia-cuda-runtime-cu12\n",
+ " Found existing installation: nvidia-cuda-runtime-cu12 12.5.82\n",
+ " Uninstalling nvidia-cuda-runtime-cu12-12.5.82:\n",
+ " Successfully uninstalled nvidia-cuda-runtime-cu12-12.5.82\n",
+ " Attempting uninstall: nvidia-cuda-nvrtc-cu12\n",
+ " Found existing installation: nvidia-cuda-nvrtc-cu12 12.5.82\n",
+ " Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82:\n",
+ " Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82\n",
+ " Attempting uninstall: nvidia-cuda-cupti-cu12\n",
+ " Found existing installation: nvidia-cuda-cupti-cu12 12.5.82\n",
+ " Uninstalling nvidia-cuda-cupti-cu12-12.5.82:\n",
+ " Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82\n",
+ " Attempting uninstall: nvidia-cublas-cu12\n",
+ " Found existing installation: nvidia-cublas-cu12 12.5.3.2\n",
+ " Uninstalling nvidia-cublas-cu12-12.5.3.2:\n",
+ " Successfully uninstalled nvidia-cublas-cu12-12.5.3.2\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",
+ " Attempting uninstall: nvidia-cusparse-cu12\n",
+ " Found existing installation: nvidia-cusparse-cu12 12.5.1.3\n",
+ " Uninstalling nvidia-cusparse-cu12-12.5.1.3:\n",
+ " Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3\n",
+ " Attempting uninstall: nvidia-cudnn-cu12\n",
+ " Found existing installation: nvidia-cudnn-cu12 9.3.0.75\n",
+ " Uninstalling nvidia-cudnn-cu12-9.3.0.75:\n",
+ " Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\n",
+ " Attempting uninstall: nvidia-cusolver-cu12\n",
+ " Found existing installation: nvidia-cusolver-cu12 11.6.3.83\n",
+ " Uninstalling nvidia-cusolver-cu12-11.6.3.83:\n",
+ " Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83\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",
+ "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\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.\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 nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127 protobuf-3.20.3 shtab-1.7.1 trl-0.14.0 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": "f737263a897b4a1f8572423c497a7939"
+ }
+ },
+ "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-n16vca_n\n",
+ " Running command git clone --filter=blob:none --quiet https://github.com/unslothai/unsloth.git /tmp/pip-req-build-n16vca_n\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=4d06347cf82ce344541809bc420a464a5588f8218d88869e1305842a93c47e7f\n",
+ " Stored in directory: /tmp/pip-ephem-wheel-cache-zychrxqw/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": "5dfc19af-024e-4396-ff1c-a0bbb862831b"
+ },
+ "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) (25.1.0)\n",
+ "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (1.5.0)\n",
+ "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (6.1.0)\n",
+ "Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (0.2.1)\n",
+ "Requirement already satisfied: yarl<2.0,>=1.17.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (1.18.3)\n",
+ "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.11/dist-packages (from huggingface-hub>=0.23.0->datasets) (4.12.2)\n",
+ "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests>=2.32.2->datasets) (3.4.1)\n",
+ "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests>=2.32.2->datasets) (3.10)\n",
+ "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests>=2.32.2->datasets) (2.3.0)\n",
+ "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests>=2.32.2->datasets) (2024.12.14)\n",
+ "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas->datasets) (2.8.2)\n",
+ "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas->datasets) (2024.2)\n",
+ "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas->datasets) (2025.1)\n",
+ "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.17.0)\n",
+ "Requirement already satisfied: unsloth in /usr/local/lib/python3.11/dist-packages (2025.1.7)\n",
+ "Collecting git+https://github.com/unslothai/unsloth.git\n",
+ " Cloning https://github.com/unslothai/unsloth.git to /tmp/pip-req-build-00e8ndk2\n",
+ " Running command git clone --filter=blob:none --quiet https://github.com/unslothai/unsloth.git /tmp/pip-req-build-00e8ndk2\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=e1cad0ce3a58f5f304fdb8e47a06a1fa2efe1cf371ca1d33720e71002c7ff438\n",
+ " Stored in directory: /tmp/pip-ephem-wheel-cache-_wyf2f_s/wheels/d1/17/05/850ab10c33284a4763b0595cd8ea9d01fce6e221cac24b3c01\n",
+ "Successfully built unsloth\n",
+ "Installing collected packages: unsloth\n",
+ " Attempting uninstall: unsloth\n",
+ " Found existing installation: unsloth 2025.1.7\n",
+ " Uninstalling unsloth-2025.1.7:\n",
+ " Successfully uninstalled unsloth-2025.1.7\n",
+ "Successfully installed unsloth-2025.1.7\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install datasets tqdm\n",
+ "!pip install unsloth\n",
+ "!pip install --force-reinstall --no-cache-dir --no-deps git+https://github.com/unslothai/unsloth.git"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "fbc9900d-28d2-4bda-9848-b572fbe778d2",
+ "metadata": {
+ "id": "fbc9900d-28d2-4bda-9848-b572fbe778d2",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000,
+ "referenced_widgets": [
+ "5b9005f533384b3baaebd81dd7be947a",
+ "11823014bf2041a2996fbc9e36cffa47",
+ "216220d0455a46e9a5f7e6a683f61b21",
+ "0914102bd06d4f818a48f2e6e12b139a",
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+ ]
+ },
+ "outputId": "c1d02e04-fdd9-4c0e-f115-645909e213c8"
+ },
+ "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: If you want to finetune Gemma 2, install flash-attn to make it faster!\n",
+ "To install flash-attn, do the below:\n",
+ "\n",
+ "pip install --no-deps --upgrade \"flash-attn>=2.6.3\"\n",
+ "==((====))== Unsloth 2025.1.7: Fast Gemma2 patching. Transformers: 4.47.1.\n",
+ " \\\\ /| GPU: NVIDIA A100-SXM4-40GB. Max memory: 39.557 GB. Platform: Linux.\n",
+ "O^O/ \\_/ \\ Torch: 2.5.1+cu124. CUDA: 8.0. CUDA Toolkit: 12.4. Triton: 3.1.0\n",
+ "\\ / Bfloat16 = TRUE. FA [Xformers = 0.0.29.post1. FA2 = False]\n",
+ " \"-____-\" Free Apache license: http://github.com/unslothai/unsloth\n",
+ "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "model.safetensors.index.json: 0%| | 0.00/39.1k [00:00, ?B/s]"
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+ "data": {
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+ ],
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+ },
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+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
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+ ],
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+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
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+ ],
+ "application/vnd.jupyter.widget-view+json": {
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+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Loading checkpoint shards: 0%| | 0/4 [00:00, ?it/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
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+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
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+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
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+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
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+ ],
+ "application/vnd.jupyter.widget-view+json": {
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+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
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+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "d86ef3319e7c43bdad9342f804ef98a3"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
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+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "d72e0e2051ad456f9870602a8bd794bf"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
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+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
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+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "Unsloth 2025.1.7 patched 42 layers with 42 QKV layers, 42 O layers and 42 MLP layers.\n"
+ ]
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "PeftModelForCausalLM(\n",
+ " (base_model): LoraModel(\n",
+ " (model): Gemma2ForCausalLM(\n",
+ " (model): Gemma2Model(\n",
+ " (embed_tokens): Embedding(256000, 3584, padding_idx=0)\n",
+ " (layers): ModuleList(\n",
+ " (0-41): 42 x Gemma2DecoderLayer(\n",
+ " (self_attn): Gemma2Attention(\n",
+ " (q_proj): lora.Linear(\n",
+ " (base_layer): Linear(in_features=3584, out_features=4096, bias=False)\n",
+ " (lora_dropout): ModuleDict(\n",
+ " (default): Identity()\n",
+ " )\n",
+ " (lora_A): ModuleDict(\n",
+ " (default): Linear(in_features=3584, out_features=16, bias=False)\n",
+ " )\n",
+ " (lora_B): ModuleDict(\n",
+ " (default): Linear(in_features=16, out_features=4096, bias=False)\n",
+ " )\n",
+ " (lora_embedding_A): ParameterDict()\n",
+ " (lora_embedding_B): ParameterDict()\n",
+ " (lora_magnitude_vector): ModuleDict()\n",
+ " )\n",
+ " (k_proj): lora.Linear(\n",
+ " (base_layer): Linear(in_features=3584, out_features=2048, bias=False)\n",
+ " (lora_dropout): ModuleDict(\n",
+ " (default): Identity()\n",
+ " )\n",
+ " (lora_A): ModuleDict(\n",
+ " (default): Linear(in_features=3584, out_features=16, bias=False)\n",
+ " )\n",
+ " (lora_B): ModuleDict(\n",
+ " (default): Linear(in_features=16, out_features=2048, bias=False)\n",
+ " )\n",
+ " (lora_embedding_A): ParameterDict()\n",
+ " (lora_embedding_B): ParameterDict()\n",
+ " (lora_magnitude_vector): ModuleDict()\n",
+ " )\n",
+ " (v_proj): lora.Linear(\n",
+ " (base_layer): Linear(in_features=3584, out_features=2048, bias=False)\n",
+ " (lora_dropout): ModuleDict(\n",
+ " (default): Identity()\n",
+ " )\n",
+ " (lora_A): ModuleDict(\n",
+ " (default): Linear(in_features=3584, out_features=16, bias=False)\n",
+ " )\n",
+ " (lora_B): ModuleDict(\n",
+ " (default): Linear(in_features=16, out_features=2048, bias=False)\n",
+ " )\n",
+ " (lora_embedding_A): ParameterDict()\n",
+ " (lora_embedding_B): ParameterDict()\n",
+ " (lora_magnitude_vector): ModuleDict()\n",
+ " )\n",
+ " (o_proj): lora.Linear(\n",
+ " (base_layer): Linear(in_features=4096, out_features=3584, bias=False)\n",
+ " (lora_dropout): ModuleDict(\n",
+ " (default): Identity()\n",
+ " )\n",
+ " (lora_A): ModuleDict(\n",
+ " (default): Linear(in_features=4096, out_features=16, bias=False)\n",
+ " )\n",
+ " (lora_B): ModuleDict(\n",
+ " (default): Linear(in_features=16, out_features=3584, bias=False)\n",
+ " )\n",
+ " (lora_embedding_A): ParameterDict()\n",
+ " (lora_embedding_B): ParameterDict()\n",
+ " (lora_magnitude_vector): ModuleDict()\n",
+ " )\n",
+ " (rotary_emb): GemmaFixedRotaryEmbedding()\n",
+ " )\n",
+ " (mlp): Gemma2MLP(\n",
+ " (gate_proj): lora.Linear(\n",
+ " (base_layer): Linear(in_features=3584, out_features=14336, bias=False)\n",
+ " (lora_dropout): ModuleDict(\n",
+ " (default): Identity()\n",
+ " )\n",
+ " (lora_A): ModuleDict(\n",
+ " (default): Linear(in_features=3584, out_features=16, bias=False)\n",
+ " )\n",
+ " (lora_B): ModuleDict(\n",
+ " (default): Linear(in_features=16, out_features=14336, bias=False)\n",
+ " )\n",
+ " (lora_embedding_A): ParameterDict()\n",
+ " (lora_embedding_B): ParameterDict()\n",
+ " (lora_magnitude_vector): ModuleDict()\n",
+ " )\n",
+ " (up_proj): lora.Linear(\n",
+ " (base_layer): Linear(in_features=3584, out_features=14336, bias=False)\n",
+ " (lora_dropout): ModuleDict(\n",
+ " (default): Identity()\n",
+ " )\n",
+ " (lora_A): ModuleDict(\n",
+ " (default): Linear(in_features=3584, out_features=16, bias=False)\n",
+ " )\n",
+ " (lora_B): ModuleDict(\n",
+ " (default): Linear(in_features=16, out_features=14336, bias=False)\n",
+ " )\n",
+ " (lora_embedding_A): ParameterDict()\n",
+ " (lora_embedding_B): ParameterDict()\n",
+ " (lora_magnitude_vector): ModuleDict()\n",
+ " )\n",
+ " (down_proj): lora.Linear(\n",
+ " (base_layer): Linear(in_features=14336, out_features=3584, bias=False)\n",
+ " (lora_dropout): ModuleDict(\n",
+ " (default): Identity()\n",
+ " )\n",
+ " (lora_A): ModuleDict(\n",
+ " (default): Linear(in_features=14336, out_features=16, bias=False)\n",
+ " )\n",
+ " (lora_B): ModuleDict(\n",
+ " (default): Linear(in_features=16, out_features=3584, bias=False)\n",
+ " )\n",
+ " (lora_embedding_A): ParameterDict()\n",
+ " (lora_embedding_B): ParameterDict()\n",
+ " (lora_magnitude_vector): ModuleDict()\n",
+ " )\n",
+ " (act_fn): PytorchGELUTanh()\n",
+ " )\n",
+ " (input_layernorm): Gemma2RMSNorm((3584,), eps=1e-06)\n",
+ " (post_attention_layernorm): Gemma2RMSNorm((3584,), eps=1e-06)\n",
+ " (pre_feedforward_layernorm): Gemma2RMSNorm((3584,), eps=1e-06)\n",
+ " (post_feedforward_layernorm): Gemma2RMSNorm((3584,), eps=1e-06)\n",
+ " )\n",
+ " )\n",
+ " (norm): Gemma2RMSNorm((3584,), eps=1e-06)\n",
+ " )\n",
+ " (lm_head): Linear(in_features=3584, out_features=256000, bias=False)\n",
+ " )\n",
+ " )\n",
+ ")"
+ ]
+ },
+ "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 = \"DrishtiSharma/GEMMA-9B-B20\"\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": "7961cd08-e18e-4913-8568-caed93916243"
+ },
+ "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",
+ "AUTOTUNE bmm(16x79x256, 16x256x79)\n",
+ " triton_bmm_1 0.0154 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=32, BLOCK_N=32, B_PROLOGUE_CAST_TYPE=None, EVEN_K=False, GROUP_M=8, num_stages=2, num_warps=4\n",
+ " triton_bmm_2 0.0154 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=64, B_PROLOGUE_CAST_TYPE=None, EVEN_K=False, GROUP_M=8, num_stages=5, num_warps=8\n",
+ " triton_bmm_3 0.0154 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=32, B_PROLOGUE_CAST_TYPE=None, EVEN_K=False, GROUP_M=8, num_stages=5, num_warps=8\n",
+ " triton_bmm_7 0.0154 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=64, BLOCK_N=64, B_PROLOGUE_CAST_TYPE=None, EVEN_K=False, GROUP_M=8, num_stages=3, num_warps=8\n",
+ " triton_bmm_4 0.0164 ms 93.7% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=64, BLOCK_N=32, B_PROLOGUE_CAST_TYPE=None, EVEN_K=False, GROUP_M=8, num_stages=5, num_warps=4\n",
+ " triton_bmm_6 0.0174 ms 88.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=64, B_PROLOGUE_CAST_TYPE=None, EVEN_K=False, GROUP_M=8, num_stages=2, num_warps=4\n",
+ " triton_bmm_0 0.0184 ms 83.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, B_PROLOGUE_CAST_TYPE=None, EVEN_K=False, GROUP_M=8, num_stages=1, num_warps=2\n",
+ " triton_bmm_5 0.0184 ms 83.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=64, BLOCK_N=64, B_PROLOGUE_CAST_TYPE=None, EVEN_K=False, GROUP_M=8, num_stages=2, num_warps=4\n",
+ " triton_bmm_9 0.0195 ms 78.9% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=128, B_PROLOGUE_CAST_TYPE=None, EVEN_K=False, GROUP_M=8, num_stages=3, num_warps=4\n",
+ " triton_bmm_10 0.0195 ms 78.9% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=128, B_PROLOGUE_CAST_TYPE=None, EVEN_K=False, GROUP_M=8, num_stages=4, num_warps=8\n",
+ "SingleProcess AUTOTUNE benchmarking takes 2.1560 seconds and 0.0130 seconds precompiling\n",
+ "AUTOTUNE bmm(16x79x79, 16x79x256)\n",
+ " triton_bmm_21 0.0123 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=64, B_PROLOGUE_CAST_TYPE=None, EVEN_K=False, GROUP_M=8, num_stages=5, num_warps=8\n",
+ " bmm 0.0133 ms 92.3% \n",
+ " triton_bmm_22 0.0133 ms 92.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=32, B_PROLOGUE_CAST_TYPE=None, EVEN_K=False, GROUP_M=8, num_stages=5, num_warps=8\n",
+ " triton_bmm_24 0.0143 ms 85.7% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=64, BLOCK_N=64, B_PROLOGUE_CAST_TYPE=None, EVEN_K=False, GROUP_M=8, num_stages=2, num_warps=4\n",
+ " triton_bmm_25 0.0143 ms 85.7% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=64, B_PROLOGUE_CAST_TYPE=None, EVEN_K=False, GROUP_M=8, num_stages=2, num_warps=4\n",
+ " triton_bmm_29 0.0143 ms 85.7% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=128, B_PROLOGUE_CAST_TYPE=None, EVEN_K=False, GROUP_M=8, num_stages=4, num_warps=8\n",
+ " triton_bmm_33 0.0143 ms 85.7% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=128, BLOCK_N=64, B_PROLOGUE_CAST_TYPE=None, EVEN_K=False, GROUP_M=8, num_stages=4, num_warps=8\n",
+ " triton_bmm_19 0.0154 ms 80.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, B_PROLOGUE_CAST_TYPE=None, EVEN_K=False, GROUP_M=8, num_stages=1, num_warps=2\n",
+ " triton_bmm_32 0.0154 ms 80.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=128, BLOCK_N=64, B_PROLOGUE_CAST_TYPE=None, EVEN_K=False, GROUP_M=8, num_stages=3, num_warps=4\n",
+ " triton_bmm_20 0.0164 ms 75.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=32, BLOCK_N=32, B_PROLOGUE_CAST_TYPE=None, EVEN_K=False, GROUP_M=8, num_stages=2, num_warps=4\n",
+ "SingleProcess AUTOTUNE benchmarking takes 2.1534 seconds and 0.0026 seconds precompiling\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Generated Response (20 tokens):\n",
+ "The correct answer is **B) 700,003**. \n",
+ "\n",
+ "Here's how to add the numbers:\n",
+ "\n",
+ "```\n",
+ " 46,911\n",
+ "+ 653,092\n",
+ "----------\n",
+ " 700,003 \n",
+ "```\n",
+ "\n",
+ "Probability of 'A' at token 1: 0.0106\n",
+ "Probability of 'B' at token 1: 0.0039\n",
+ "Probability of 'C' at token 1: 0.0010\n",
+ "Probability of 'D' at token 1: 0.0021\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": [
+ "8f560d4ed6da47879392d67409ddf54d",
+ "ae84de29b0fa438d832adc62068af1c8",
+ "da9be8e9929747b5b42f6f55900d4cd3",
+ "f3ea50adc59d4fa4b57d8e34634373cc",
+ "d431d3691ce74c1da15747837bd38316",
+ "a62172789a4c4f1191823b17fa0255ff",
+ "0f820f0cae714613aa6254399fb536d5",
+ "eec76907660c4b1191935937a866b5da",
+ "600642a9e4584e8a8cf10c8700d18e89",
+ "c4dec09f5bbf4001a4feb22d54bebb4f",
+ "b29afbd017b143bc8e056a28999710f7",
+ "7d391f01b1494668a65ffe27a4f58b7d",
+ "0d78df11324c44189a59ad23b49c819a",
+ "55ec87cea08441c0bd54e0852f24cc88",
+ "66b44b2e81464c46ab7884edfed9c6b7",
+ "4a6f740d7b0e4e6d9115112bf0df0113",
+ "7a3c200e30f34216b9c2e8a388eb73cd",
+ "fb3d102180fc44e9815ec302ebfe1245",
+ "b4db7346fda24645822f379941658510",
+ "5c57648eade1426a889f3bd19ea7eb35",
+ "b44cf46dc76f4e5ead2c18cf92be1181",
+ "e10ce95e114d4361b83f1ac33513b099"
+ ]
+ },
+ "outputId": "352a3d43-1d7d-471f-dd36-de5a856f23d9"
+ },
+ "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": "8f560d4ed6da47879392d67409ddf54d"
+ }
+ },
+ "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": "7d391f01b1494668a65ffe27a4f58b7d"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "1172\n",
+ "Average 'tok' value: 70.26706484641639\n",
+ "Max 'tok' value: 197\n",
+ "Output\n",
+ "B 311\n",
+ "C 310\n",
+ "D 285\n",
+ "A 266\n",
+ "Name: count, dtype: int64\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 1172/1172 [04:44<00:00, 4.11it/s]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "ANS\n",
+ "C 313\n",
+ "B 307\n",
+ "A 277\n",
+ "D 275\n",
+ "Name: count, dtype: int64\n",
+ "Accuracy: 0.8754\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": {
+ "id": "mPFAiosJ3jzD",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 813,
+ "referenced_widgets": [
+ "81c133c3445040d581ccac56b06ed6c4",
+ "28d762edb4c14dad8564971733f2e8fb",
+ "b223547deac0466aae15e9b6ade0cf16",
+ "d70ecafc77ab45a1a19fa44677c51cf7",
+ "32b732a285f847719af99cd67bf82e20",
+ "2e226bcaaa84454c824136668a7ef15e",
+ "c1d33f5c7ce34fe18acafaa5a6d56fc5",
+ "f63cb64256694552beea9b67226a5b43",
+ "541a3fa21d1b41ca9e074f84a8491fb5",
+ "2704a04e0d95450b9ad4e46c48a9424d",
+ "203cc7476fc443fd988de59eb9703863",
+ "e91468df63554cad88a33d565851a55d",
+ "64f9f73686d04f77920c997baba1ebc5",
+ "2cda1e6e452c479397ddaf2fd32e1d66",
+ "e663184e76484a2095984a2e856d0dfb",
+ "722e03131aa7425f8dab0526e6caed4d",
+ "9232a84af8784818805303fb8633ceb2",
+ "751aaa3d080c4a429364b117879ec235",
+ "257198f670e143b3b4478d4b273fd124",
+ "c436a37c8d9f4accbe791b604e09272a",
+ "9a531fbbabd340f2984d83747b60bde9",
+ "717574c3673b48ffb85acdaa6c05570e"
+ ]
+ },
+ "outputId": "e49448d3-49b0-4874-e300-972ffb8c8e6b"
+ },
+ "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": "81c133c3445040d581ccac56b06ed6c4"
+ }
+ },
+ "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": "e91468df63554cad88a33d565851a55d"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "1172\n",
+ "Average 'tok' value: 114.31313993174061\n",
+ "Max 'tok' value: 389\n",
+ "Output\n",
+ "B 311\n",
+ "C 310\n",
+ "D 285\n",
+ "A 266\n",
+ "Name: count, dtype: int64\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ " 1%| | 11/1172 [00:02<04:41, 4.13it/s]AUTOTUNE bmm(16x256x256, 16x256x256)\n",
+ " bmm 0.0184 ms 100.0% \n",
+ " triton_bmm_44 0.0215 ms 85.7% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=64, B_PROLOGUE_CAST_TYPE=None, EVEN_K=False, GROUP_M=8, num_stages=2, num_warps=4\n",
+ " triton_bmm_48 0.0236 ms 78.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=128, B_PROLOGUE_CAST_TYPE=None, EVEN_K=False, GROUP_M=8, num_stages=4, num_warps=8\n",
+ " triton_bmm_43 0.0246 ms 75.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=64, BLOCK_N=64, B_PROLOGUE_CAST_TYPE=None, EVEN_K=False, GROUP_M=8, num_stages=2, num_warps=4\n",
+ " triton_bmm_47 0.0246 ms 75.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=128, B_PROLOGUE_CAST_TYPE=None, EVEN_K=False, GROUP_M=8, num_stages=3, num_warps=4\n",
+ " triton_bmm_51 0.0246 ms 75.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=128, BLOCK_N=64, B_PROLOGUE_CAST_TYPE=None, EVEN_K=False, GROUP_M=8, num_stages=3, num_warps=4\n",
+ " triton_bmm_53 0.0256 ms 72.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=128, BLOCK_N=128, B_PROLOGUE_CAST_TYPE=None, EVEN_K=False, GROUP_M=8, num_stages=2, num_warps=8\n",
+ " triton_bmm_41 0.0266 ms 69.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=32, B_PROLOGUE_CAST_TYPE=None, EVEN_K=False, GROUP_M=8, num_stages=5, num_warps=8\n",
+ " triton_bmm_52 0.0266 ms 69.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=128, BLOCK_N=64, B_PROLOGUE_CAST_TYPE=None, EVEN_K=False, GROUP_M=8, num_stages=4, num_warps=8\n",
+ " triton_bmm_40 0.0276 ms 66.7% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=64, B_PROLOGUE_CAST_TYPE=None, EVEN_K=False, GROUP_M=8, num_stages=5, num_warps=8\n",
+ "SingleProcess AUTOTUNE benchmarking takes 2.1609 seconds and 0.0407 seconds precompiling\n",
+ "AUTOTUNE bmm(16x256x256, 16x256x256)\n",
+ " bmm 0.0154 ms 100.0% \n",
+ " triton_bmm_71 0.0266 ms 57.7% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=128, BLOCK_N=64, B_PROLOGUE_CAST_TYPE=None, EVEN_K=False, GROUP_M=8, num_stages=4, num_warps=8\n",
+ " triton_bmm_63 0.0276 ms 55.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=64, B_PROLOGUE_CAST_TYPE=None, EVEN_K=False, GROUP_M=8, num_stages=2, num_warps=4\n",
+ " triton_bmm_67 0.0276 ms 55.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=128, B_PROLOGUE_CAST_TYPE=None, EVEN_K=False, GROUP_M=8, num_stages=4, num_warps=8\n",
+ " triton_bmm_62 0.0287 ms 53.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=64, BLOCK_N=64, B_PROLOGUE_CAST_TYPE=None, EVEN_K=False, GROUP_M=8, num_stages=2, num_warps=4\n",
+ " triton_bmm_70 0.0297 ms 51.7% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=128, BLOCK_N=64, B_PROLOGUE_CAST_TYPE=None, EVEN_K=False, GROUP_M=8, num_stages=3, num_warps=4\n",
+ " triton_bmm_75 0.0297 ms 51.7% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=128, BLOCK_N=128, B_PROLOGUE_CAST_TYPE=None, EVEN_K=False, GROUP_M=8, num_stages=5, num_warps=8\n",
+ " triton_bmm_60 0.0307 ms 50.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=32, B_PROLOGUE_CAST_TYPE=None, EVEN_K=False, GROUP_M=8, num_stages=5, num_warps=8\n",
+ " triton_bmm_72 0.0307 ms 50.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=128, BLOCK_N=128, B_PROLOGUE_CAST_TYPE=None, EVEN_K=False, GROUP_M=8, num_stages=2, num_warps=8\n",
+ " triton_bmm_59 0.0328 ms 46.9% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=64, B_PROLOGUE_CAST_TYPE=None, EVEN_K=False, GROUP_M=8, num_stages=5, num_warps=8\n",
+ "SingleProcess AUTOTUNE benchmarking takes 2.1774 seconds and 0.0040 seconds precompiling\n",
+ "100%|██████████| 1172/1172 [05:08<00:00, 3.80it/s]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "ANS\n",
+ "B 312\n",
+ "C 308\n",
+ "A 294\n",
+ "D 258\n",
+ "Name: count, dtype: int64\n",
+ "Accuracy: 0.7782\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": {
+ "id": "6vmG3Z92410E",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 393,
+ "referenced_widgets": [
+ "60aebf3bff6e49cc9344bdc31fd07826",
+ "2764325cf16f47728d2c9862a64a5e17",
+ "94cbb8aaa09f46d0af6e4864cceca115",
+ "cea6792c214641ca93b74d28f08ec47b",
+ "085cd5d3c198427ca2c0465e5f72f077",
+ "112e2d09d9e841469802034dea90cd8d",
+ "53876c5743df4d11aca4a0b067d657e8",
+ "1767f9dee16e4925b10144b332fdd21f",
+ "26e9cb384777480da4eac7a82161d6ee",
+ "4d71787cfa1f424c9c5835011d8b6303",
+ "cbdf1179fe3f44c992ef976ab3cefae3",
+ "75e38d2b8db24e3aab999a3f4f2c514b",
+ "4b5a7d9257574b7199f5273eb57d8a83",
+ "e7c2c2633e3b4e91b40aab7cd9be4c02",
+ "47f7ebdd82de46758bc1b2abcaea64b7",
+ "78b56574dc7b45fcb16cca75ee622126",
+ "6d35db72d9b74d56b7a87eff74b4ddde",
+ "ee09dc671b6248c1b8527ada2d132e86",
+ "7d0ae23289dc4b2692b8825a5b6a878a",
+ "f027de5286854672af52f09a4b3ea401",
+ "9f8f571564224fe0a18aac2a588c6514",
+ "ce9cbfbfbee141f0bcce4aa69ca7159e"
+ ]
+ },
+ "outputId": "7bb1dd9d-4ccc-4981-8f9f-f29975567c76"
+ },
+ "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": "60aebf3bff6e49cc9344bdc31fd07826"
+ }
+ },
+ "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": "75e38d2b8db24e3aab999a3f4f2c514b"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "2376\n",
+ "Average 'tok' value: 60.46590909090909\n",
+ "Max 'tok' value: 193\n",
+ "Output\n",
+ "C 633\n",
+ "A 596\n",
+ "B 585\n",
+ "D 561\n",
+ "E 1\n",
+ "Name: count, dtype: int64\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 2376/2376 [09:38<00:00, 4.11it/s]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "ANS\n",
+ "C 639\n",
+ "A 618\n",
+ "B 577\n",
+ "D 542\n",
+ "Name: count, dtype: int64\n",
+ "Accuracy: 0.9545\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\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": {
+ "id": "aFPK7wPX5TN7",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 463,
+ "referenced_widgets": [
+ "14f21b16ff21495b9ac17536ddbed244",
+ "d07788be45a8401f9389312ff0af59ac",
+ "8a064f658c8d4df28e55f38d82e186e9",
+ "e8d1010dcc7642d3b2e948b73f0eb3d1",
+ "53ca7189bb3e4c7382d404f248796e2c",
+ "1adbcf3b8288407180a1ddf5430b1dce",
+ "f4313b6fe2a243c29b3e449e901ed83b",
+ "b2ed1737b8ad484a9e303f2f5d551590",
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+ "01b74603195a4700be4b4504ad766da3",
+ "f52c63ae110c49bda9a598e802ee66ec",
+ "4b047d75f4714270947894809abab4a3",
+ "4c9d2d41825343d08903d872aa6d883f",
+ "873a953abf124724a222faff6c7d4e2b",
+ "7daf98594bcb4450a385cca12081789a",
+ "c84e6a2548634dc3b26fc2deea141d6c",
+ "7a1a71012b344a57aa8642b57b3f6d22",
+ "829fb1c8819e4dac977d8fbf96b2c751",
+ "576e4b78700e4206afbbc32a80b87550",
+ "18f12985ef3d48afa705218127e1e80a",
+ "64281102cdaf4f76a88f48bd6f496c51",
+ "f9857eefc0b04fb88f0ea90174f5db1b"
+ ]
+ },
+ "outputId": "10228668-278f-4d04-b868-091e2c19ed21"
+ },
+ "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": "14f21b16ff21495b9ac17536ddbed244"
+ }
+ },
+ "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": "4b047d75f4714270947894809abab4a3"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "2376\n",
+ "Average 'tok' value: 99.67003367003367\n",
+ "Max 'tok' value: 650\n",
+ "Output\n",
+ "C 610\n",
+ "A 570\n",
+ "B 563\n",
+ "D 535\n",
+ "4 26\n",
+ "1 26\n",
+ "3 23\n",
+ "2 22\n",
+ "E 1\n",
+ "Name: count, dtype: int64\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 2376/2376 [09:39<00:00, 4.10it/s]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "ANS\n",
+ "A 651\n",
+ "C 614\n",
+ "B 581\n",
+ "D 530\n",
+ "Name: count, dtype: int64\n",
+ "Accuracy: 0.9032\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": {
+ "id": "FtThThQC8hs2",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 376,
+ "referenced_widgets": [
+ "4f40a19ac2ac4d35ad0dc89cb935d2ae",
+ "3e0c4fe7eea745a1af228fa103013002",
+ "278d5ab1512a4e8dbc2a76abb2fcff43",
+ "fd453675199f401593b1f1638e9f059e",
+ "8a032b97c80e452eb15373ee7b4a2922",
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+ "340a888c60c64e9da5e54219ab1bc6ec",
+ "f807932417114f5383e2e38307b7ef0a",
+ "48ce807c77c14c60b61fb860a5ef75b1",
+ "1e6527da797944ac9c9b7000d017ee04",
+ "424d8dde35904d18add7a74aa2fa20e2",
+ "0674146ad76541289bb7c28ca9e3e7f3",
+ "170d7853485d44f2832961456e77b3c3",
+ "7f81bb6331ab4776911eebf77b37d1d3",
+ "eaf1f4047f8247dea7bd613cfd9941bd",
+ "089ac7da0d734afeb6bdf0615a9bc3e5",
+ "40a02d7b78544e8a8bcdbb3b9da834ad",
+ "4e845114bbfd4b7caf309437d39a3afd",
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+ ]
+ },
+ "outputId": "811db57b-f514-4e7d-b82f-f5cbd21e84a0"
+ },
+ "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": "4f40a19ac2ac4d35ad0dc89cb935d2ae"
+ }
+ },
+ "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": "48ce807c77c14c60b61fb860a5ef75b1"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "14042\n",
+ "Average 'tok' value: 106.63509471585245\n",
+ "Max 'tok' value: 1009\n",
+ "Output\n",
+ "D 3776\n",
+ "C 3582\n",
+ "B 3462\n",
+ "A 3222\n",
+ "Name: count, dtype: int64\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 14042/14042 [57:19<00:00, 4.08it/s]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "ANS\n",
+ "B 3903\n",
+ "A 3435\n",
+ "D 3384\n",
+ "C 3320\n",
+ "Name: count, dtype: int64\n",
+ "Accuracy: 0.6877\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\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": [
+ "5dba8642890e4d909fe923c81c511e97",
+ "2cbb2f42b41144cf8a776fd5a6490d34",
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+ "f1d73a5908514a6db9cc0449bd181d94",
+ "b3351a989d72474b80269db78d2884a9",
+ "e4993a3e1cc14c3d89acbbd3f6f058ba",
+ "6248e292eb2a4436b0a99bd09ad5d782",
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+ ]
+ },
+ "outputId": "3f271918-3406-4ed1-99c1-3b8502cb1929"
+ },
+ "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": "5dba8642890e4d909fe923c81c511e97"
+ }
+ },
+ "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": "77022b99e22a4dbc8fb59f37b0de1299"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "14042\n",
+ "Average 'tok' value: 180.577624270047\n",
+ "Max 'tok' value: 1917\n",
+ "Output\n",
+ "D 3776\n",
+ "C 3582\n",
+ "B 3462\n",
+ "A 3222\n",
+ "Name: count, dtype: int64\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 14042/14042 [57:03<00:00, 4.10it/s]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "ANS\n",
+ "B 4446\n",
+ "A 3928\n",
+ "C 2907\n",
+ "D 2761\n",
+ "Name: count, dtype: int64\n",
+ "Accuracy: 0.5400\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": [
+ "7b189b3fe9d84eb880b0d6e5d206c21d",
+ "9c2032c8eaf8426dae32cafa944860c1",
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+ "9e6bd05a17ec408e9295f2ddd474b9f9",
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+ ]
+ },
+ "outputId": "a2f360f3-c5c4-42c0-c382-6e2280f372db"
+ },
+ "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": "7b189b3fe9d84eb880b0d6e5d206c21d"
+ }
+ },
+ "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": "390e5e8bcd274f33b75c5f147b26a998"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "3270\n",
+ "Average 'tok' value: 138.53669724770643\n",
+ "Max 'tok' value: 1248\n",
+ "Output\n",
+ "True 2033\n",
+ "False 1237\n",
+ "Name: count, dtype: int64\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 3270/3270 [13:14<00:00, 4.11it/s]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "ANS\n",
+ "True 2057\n",
+ "False 1213\n",
+ "Name: count, dtype: int64\n",
+ "Accuracy: 0.8685\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\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": [
+ "2478e7ba40fc4a40a980715f905108cf",
+ "98be3919ff6e4768ba47923fe433fbc3",
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+ "da1a466dedd44e83ab0608b7b79bd352",
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+ ]
+ },
+ "outputId": "be263dd2-2c44-4b03-eff1-8ecaa04eb66f"
+ },
+ "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": "2478e7ba40fc4a40a980715f905108cf"
+ }
+ },
+ "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": "8808e9e8458c4db19bada79327ef1610"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "3270\n",
+ "Average 'tok' value: 250.7406727828746\n",
+ "Max 'tok' value: 22950\n",
+ "Output\n",
+ "True 2033\n",
+ "False 1237\n",
+ "Name: count, dtype: int64\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 3269/3269 [13:14<00:00, 4.11it/s]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "ANS\n",
+ "True 2205\n",
+ "False 1064\n",
+ "Name: count, dtype: int64\n",
+ "Accuracy: 0.8492\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": [
+ "45ffe00f803048c28e3c945430b26634",
+ "346f55ed5b15430a9791dd76267ab4b4",
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+ "b3f39d102ae34efd930fd1410932ebf7",
+ "bdb624655a3a41489f0db1f8e9359a69"
+ ]
+ },
+ "outputId": "c01e2590-e1cb-4f1c-b26b-c4258e58f563"
+ },
+ "id": "K4gKxj8ZRdYS",
+ "execution_count": 14,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "ContextMCQ_E.csv: 0%| | 0.00/1.63M [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "45ffe00f803048c28e3c945430b26634"
+ }
+ },
+ "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": "596f92fb2103400bad6e96a71876bb0f"
+ }
+ },
+ "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 [04:04<00:00, 4.08it/s]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "ANS\n",
+ "C 265\n",
+ "A 264\n",
+ "B 242\n",
+ "D 229\n",
+ "Name: count, dtype: int64\n",
+ "Accuracy: 0.8750\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": "57db214993bb4d539ce5ff540eb3567f"
+ }
+ },
+ "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": "804bcc27a9ab44ecab6873b464bc9388"
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+ },
+ "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 [04:08<00:00, 4.02it/s]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "ANS\n",
+ "C 270\n",
+ "D 258\n",
+ "A 243\n",
+ "B 229\n",
+ "Name: count, dtype: int64\n",
+ "Accuracy: 0.7980\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": "6e8d11a9-c31e-403c-b1da-e1315f3c82c7"
+ },
+ "id": "YI3SR_t1Vk2s",
+ "execution_count": 16,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "BOOLQ ENGLISH : 0.8685015290519877\n",
+ "BOOLQ HINDI : 0.8491893545426736\n",
+ "C-MCQ ENGLISH : 0.875\n",
+ "C-MCQ HINDI : 0.798\n",
+ "MMMLU ENGLISH : 0.6876513317191283\n",
+ "MMMLU HINDI : 0.5399515738498789\n",
+ "ARC-E ENGLISH : 0.9545454545454546\n",
+ "ARC-E HINDI : 0.9031986531986532\n",
+ "ARC-C ENGLISH : 0.8754266211604096\n",
+ "ARC-C HINDI : 0.7781569965870307\n",
+ "AVG SCORE : HINDI : 0.7736993156356473\n",
+ "AVG SCORE : ENGLISH : 0.852224987295396\n",
+ "TOT AVG SCORE : 0.8129621514655216\n",
+ "CLICK CTRl+S and wait for 2 sec\n",
+ "1) NOTEBOOK NAME SHOULD BE : GEMMA-9B-B20.ipynb\n",
+ "2) ADD THE CODE TO GITHUB @ https://github.com/1-800-SHARED-TASKS/New-Language-Adaptation/tree/main/Our-Evals/ALL-EVALS/ \n",
+ "3) UPDATE THE GOOGLE SHEET WITH THE SCORES \n"
+ ]
+ }
+ ]
+ },
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