{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "3efa01ce-380d-4730-9ee2-3dcc6efdb61d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting huggingface_hub\n", " Downloading huggingface_hub-0.27.1-py3-none-any.whl.metadata (13 kB)\n", "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (3.9.0)\n", "Collecting fsspec>=2023.5.0 (from huggingface_hub)\n", " Downloading fsspec-2024.12.0-py3-none-any.whl.metadata (11 kB)\n", "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (23.2)\n", "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (6.0.1)\n", "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2.31.0)\n", "Collecting tqdm>=4.42.1 (from huggingface_hub)\n", " Downloading tqdm-4.67.1-py3-none-any.whl.metadata (57 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.7/57.7 kB\u001b[0m \u001b[31m1.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n", "\u001b[?25hRequirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.4.0)\n", "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2.1.1)\n", "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.4)\n", "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (1.26.13)\n", "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2022.12.7)\n", "Downloading huggingface_hub-0.27.1-py3-none-any.whl (450 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m450.7/450.7 kB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n", "\u001b[?25hDownloading fsspec-2024.12.0-py3-none-any.whl (183 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m183.9/183.9 kB\u001b[0m \u001b[31m39.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hDownloading tqdm-4.67.1-py3-none-any.whl (78 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m78.5/78.5 kB\u001b[0m \u001b[31m33.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hInstalling collected packages: tqdm, fsspec, huggingface_hub\n", " Attempting uninstall: fsspec\n", " Found existing installation: fsspec 2023.4.0\n", " Uninstalling fsspec-2023.4.0:\n", " Successfully uninstalled fsspec-2023.4.0\n", "Successfully installed fsspec-2024.12.0 huggingface_hub-0.27.1 tqdm-4.67.1\n", "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", "\u001b[0m\n", "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.0\u001b[0m\n", "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n" ] } ], "source": [ "!pip install huggingface_hub" ] }, { "cell_type": "code", "execution_count": 2, "id": "0c24ca36-1782-4ce8-8094-6f6528dada19", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 17, "referenced_widgets": [ "68f99e9e99d349b98e4e5436f543329c", "0b5c5a83d23340afbf77ee40cfffbe7c", "4fb2bb7d8e714a579cd6915a2a8396e4", "843ba283146140a58d3bf5e8a61c38b8", "3c2559e87d9f46e1bb9ae66375d076f1", "541f45e6f89d40fdb0a72066158b6235", "5cb6809ec4aa4c9aaad1c3d3c11afa59", "a114fbacd4ec49278995ace49cdd8b3c", "9f7dd2f5346b4e95b016d7af1fad6914", "b575b715f2ba4659b6cdd972d754c5a5", "3b7556eab25842db92355ee11a500d12", "9bcda41c40814728832ffb579978ae00", "0335211b70bf45a8b94abffbf726a23c", "0d4db7411983442484fcdbb3946d7a73", "7382eb2578094f1b96581b2d8b4285c5", "0c1c729cb61b4ace99a6dad6c923b133", "d47b30601b17402dabcda7cf9f869b05", "b9c41cba0f584518bcacc3b0c3a19b43", "755df57c90504508ac48b2e4d9c4ee9a", "10b5fc5c8fd84322a48e50509f82b79a" ] }, "id": "0c24ca36-1782-4ce8-8094-6f6528dada19", "outputId": "5adc2644-fa5b-4bf1-af14-971fec8f5fea" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "cfd885fa55ef460995100a3ff4da830a", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value='
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async-timeout, aiohappyeyeballs, typeguard, pandas, nvidia-cusparse-cu12, nvidia-cudnn-cu12, multiprocess, multidict, markdown-it-py, aiosignal, yarl, tokenizers, rich, nvidia-cusolver-cu12, tyro, transformers, torch, aiohttp, xformers, cut_cross_entropy, bitsandbytes, accelerate, peft, datasets, trl, unsloth_zoo, unsloth\n", " Attempting uninstall: wheel\n", " Found existing installation: wheel 0.41.3\n", " Uninstalling wheel-0.41.3:\n", " Successfully uninstalled wheel-0.41.3\n", " Attempting uninstall: typing-extensions\n", " Found existing installation: typing_extensions 4.4.0\n", " Uninstalling typing_extensions-4.4.0:\n", " Successfully uninstalled typing_extensions-4.4.0\n", " Attempting uninstall: triton\n", " Found existing installation: triton 2.1.0\n", " Uninstalling triton-2.1.0:\n", " Successfully uninstalled triton-2.1.0\n", " Attempting uninstall: sympy\n", " Found existing installation: sympy 1.12\n", " Uninstalling sympy-1.12:\n", " Successfully uninstalled sympy-1.12\n", " Attempting uninstall: requests\n", " Found existing installation: requests 2.31.0\n", " Uninstalling requests-2.31.0:\n", " Successfully uninstalled requests-2.31.0\n", " Attempting uninstall: fsspec\n", " Found existing installation: fsspec 2024.12.0\n", " Uninstalling fsspec-2024.12.0:\n", " Successfully uninstalled fsspec-2024.12.0\n", " Attempting uninstall: torch\n", " Found existing installation: torch 2.1.0+cu118\n", " Uninstalling torch-2.1.0+cu118:\n", " Successfully uninstalled torch-2.1.0+cu118\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", "torchaudio 2.1.0+cu118 requires torch==2.1.0, but you have torch 2.5.1 which is incompatible.\n", "torchvision 0.16.0+cu118 requires torch==2.1.0, but you have torch 2.5.1 which is incompatible.\u001b[0m\u001b[31m\n", "\u001b[0mSuccessfully installed accelerate-1.3.0 aiohappyeyeballs-2.4.4 aiohttp-3.11.11 aiosignal-1.3.2 async-timeout-5.0.1 bitsandbytes-0.45.1 cut_cross_entropy-25.1.1 datasets-3.2.0 dill-0.3.8 docstring-parser-0.16 frozenlist-1.5.0 fsspec-2024.9.0 hf_transfer-0.1.9 markdown-it-py-3.0.0 mdurl-0.1.2 multidict-6.1.0 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-nccl-cu12-2.21.5 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.4.127 pandas-2.2.3 peft-0.14.0 propcache-0.2.1 protobuf-3.20.3 pyarrow-19.0.0 pytz-2024.2 regex-2024.11.6 requests-2.32.3 rich-13.9.4 safetensors-0.5.2 sentencepiece-0.2.0 shtab-1.7.1 sympy-1.13.1 tokenizers-0.21.0 torch-2.5.1 transformers-4.48.1 triton-3.1.0 trl-0.13.0 typeguard-4.4.1 typing-extensions-4.12.2 tyro-0.9.13 tzdata-2025.1 unsloth-2025.1.6 unsloth_zoo-2025.1.5 wheel-0.45.1 xformers-0.0.29.post1 xxhash-3.5.0 yarl-1.18.3\n", "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", "\u001b[0m\n", "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.0\u001b[0m\n", "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n", "Collecting git+https://github.com/unslothai/unsloth.git\n", " Cloning https://github.com/unslothai/unsloth.git to /tmp/pip-req-build-9qxqishr\n", " Running command git clone --filter=blob:none --quiet https://github.com/unslothai/unsloth.git /tmp/pip-req-build-9qxqishr\n", " Resolved https://github.com/unslothai/unsloth.git to commit bdf0cd6033595be4e7ed23d0d002bb176d343152\n", " Installing build dependencies ... \u001b[?25ldone\n", "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n", "\u001b[?25hBuilding wheels for collected packages: unsloth\n", " Building wheel for unsloth (pyproject.toml) ... \u001b[?25ldone\n", "\u001b[?25h Created wheel for unsloth: filename=unsloth-2025.1.7-py3-none-any.whl size=174896 sha256=253ba2c1a84803331d58627ccd02f1010f226c7bbca68a55627d828638e9dd48\n", " Stored in directory: /tmp/pip-ephem-wheel-cache-5jp0uhfb/wheels/ed/d4/e9/76fb290ee3df0a5fc21ce5c2c788e29e9607a2353d8342fd0d\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", "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", "\u001b[0m\n", "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.0\u001b[0m\n", "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n" ] } ], "source": [ "!pip install unsloth\n", "!pip install --force-reinstall --no-cache-dir --no-deps git+https://github.com/unslothai/unsloth.git" ] }, { "cell_type": "code", "execution_count": 4, "id": "fbc9900d-28d2-4bda-9848-b572fbe778d2", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 511, "referenced_widgets": [ "4f85431cdf544cf4bb6efa09e37830b8", "9ed80534137246e48854e08e7e9a94cf", "1b1e48bdf1c04513a5781b561dd0c073", "cdc1baedb4d04f07b6f214c70449ec98", "456fb0e0057b41e290fa839df930b102", "76c9df78bcd94816930b89385539e34d", "de50acf50e384900aefe898173e17d58", "97b6388e992b45c88dbe59ed8e3a33fe", "246646b3771e460e8d08b47fcce25e5c", "e48a5f199d0e4fb399b350dafe2ddb71", "31432be57d8f46c9ae7f0e12f632db3a", "2efeaa80fd3e449f95f6d66b386f8120", "dc05a27f7dca4d1a9209d7e063176b21", "4194508eed654972b32f5a0857745443", "307a4db62ea44b09999c3bc792b24043", "5a2078b36cf34d279c8605a9c8ed9efb", "0bc3d38c80da407da118590e94d010fa", "ce2a4d61a62947018394a457231bb963", "ea00c8caddaa4dccaefe33c1b74c0ca5", "85b9589aafab49f2ae723bad8d7d0271", "3695b48e2ad84d56a6960202f6806328", "75639126abae4351bea6036dd7a4807b", "4cb63afd92694b34b02c4d96ba79275b", "0bb8291d9755475eb330ec0eeee4ac75", "3bd2ee06e09549fa8be39503069d8b39", "e9176e2ce1ce4b32867e65f4ee797375", "2b849ffcbf5c466a8b565238200b4c51", "59b46f48c6e64ac4a3950a2490f82380", "5b624c14c54c43458f119ae5d7f83895", "f7a89133de6b44218709d775b9f98327", "ed6575b4322e47228973545f7e45a0ef", "bc3a46fa1df144c3aa6f3dc096b94dc5", "57886f4de1db4c89b2765ab7f38527d3", "f9e6e2db0abe4236984abb9763d3e215", "6a5e2c1871d84c23939e182c39420a3e", "927decca17314f32a9bfe4f4b22df208", "ba5209d608954043a842317652044f9b", "8e7659dc9cde45399f3efe6c171c7bd2", "e849136fd3f8465c97d7f7eb6ac4d13f", "4307044e2f514ec7a9c41b06c55e8e2d", "9cfbf6c37e464b17809876fe236b04e6", "2d3b2be48c7745dbb410ac0af556fa5b", "8f27782eddc24011aeba1ef17d0ca8d4", "970407d152cd4977aa995d56e3596989", "7f09311de54d44568db085b5627323f9", "fb255b59114f4e50b0c9937aba52fe42", "7545e85de0b14c9394431911ba028d4d", "a0deeec5c183480ab55320c21eb79b9c", "973b4316f863490c9c62eaebaf9d7409", "001caa5912264fd0a80ea3be0da73f97", "db7781cc15a2438d8174a7013e8eb09c", "35c812e1115143fc9663088e281022c8", "f3b934a8b104406dbf0a39140455f349", "ea61d57caa55495fa843f8730630ecff", "f54a36ea667c40c3a1c2967d10915975", "0bb47d588a434fabad92c0125e57edf4", "cbf9991aaac44219bf9ffd90764bef80", "6329a4395de648cbac31e553061bf192", "43ef8bbab5b34e219705fb6e6f5c5845", "2818f20ebcdd49849a5fa96597039ae7", "b3c298d639c048138ecf2804b375410f", "a8b54fd8e9474cc7b67ef106b143db9f", "e02d3d8ed4ad43db8b74cbe8365414ad", "e1a371d9714d4452864e568ef124a9ff", "f470ef92cf074250b42226d034e78227", "d4d93a072e54423a997a2258deaa3c43", "b012e56c71c6485ab949691bdd936aab", "95925bb83d0b4b1f9f43800ce6cefcdc", "a5eff12d6a794e68860f1d14443a1f66", "47bdac5003ed456b9f200cde75d7f14e", "696c50ec448f42c9867184ef9768527f", "573f1bc1717347539faf2c74b4678fcb", "0b955400e467423f868172e7786fc13d", "2776314f709340d5862b6bf115a3b184", "6841a62b8ebc42e29c7f04277421be78", "daf6aa574e51484280c7c9b3a56c25dd", "0161aad8e0db4d588f6d34cf8c5d55da", "e6c89ae496544021a818884adde5b932", "5585736680a140a6b0cc0b51c32ca3ed", "e2ac8efb39054a7fa4185fb22e34bf62", "a66971f4bb2d417d909925a887278b6b", "824b54c5278b46b1a06c68d1351ce934", "35813343f30b48ca99b36ff0488d9b57", "c0bb9cd67824489eb5677b3fe768958f", "3b377c82bcf94600950681133fcb6f68", "000cdbf7a2824301b6e1a91a6e4e30b8", "a9222abebc7d42a0ba33806940633321", "5e12251c61db4f1584e93001a18ad251", "25f5d5b1efab4aeaa80c2963203a4630", "972b1f6f453a434c942b7faedf3c20ae", "68251973ac1c4c2bbe3fbeeac07d7bf6", "8710b55ec21e46dd9a4986a9b21f841d", "dfcbd2dbefc7477abfeb5cba70e3b371", "6b8b9e7fd54d41fbbb1e3c19cd671c59", "27f43c40573847bea3dfe25764510f61", "a1edd32161d04cb5b5ff012cb5f7d1ad", "c433e628f13a420aa3246edb1e8ae86e", "bb32484f0bf94db8bc42b5d6db139ce1", "88aea17a86df48dc8246b566126afe12", "7f8ac41c6390494f86501908f2bd35b9", "b7cb1a0624e8419cb2cedb250d479b21", "db6e70c121d540c1a10f9577a4acf612", "4d3ae00041c34471bf2c4398d4782738", "a8f6e268bd79477780f25578ec482cd0", "af5ea249d2eb4343952197645d90d3e8", "3571486ecdb3422284ba4bf34c2e4f90", "35beb2612e144f7283cc4fac073e11a9", "73038c4bad7241949b97235386febebe", "5f2ef68d6844417eb6be1063491e271a", "ea2fd6446bd34943b50f94eb45faebbf" ] }, "id": "fbc9900d-28d2-4bda-9848-b572fbe778d2", "outputId": "c5abc8a1-c904-4057-a241-b4d0875015cc" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n", "🦥 Unsloth Zoo will now patch everything to make training faster!\n", "==((====))== Unsloth 2025.1.7: Fast Llama patching. Transformers: 4.48.1.\n", " \\\\ /| GPU: NVIDIA A100-SXM4-80GB. Max memory: 79.254 GB. Platform: Linux.\n", "O^O/ \\_/ \\ Torch: 2.5.1+cu124. CUDA: 8.0. CUDA Toolkit: 12.4. Triton: 3.1.0\n", "\\ / Bfloat16 = TRUE. FA [Xformers = 0.0.29.post1. FA2 = False]\n", " \"-____-\" Free Apache license: http://github.com/unslothai/unsloth\n", "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "960c554bc34748fe9de4b3861192bbdd", "version_major": 2, "version_minor": 0 }, "text/plain": [ "model.safetensors.index.json: 0%| | 0.00/20.9k [00:00 0 ! Suggested 8, 16, 32, 64, 128\n", " target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n", " \"gate_proj\", \"up_proj\", \"down_proj\",],\n", " lora_alpha = 16,\n", " lora_dropout = 0, # Supports any, but = 0 is optimized\n", " bias = \"none\", # Supports any, but = \"none\" is optimized\n", " # [NEW] \"unsloth\" uses 30% less VRAM, fits 2x larger batch sizes!\n", " use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for very long context\n", " random_state = 3407,\n", " use_rslora = False, # We support rank stabilized LoRA\n", " loftq_config = None, # And LoftQ\n", ")" ] }, { "cell_type": "code", "execution_count": 6, "id": "35603404-a7a0-4f24-92de-fd778500df99", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "35603404-a7a0-4f24-92de-fd778500df99", "outputId": "691b5670-a5b4-44b5-d44a-fbcd2563710a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: datasets in /usr/local/lib/python3.10/dist-packages (3.2.0)\n", "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (4.67.1)\n", "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.9.0)\n", "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.24.1)\n", "Requirement already satisfied: pyarrow>=15.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (19.0.0)\n", "Requirement already satisfied: dill<0.3.9,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.3.8)\n", "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (2.2.3)\n", "Requirement already satisfied: requests>=2.32.2 in /usr/local/lib/python3.10/dist-packages (from datasets) (2.32.3)\n", "Requirement already satisfied: xxhash in /usr/local/lib/python3.10/dist-packages (from datasets) (3.5.0)\n", "Requirement already satisfied: multiprocess<0.70.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.70.16)\n", "Requirement already satisfied: fsspec<=2024.9.0,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from fsspec[http]<=2024.9.0,>=2023.1.0->datasets) (2024.9.0)\n", "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.11.11)\n", "Requirement already satisfied: huggingface-hub>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.27.1)\n", "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets) (23.2)\n", "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (6.0.1)\n", "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (2.4.4)\n", "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.2)\n", "Requirement already satisfied: async-timeout<6.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (5.0.1)\n", "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (23.1.0)\n", "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.5.0)\n", "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (6.1.0)\n", "Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (0.2.1)\n", "Requirement already satisfied: yarl<2.0,>=1.17.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.18.3)\n", "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.23.0->datasets) (4.12.2)\n", "Requirement already satisfied: charset-normalizer<4,>=2 in 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python-dateutil>=2.8.2->pandas->datasets) (1.16.0)\n", "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", "\u001b[0m\n", "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.0\u001b[0m\n", "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n" ] } ], "source": [ "!pip install datasets tqdm\n", "import pandas as pd\n", "from datasets import load_dataset\n", "import os" ] }, { "cell_type": "code", "execution_count": 7, "id": "2c49fafe-1e9a-4e29-8d85-94ff79f8c9c0", "metadata": { "id": "2c49fafe-1e9a-4e29-8d85-94ff79f8c9c0" }, "outputs": [], "source": [ "FastLanguageModel.for_inference(model)\n", "from tqdm import tqdm\n", "tqdm.pandas()" ] }, { "cell_type": "code", "execution_count": 8, "id": "1749c745-d1fb-430b-9469-4913bb2a6cb5", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 441 }, "id": "1749c745-d1fb-430b-9469-4913bb2a6cb5", "outputId": "536023ca-6ee0-4b8b-a41c-d4ac450e8466" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1cf38cbcd3d449c9b76de838b24f42c3", "version_major": 2, "version_minor": 0 }, "text/plain": [ "MMMLU_H.csv: 0%| | 0.00/16.6M [00:00\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
InputOutputSub-DomainDataset
0Q पर दिए गए क्षेत्र विस्तार Q(sqrt(2), sqrt(3)...Babstract_algebraMMLU
1मान लीजिए S_5 में p = (1, 2, 5, 4)(2, 3) है. S...Cabstract_algebraMMLU
2दिए गए बहुपद के संकेतित परिमित क्षेत्र में गुण...Dabstract_algebraMMLU
3कथन 1 | नॉन-एबेलियन ग्रुप का एक फ़ैक्टर ग्रुप ...Babstract_algebraMMLU
4दिए गए बहुपद रिंग में दिए गए बहुपदों का गुणनफल...Babstract_algebraMMLU
...............
14037चीन और कोरिया में धार्मिक परम्पराओं का मुख्य क...Aworld_religionsMMLU
14038हान राजवंश के समय सूखे के दौरान आम लोग किससे अ...Cworld_religionsMMLU
14039धर्मशास्त्रीय शब्द होमोओसिओस का अर्थ निम्नलिखि...Bworld_religionsMMLU
14040जापानी मूल मिथक के अनुसार, अमातरासु को किसने अ...Bworld_religionsMMLU
14041ऑगस्टस का नुमेन निम्नलिखित में से किस विशेषता ...Aworld_religionsMMLU
\n", "

14042 rows × 4 columns

\n", "" ], "text/plain": [ " Input Output \\\n", "0 Q पर दिए गए क्षेत्र विस्तार Q(sqrt(2), sqrt(3)... B \n", "1 मान लीजिए S_5 में p = (1, 2, 5, 4)(2, 3) है. S... C \n", "2 दिए गए बहुपद के संकेतित परिमित क्षेत्र में गुण... D \n", "3 कथन 1 | नॉन-एबेलियन ग्रुप का एक फ़ैक्टर ग्रुप ... B \n", "4 दिए गए बहुपद रिंग में दिए गए बहुपदों का गुणनफल... B \n", "... ... ... \n", "14037 चीन और कोरिया में धार्मिक परम्पराओं का मुख्य क... A \n", "14038 हान राजवंश के समय सूखे के दौरान आम लोग किससे अ... C \n", "14039 धर्मशास्त्रीय शब्द होमोओसिओस का अर्थ निम्नलिखि... B \n", "14040 जापानी मूल मिथक के अनुसार, अमातरासु को किसने अ... B \n", "14041 ऑगस्टस का नुमेन निम्नलिखित में से किस विशेषता ... A \n", "\n", " Sub-Domain Dataset \n", "0 abstract_algebra MMLU \n", "1 abstract_algebra MMLU \n", "2 abstract_algebra MMLU \n", "3 abstract_algebra MMLU \n", "4 abstract_algebra MMLU \n", "... ... ... \n", "14037 world_religions MMLU \n", "14038 world_religions MMLU \n", "14039 world_religions MMLU \n", "14040 world_religions MMLU \n", "14041 world_religions MMLU \n", "\n", "[14042 rows x 4 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "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" ] }, { "cell_type": "code", "execution_count": 9, "id": "251f9198-60d4-4ec1-88a8-f344234be2de", "metadata": { "id": "251f9198-60d4-4ec1-88a8-f344234be2de" }, "outputs": [], "source": [ "df['tok'] = df['Input'].apply(lambda x: len(tokenizer.encode(x)))" ] }, { "cell_type": "code", "execution_count": 10, "id": "dbffda0c-e536-4891-8862-e1e5936eb6be", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "dbffda0c-e536-4891-8862-e1e5936eb6be", "outputId": "8f504fe6-28bf-4e3f-b3ab-9dcf8be8a4f7" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Average 'tok' value: 243.72283150548355\n", "Max 'tok' value: 2679\n" ] } ], "source": [ "print(f\"Average 'tok' value: {df['tok'].mean()}\")\n", "print(f\"Max 'tok' value: {df['tok'].max()}\")" ] }, { "cell_type": "code", "execution_count": 11, "id": "9409ad7f-697c-4533-8d2f-399928ca9033", "metadata": { "id": "9409ad7f-697c-4533-8d2f-399928ca9033" }, "outputs": [], "source": [ "df = df.sort_values('tok', ascending=False)" ] }, { "cell_type": "code", "execution_count": 12, "id": "26544979-f1f2-4622-be64-9f8559a99460", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "26544979-f1f2-4622-be64-9f8559a99460", "outputId": "5471b472-a696-41b1-e2da-fe0a7d3b69df" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", "To disable this warning, you can either:\n", "\t- Avoid using `tokenizers` before the fork if possible\n", "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Collecting matplotlib\n", " Downloading matplotlib-3.10.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB)\n", "Collecting contourpy>=1.0.1 (from matplotlib)\n", " Downloading contourpy-1.3.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.4 kB)\n", "Collecting cycler>=0.10 (from matplotlib)\n", " Downloading cycler-0.12.1-py3-none-any.whl.metadata (3.8 kB)\n", "Collecting fonttools>=4.22.0 (from matplotlib)\n", " Downloading fonttools-4.55.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (166 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m166.1/166.1 kB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n", "\u001b[?25hCollecting kiwisolver>=1.3.1 (from matplotlib)\n", " Downloading kiwisolver-1.4.8-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.metadata (6.2 kB)\n", "Requirement already satisfied: numpy>=1.23 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.24.1)\n", "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (23.2)\n", "Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (9.3.0)\n", "Requirement already satisfied: pyparsing>=2.3.1 in /usr/lib/python3/dist-packages (from matplotlib) (2.4.7)\n", "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (2.8.2)\n", "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n", "Downloading matplotlib-3.10.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (8.6 MB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.6/8.6 MB\u001b[0m \u001b[31m40.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n", "\u001b[?25hDownloading contourpy-1.3.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (324 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m325.0/325.0 kB\u001b[0m \u001b[31m88.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hDownloading cycler-0.12.1-py3-none-any.whl (8.3 kB)\n", "Downloading fonttools-4.55.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.6 MB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.6/4.6 MB\u001b[0m \u001b[31m17.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0mm\n", "\u001b[?25hDownloading kiwisolver-1.4.8-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.6 MB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m137.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hInstalling collected packages: kiwisolver, fonttools, cycler, contourpy, matplotlib\n", "Successfully installed contourpy-1.3.1 cycler-0.12.1 fonttools-4.55.6 kiwisolver-1.4.8 matplotlib-3.10.0\n", "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", "\u001b[0m\n", "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.0\u001b[0m\n", "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n" ] } ], "source": [ "!pip install matplotlib" ] }, { "cell_type": "code", "execution_count": 13, "id": "8ce90df6-271e-460b-97fe-30a2480c78b6", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 564 }, "id": "8ce90df6-271e-460b-97fe-30a2480c78b6", "outputId": "179896b5-bf52-439c-f5c8-55a29f70a0b1" }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "plt.figure(figsize=(10, 6))\n", "plt.plot(df['tok'].values, marker='o', linestyle='-')\n", "plt.xlabel('Sorted Entry Index')\n", "plt.ylabel('Token Count')\n", "plt.title(\"Line Plot of Token Counts (Sorted Ascending)\")\n", "plt.grid(True)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 14, "id": "01127bdd-aa73-4118-a5b8-53e5ed709bf5", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 363 }, "id": "01127bdd-aa73-4118-a5b8-53e5ed709bf5", "outputId": "435482de-04cc-4838-a04e-2c050f503392" }, "outputs": [ { "data": { "text/html": [ "
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InputOutputSub-DomainDatasettok
1262सौना का उपयोग करना जिसे \"सौना स्नान\" या सौना ब...Bcollege_medicineMMLU2679
1202सौना का उपयोग करना जिसे \"सौना स्नान\" या सौना ब...Acollege_medicineMMLU2645
1289सौना का उपयोग करना जिसे \"सौना स्नान\" या सौना ब...Acollege_medicineMMLU2630
1246सौना का उपयोग करना जिसे \"सौना स्नान\" या सौना ब...Dcollege_medicineMMLU2549
1358सौना का उपयोग करना जिसे \"सौना स्नान\" या सौना ब...Dcollege_medicineMMLU2546
5938यह प्रश्न निम्नलिखित जानकारी से संबंधित है।\\nस...Ahigh_school_world_historyMMLU1868
5874यह प्रश्न निम्नलिखित जानकारी से संबंधित है।\\nस...Bhigh_school_world_historyMMLU1833
12115साल 1993 में, एक पशुपालक किसान को 20 एकड़ के स...Aprofessional_lawMMLU1821
10876एक भूमि-विकास कंपनी के पास दक्षिणपश्चिम में 40...Cprofessional_lawMMLU1809
11923साल 1993 में, एक ज़मींदार के पास एक अंगूर के ब...Cprofessional_lawMMLU1787
\n", "
" ], "text/plain": [ " Input Output \\\n", "1262 सौना का उपयोग करना जिसे \"सौना स्नान\" या सौना ब... B \n", "1202 सौना का उपयोग करना जिसे \"सौना स्नान\" या सौना ब... A \n", "1289 सौना का उपयोग करना जिसे \"सौना स्नान\" या सौना ब... A \n", "1246 सौना का उपयोग करना जिसे \"सौना स्नान\" या सौना ब... D \n", "1358 सौना का उपयोग करना जिसे \"सौना स्नान\" या सौना ब... D \n", "5938 यह प्रश्न निम्नलिखित जानकारी से संबंधित है।\\nस... A \n", "5874 यह प्रश्न निम्नलिखित जानकारी से संबंधित है।\\nस... B \n", "12115 साल 1993 में, एक पशुपालक किसान को 20 एकड़ के स... A \n", "10876 एक भूमि-विकास कंपनी के पास दक्षिणपश्चिम में 40... C \n", "11923 साल 1993 में, एक ज़मींदार के पास एक अंगूर के ब... C \n", "\n", " Sub-Domain Dataset tok \n", "1262 college_medicine MMLU 2679 \n", "1202 college_medicine MMLU 2645 \n", "1289 college_medicine MMLU 2630 \n", "1246 college_medicine MMLU 2549 \n", "1358 college_medicine MMLU 2546 \n", "5938 high_school_world_history MMLU 1868 \n", "5874 high_school_world_history MMLU 1833 \n", "12115 professional_law MMLU 1821 \n", "10876 professional_law MMLU 1809 \n", "11923 professional_law MMLU 1787 " ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head(10)" ] }, { "cell_type": "code", "execution_count": 15, "id": "5af6bcac-3c98-421b-8f4b-854b57db8416", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 104 }, "id": "5af6bcac-3c98-421b-8f4b-854b57db8416", "outputId": "e21ead62-df42-4fd8-e714-883e8ad960c7" }, "outputs": [ { "data": { "text/plain": [ "'from tqdm import tqdm\\nimport torch\\ndef generate_responses_phi4_batched(df, tokenizer, model, batch_size=8):\\n responses = []\\n for i in tqdm(range(0, len(df), batch_size), desc=\"Processing Batches\"):\\n batch = df[\\'Input\\'][i:i + batch_size].tolist()\\n prompts = [f\"### INPUT : {text} ### MCQ ### OUTPUT :\" for text in batch]\\n messages = [{\"role\": \"user\", \"content\": prompt} for prompt in prompts]\\n inputs = [tokenizer.apply_chat_template([msg], tokenize=True, add_generation_prompt=True, return_tensors=\"pt\") for msg in messages]\\n max_length = max(input_tensor.size(1) for input_tensor in inputs)\\n padded_inputs = []\\n for input_tensor in inputs:\\n pad_size = max_length - input_tensor.size(1)\\n padded_tensor = torch.nn.functional.pad(input_tensor, (0, pad_size), value=tokenizer.pad_token_id)\\n padded_inputs.append(padded_tensor)\\n batch_inputs = torch.cat(padded_inputs, dim=0).to(\"cuda\")\\n outputs = model.generate(input_ids=batch_inputs, max_new_tokens=20, use_cache=True, temperature=0.1, min_p=0.1, pad_token_id=tokenizer.eos_token_id)\\n batch_responses = tokenizer.batch_decode(outputs, skip_special_tokens=True)\\n for response in batch_responses:\\n processed_response = response.split(\"### OUTPUT :\\nassistant\")[-1].strip()\\n responses.append(processed_response)\\n df[\\'Response\\'] = responses\\n return df'" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"\"\"from tqdm import tqdm\n", "import torch\n", "def generate_responses_phi4_batched(df, tokenizer, model, batch_size=8):\n", " responses = []\n", " for i in tqdm(range(0, len(df), batch_size), desc=\"Processing Batches\"):\n", " batch = df['Input'][i:i + batch_size].tolist()\n", " prompts = [f\"### INPUT : {text} ### MCQ ### OUTPUT :\" for text in batch]\n", " messages = [{\"role\": \"user\", \"content\": prompt} for prompt in prompts]\n", " inputs = [tokenizer.apply_chat_template([msg], tokenize=True, add_generation_prompt=True, return_tensors=\"pt\") for msg in messages]\n", " max_length = max(input_tensor.size(1) for input_tensor in inputs)\n", " padded_inputs = []\n", " for input_tensor in inputs:\n", " pad_size = max_length - input_tensor.size(1)\n", " padded_tensor = torch.nn.functional.pad(input_tensor, (0, pad_size), value=tokenizer.pad_token_id)\n", " padded_inputs.append(padded_tensor)\n", " batch_inputs = torch.cat(padded_inputs, dim=0).to(\"cuda\")\n", " outputs = model.generate(input_ids=batch_inputs, max_new_tokens=20, use_cache=True, temperature=0.1, min_p=0.1, pad_token_id=tokenizer.eos_token_id)\n", " batch_responses = tokenizer.batch_decode(outputs, skip_special_tokens=True)\n", " for response in batch_responses:\n", " processed_response = response.split(\"### OUTPUT :\\nassistant\")[-1].strip()\n", " responses.append(processed_response)\n", " df['Response'] = responses\n", " return df\"\"\"" ] }, { "cell_type": "code", "execution_count": 16, "id": "DnE5jnbPTd03", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "DnE5jnbPTd03", "outputId": "cc1121de-5728-456a-d840-843180e509bf" }, "outputs": [ { "data": { "text/html": [ "
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InputOutputSub-DomainDatasettok
2084(2 + 5)^2 − 42 को सरल कीजिए। ### A) 7 B) 16 C...Aelementary_mathematicsMMLU42
8108महामंदी किस वर्ष शुरू हुई? ### A) 1884 B) 192...BmiscellaneousMMLU42
77वलय Z x Z की विशेषता पता करें. ### A) 0 B) 3 ...Aabstract_algebraMMLU42
8009माणिक्य किस रंग का होता है? ### A) लाल B) काल...AmiscellaneousMMLU42
8138गेरी एडम्स किस संगठन के अध्यक्ष हैं? ### A) G...CmiscellaneousMMLU42
2178घटाएँ. 2,396 – 1,709 ### A) 687 B) 687 C) 1,4...Aelementary_mathematicsMMLU42
8005निम्नलिखित में से कौन सा तत्व धातु है? ### A)...DmiscellaneousMMLU41
669पानी का pH मान कितना है? ### A) 3.5 B) 7 C) 1...Bclinical_knowledgeMMLU41
23723 + p = 15 में p बराबर क्या है? ### A) 3 B) 5...Celementary_mathematicsMMLU41
7772इनमें से कौन सा शब्द क्रिया विशेषण है? ### A)...DmiscellaneousMMLU41
2399100 का तीन पांचवा भाग क्या है? ### A) 3 B) 5 ...Delementary_mathematicsMMLU41
242828 x 42 का मान क्या है? ### A) 420 B) 816 C) ...Delementary_mathematicsMMLU41
215942 और 84 का महत्तम समापवर्तक क्या है? ### A) ...Celementary_mathematicsMMLU41
2277कौन सी संख्या 7 का गुणज है? ### A) 27 B) 48 C...Celementary_mathematicsMMLU41
7722इनमें से कौन मसाला नहीं है? ### A) डिल B) सौं...CmiscellaneousMMLU41
13941नये नियम में कितनी पुस्तकें हैं? ### A) 30 B)...Cworld_religionsMMLU41
215736 और 90 का महत्तम समापवर्तक क्या है? ### A) ...Belementary_mathematicsMMLU41
2126एक दशक में कितने वर्ष होते हैं? ### A) 5 B) 1...Belementary_mathematicsMMLU40
22247% किसके बराबर है? ### A) 0.007 B) 0.07 C) 0....Belementary_mathematicsMMLU40
7667लौवर संग्रहालय कहां है? ### A) पेरिस B) ल्योन...AmiscellaneousMMLU40
757581 के वर्ग का वर्गमूल क्या है? ### A) 9 B) 27...CmiscellaneousMMLU40
226725 और 55 का GCD ज्ञात कीजिए। ### A) 5 B) 11 C...Aelementary_mathematicsMMLU39
219924 और 36 का LCM ज्ञात कीजिए। ### A) 96 B) 144...Celementary_mathematicsMMLU39
4358$\\log_3 81$ आकलन करें. ### A) 4 B) 0.25 C) -1...Ahigh_school_mathematicsMMLU39
226830 का 60% क्या है? ### A) 1.8 B) 18 C) 180 D)...Belementary_mathematicsMMLU38
2342सटीक उत्तर खोजें: 800 - 301 ### A) 599 B) 500...Celementary_mathematicsMMLU38
2332सटीक उत्तर खोजें: 942 / 3 ### A) 214 B) 304 C...Celementary_mathematicsMMLU38
21543 बटा 4 * x = 24 हल करें। ### A) 18 B) 32 C) ...Belementary_mathematicsMMLU38
2155सटीक उत्तर खोजें: 110 + 70 ### A) 18 B) 81 C)...Celementary_mathematicsMMLU38
2437सटीक उत्तर खोजें: 365 + 56 ### A) 300 B) 309 ...Belementary_mathematicsMMLU38
2432समीकरण 18 + p = 29 को हल करें। ### A) −47 B) ...Celementary_mathematicsMMLU38
2345समीकरण v − 26 = 68 को हल करें। ### A) −42 B) ...Celementary_mathematicsMMLU38
240452 + 6 * 2 की गणना करें। ### A) 116 B) 64 C) ...Belementary_mathematicsMMLU38
7585बल = द्रव्यमान = क्या? ### A) वेग B) दूरी C) ...CmiscellaneousMMLU37
21508 + 8 ÷ 2 + 2 = ### A) 4 B) 8 C) 10 D) 14 ###...Delementary_mathematicsMMLU36
2316समीकरण x + 71 = −22 हल करें। ### A) −93 B) −4...Aelementary_mathematicsMMLU36
2306समीकरण 14 = w + 23 हल करें। ### A) −37 B) −9 ...Belementary_mathematicsMMLU36
2293समीकरण −47 = g + 24 हल करें। ### A) -71 B) -2...Belementary_mathematicsMMLU36
2112Compute 22 / 2 + 9. ### A) 10 B) 11 C) 20 D) ...Celementary_mathematicsMMLU34
2087−4 + ( −3 )= ### A) −7 B) −1 C) 1 D) 7 ### M...Aelementary_mathematicsMMLU33
2983एंटीमोनी का प्रतीक … है ### A) W B) Sb C) Fe ...Bhigh_school_chemistryMMLU33
1190(1+i)^10 = ### A) 1 B) i C) 32 D) 32i ### MCQ...Dcollege_mathematicsMMLU30
\n", "
" ], "text/plain": [ " Input Output \\\n", "2084 (2 + 5)^2 − 42 को सरल कीजिए। ### A) 7 B) 16 C... A \n", "8108 महामंदी किस वर्ष शुरू हुई? ### A) 1884 B) 192... B \n", "77 वलय Z x Z की विशेषता पता करें. ### A) 0 B) 3 ... A \n", "8009 माणिक्य किस रंग का होता है? ### A) लाल B) काल... A \n", "8138 गेरी एडम्स किस संगठन के अध्यक्ष हैं? ### A) G... C \n", "2178 घटाएँ. 2,396 – 1,709 ### A) 687 B) 687 C) 1,4... A \n", "8005 निम्नलिखित में से कौन सा तत्व धातु है? ### A)... D \n", "669 पानी का pH मान कितना है? ### A) 3.5 B) 7 C) 1... B \n", "2372 3 + p = 15 में p बराबर क्या है? ### A) 3 B) 5... C \n", "7772 इनमें से कौन सा शब्द क्रिया विशेषण है? ### A)... D \n", "2399 100 का तीन पांचवा भाग क्या है? ### A) 3 B) 5 ... D \n", "2428 28 x 42 का मान क्या है? ### A) 420 B) 816 C) ... D \n", "2159 42 और 84 का महत्तम समापवर्तक क्या है? ### A) ... C \n", "2277 कौन सी संख्या 7 का गुणज है? ### A) 27 B) 48 C... C \n", "7722 इनमें से कौन मसाला नहीं है? ### A) डिल B) सौं... C \n", "13941 नये नियम में कितनी पुस्तकें हैं? ### A) 30 B)... C \n", "2157 36 और 90 का महत्तम समापवर्तक क्या है? ### A) ... B \n", "2126 एक दशक में कितने वर्ष होते हैं? ### A) 5 B) 1... B \n", "2224 7% किसके बराबर है? ### A) 0.007 B) 0.07 C) 0.... B \n", "7667 लौवर संग्रहालय कहां है? ### A) पेरिस B) ल्योन... A \n", "7575 81 के वर्ग का वर्गमूल क्या है? ### A) 9 B) 27... C \n", "2267 25 और 55 का GCD ज्ञात कीजिए। ### A) 5 B) 11 C... A \n", "2199 24 और 36 का LCM ज्ञात कीजिए। ### A) 96 B) 144... C \n", "4358 $\\log_3 81$ आकलन करें. ### A) 4 B) 0.25 C) -1... A \n", "2268 30 का 60% क्या है? ### A) 1.8 B) 18 C) 180 D)... B \n", "2342 सटीक उत्तर खोजें: 800 - 301 ### A) 599 B) 500... C \n", "2332 सटीक उत्तर खोजें: 942 / 3 ### A) 214 B) 304 C... C \n", "2154 3 बटा 4 * x = 24 हल करें। ### A) 18 B) 32 C) ... B \n", "2155 सटीक उत्तर खोजें: 110 + 70 ### A) 18 B) 81 C)... C \n", "2437 सटीक उत्तर खोजें: 365 + 56 ### A) 300 B) 309 ... B \n", "2432 समीकरण 18 + p = 29 को हल करें। ### A) −47 B) ... C \n", "2345 समीकरण v − 26 = 68 को हल करें। ### A) −42 B) ... C \n", "2404 52 + 6 * 2 की गणना करें। ### A) 116 B) 64 C) ... B \n", "7585 बल = द्रव्यमान = क्या? ### A) वेग B) दूरी C) ... C \n", "2150 8 + 8 ÷ 2 + 2 = ### A) 4 B) 8 C) 10 D) 14 ###... D \n", "2316 समीकरण x + 71 = −22 हल करें। ### A) −93 B) −4... A \n", "2306 समीकरण 14 = w + 23 हल करें। ### A) −37 B) −9 ... B \n", "2293 समीकरण −47 = g + 24 हल करें। ### A) -71 B) -2... B \n", "2112 Compute 22 / 2 + 9. ### A) 10 B) 11 C) 20 D) ... C \n", "2087 −4 + ( −3 )= ### A) −7 B) −1 C) 1 D) 7 ### M... A \n", "2983 एंटीमोनी का प्रतीक … है ### A) W B) Sb C) Fe ... B \n", "1190 (1+i)^10 = ### A) 1 B) i C) 32 D) 32i ### MCQ... D \n", "\n", " Sub-Domain Dataset tok \n", "2084 elementary_mathematics MMLU 42 \n", "8108 miscellaneous MMLU 42 \n", "77 abstract_algebra MMLU 42 \n", "8009 miscellaneous MMLU 42 \n", "8138 miscellaneous MMLU 42 \n", "2178 elementary_mathematics MMLU 42 \n", "8005 miscellaneous MMLU 41 \n", "669 clinical_knowledge MMLU 41 \n", "2372 elementary_mathematics MMLU 41 \n", "7772 miscellaneous MMLU 41 \n", "2399 elementary_mathematics MMLU 41 \n", "2428 elementary_mathematics MMLU 41 \n", "2159 elementary_mathematics MMLU 41 \n", "2277 elementary_mathematics MMLU 41 \n", "7722 miscellaneous MMLU 41 \n", "13941 world_religions MMLU 41 \n", "2157 elementary_mathematics MMLU 41 \n", "2126 elementary_mathematics MMLU 40 \n", "2224 elementary_mathematics MMLU 40 \n", "7667 miscellaneous MMLU 40 \n", "7575 miscellaneous MMLU 40 \n", "2267 elementary_mathematics MMLU 39 \n", "2199 elementary_mathematics MMLU 39 \n", "4358 high_school_mathematics MMLU 39 \n", "2268 elementary_mathematics MMLU 38 \n", "2342 elementary_mathematics MMLU 38 \n", "2332 elementary_mathematics MMLU 38 \n", "2154 elementary_mathematics MMLU 38 \n", "2155 elementary_mathematics MMLU 38 \n", "2437 elementary_mathematics MMLU 38 \n", "2432 elementary_mathematics MMLU 38 \n", "2345 elementary_mathematics MMLU 38 \n", "2404 elementary_mathematics MMLU 38 \n", "7585 miscellaneous MMLU 37 \n", "2150 elementary_mathematics MMLU 36 \n", "2316 elementary_mathematics MMLU 36 \n", "2306 elementary_mathematics MMLU 36 \n", "2293 elementary_mathematics MMLU 36 \n", "2112 elementary_mathematics MMLU 34 \n", "2087 elementary_mathematics MMLU 33 \n", "2983 high_school_chemistry MMLU 33 \n", "1190 college_mathematics MMLU 30 " ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[14000:]" ] }, { "cell_type": "code", "execution_count": 17, "id": "x9Gu8g6zZy_Q", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "x9Gu8g6zZy_Q", "outputId": "8b7f663f-b35e-48c3-9d2e-6890458dc7cd" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Probability of 'A': 0.4350\n", "Probability of 'B': 0.2868\n", "Probability of 'C': 0.1246\n", "Probability of 'D': 0.1535\n" ] } ], "source": [ "import torch\n", "import torch.nn.functional as F\n", "from transformers import AutoTokenizer, AutoModelForCausalLM\n", "input_text = \"जोड़ें 46,911 + 653,092 ### A) 699,903 B) 700,003 C) 913,203 D) 1,122,202 ### MCQ ###\tRespond with just one letter based on these options : \"\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", "with torch.no_grad():\n", " outputs = model.generate(input_ids=inputs, max_new_tokens=1, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n", " next_token_logits = outputs.scores[0]\n", "probs = F.softmax(next_token_logits, dim=-1)\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", "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': {prob_a:.4f}\")\n", "print(f\"Probability of 'B': {prob_b:.4f}\")\n", "print(f\"Probability of 'C': {prob_c:.4f}\")\n", "print(f\"Probability of 'D': {prob_d:.4f}\")" ] }, { "cell_type": "code", "execution_count": 18, "id": "QuuDF-qci19H", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "QuuDF-qci19H", "outputId": "b15b3e0d-a767-4220-8858-356cea9456fe" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generated Response (20 tokens):\n", "system\n", "\n", "Cutting Knowledge Date: December 2023\n", "Today Date: 27 Jan 2025\n", "\n", "user\n", "\n", "### INPUT : दिए गए बहुपद के संकेतित परिमित क्षेत्र में गुणांकों के साथ सभी शून्य बताएं. x^5 + 3x^3 + x^2 + 2x in Z_5 ### A) 0 B) 1 C) 0,1 D) 0,4\t### MCQ ### Respond with just one letter based on these options : RESPONSE :assistant\n", "\n", "C\n", "\n", "Probability of 'A': 0.0000\n", "Probability of 'B': 0.0000\n", "Probability of 'C': 0.8113\n", "Probability of 'D': 0.1887\n" ] } ], "source": [ "import torch\n", "import torch.nn.functional as F\n", "from transformers import AutoTokenizer, AutoModelForCausalLM\n", "input_text = \"दिए गए बहुपद के संकेतित परिमित क्षेत्र में गुणांकों के साथ सभी शून्य बताएं. x^5 + 3x^3 + x^2 + 2x in Z_5 ### A) 0 B) 1 C) 0,1 D) 0,4\t### MCQ ### Respond with just one letter based on these options : \"\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=1, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n", " next_token_logits = outputs.scores[0]\n", "probs = F.softmax(next_token_logits, dim=-1)\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", "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': {prob_a:.4f}\")\n", "print(f\"Probability of 'B': {prob_b:.4f}\")\n", "print(f\"Probability of 'C': {prob_c:.4f}\")\n", "print(f\"Probability of 'D': {prob_d:.4f}\")" ] }, { "cell_type": "code", "execution_count": 19, "id": "r1dozae-gO5B", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "r1dozae-gO5B", "outputId": "4034e39b-ded9-4b3a-b829-e7d125a358dc" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generated Response (20 tokens):\n", "system\n", "\n", "Cutting Knowledge Date: December 2023\n", "Today Date: 27 Jan 2025\n", "\n", "user\n", "\n", "### INPUT : जोड़ें 46,911 + 653,092 ### A) 699,903 B) 700,003 C) 913,203 D) 1,122,202 ### MCQ ### RESPONSE :assistant\n", "\n", "सटीक उत्तर है A) 699,903\n", "\n", "Probability of 'A' at token 1: 0.0579\n", "Probability of 'B' at token 1: 0.0713\n", "Probability of 'C' at token 1: 0.0000\n", "Probability of 'D' at token 1: 0.0000\n", "Probability of 'A' at token 2: 0.0000\n", "Probability of 'B' at token 2: 0.0000\n", "Probability of 'C' at token 2: 0.0000\n", "Probability of 'D' at token 2: 0.0000\n", "Probability of 'A' at token 3: 0.0000\n", "Probability of 'B' at token 3: 0.0000\n", "Probability of 'C' at token 3: 0.0000\n", "Probability of 'D' at token 3: 0.0000\n" ] } ], "source": [ "import torch\n", "import torch.nn.functional as F\n", "input_text = \"जोड़ें 46,911 + 653,092 ### A) 699,903 B) 700,003 C) 913,203 D) 1,122,202 ### MCQ ###\"\n", "prompt = f\"### INPUT : {input_text} RESPONSE : \"\n", "message = [{\"role\": \"user\", \"content\": prompt}]\n", "inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n", "outputs = model.generate(input_ids=inputs, max_new_tokens=200, use_cache=True, temperature=0.1, min_p=0.1, pad_token_id=tokenizer.eos_token_id)\n", "response = tokenizer.decode(outputs[0], skip_special_tokens=True)\n", "processed_response = response.split(\"### RESPONSE :\\nmodel\")[-1].strip()\n", "print(f\"Generated Response (20 tokens):\\n{processed_response}\\n\")\n", "with torch.no_grad():\n", " outputs = model.generate(input_ids=inputs, max_new_tokens=3, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n", " scores = outputs.scores\n", "token_ids_a = tokenizer.encode('A', add_special_tokens=False)[0]\n", "token_ids_b = tokenizer.encode('B', add_special_tokens=False)[0]\n", "token_ids_c = tokenizer.encode('C', add_special_tokens=False)[0]\n", "token_ids_d = tokenizer.encode('D', add_special_tokens=False)[0]\n", "for i, score in enumerate(scores, 1):\n", " probs = F.softmax(score, dim=-1)\n", " prob_a = probs[0, token_ids_a].item()\n", " prob_b = probs[0, token_ids_b].item()\n", " prob_c = probs[0, token_ids_c].item()\n", " prob_d = probs[0, token_ids_d].item()\n", " print(f\"Probability of 'A' at token {i}: {prob_a:.4f}\")\n", " print(f\"Probability of 'B' at token {i}: {prob_b:.4f}\")\n", " print(f\"Probability of 'C' at token {i}: {prob_c:.4f}\")\n", " print(f\"Probability of 'D' at token {i}: {prob_d:.4f}\")" ] }, { "cell_type": "code", "execution_count": 20, "id": "uyYEgG7_khiV", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "uyYEgG7_khiV", "outputId": "e2bf0fc3-8b46-4361-9dc6-b07e587ee32d" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 14042/14042 [20:01<00:00, 11.68it/s]\n" ] } ], "source": [ "import torch\n", "import torch.nn.functional as F\n", "from transformers import AutoTokenizer, AutoModelForCausalLM\n", "from tqdm import tqdm\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" ] }, { "cell_type": "code", "execution_count": 21, "id": "WFl4ieGwkF0G", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 104 }, "id": "WFl4ieGwkF0G", "outputId": "efada48a-21b6-477f-83f4-8466a1e531aa" }, "outputs": [ { "data": { "text/plain": [ "'import torch\\nimport torch.nn.functional as F\\nfrom transformers import AutoTokenizer, AutoModelForCausalLM\\nresponses = []\\nprob_a_list = []\\nprob_b_list = []\\nprob_c_list = []\\nprob_d_list = []\\nbatch_size = 1\\nfor 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=1, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\\n next_token_logits = outputs.scores[0]\\n probs = F.softmax(next_token_logits, dim=-1)\\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 prob_a_list.append(probs[0, token_ids_a].item())\\n prob_b_list.append(probs[0, token_ids_b].item())\\n prob_c_list.append(probs[0, token_ids_c].item())\\n prob_d_list.append(probs[0, token_ids_d].item())\\ndf[\\'A\\'] = prob_a_list\\ndf[\\'B\\'] = prob_b_list\\ndf[\\'C\\'] = prob_c_list\\ndf[\\'D\\'] = prob_d_list'" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"\"\"import torch\n", "import torch.nn.functional as F\n", "from transformers import AutoTokenizer, AutoModelForCausalLM\n", "responses = []\n", "prob_a_list = []\n", "prob_b_list = []\n", "prob_c_list = []\n", "prob_d_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=1, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n", " next_token_logits = outputs.scores[0]\n", " probs = F.softmax(next_token_logits, dim=-1)\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", " prob_a_list.append(probs[0, token_ids_a].item())\n", " prob_b_list.append(probs[0, token_ids_b].item())\n", " prob_c_list.append(probs[0, token_ids_c].item())\n", " prob_d_list.append(probs[0, token_ids_d].item())\n", "df['A'] = prob_a_list\n", "df['B'] = prob_b_list\n", "df['C'] = prob_c_list\n", "df['D'] = prob_d_list\"\"\"" ] }, { "cell_type": "code", "execution_count": 22, "id": "y9FMYJ1s2BZG", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 423 }, "id": "y9FMYJ1s2BZG", "outputId": "7880a640-5f8a-44f4-cef9-d1d534da08bb" }, "outputs": [ { "data": { "text/html": [ "
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InputOutputSub-DomainDatasettokA1A2A3B1B2B3C1C2C3D1D2D3
1262सौना का उपयोग करना जिसे \"सौना स्नान\" या सौना ब...Bcollege_medicineMMLU26790.1096890.00.00.4715310.00.00.2523920.00.00.1663880.00.0
1202सौना का उपयोग करना जिसे \"सौना स्नान\" या सौना ब...Acollege_medicineMMLU26450.4158940.00.00.1807470.00.00.2226120.00.00.1807470.00.0
1289सौना का उपयोग करना जिसे \"सौना स्नान\" या सौना ब...Acollege_medicineMMLU26300.9241420.00.00.0758580.00.00.0000000.00.00.0000000.00.0
1246सौना का उपयोग करना जिसे \"सौना स्नान\" या सौना ब...Dcollege_medicineMMLU25490.0000000.00.00.9081850.00.00.0000000.00.00.0918150.00.0
1358सौना का उपयोग करना जिसे \"सौना स्नान\" या सौना ब...Dcollege_medicineMMLU25460.0000000.00.00.3459180.00.00.2280430.00.00.4260400.00.0
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2293समीकरण −47 = g + 24 हल करें। ### A) -71 B) -2...Belementary_mathematicsMMLU360.3123550.00.00.3847050.00.00.1357490.00.00.1671920.00.0
2112Compute 22 / 2 + 9. ### A) 10 B) 11 C) 20 D) ...Celementary_mathematicsMMLU340.2342730.00.00.6639130.00.00.0000000.00.00.1018140.00.0
2087−4 + ( −3 )= ### A) −7 B) −1 C) 1 D) 7 ### M...Aelementary_mathematicsMMLU330.3971200.00.00.4891030.00.00.1137770.00.00.0000000.00.0
2983एंटीमोनी का प्रतीक … है ### A) W B) Sb C) Fe ...Bhigh_school_chemistryMMLU330.3224900.00.00.6024910.00.00.0750190.00.00.0000000.00.0
1190(1+i)^10 = ### A) 1 B) i C) 32 D) 32i ### MCQ...Dcollege_mathematicsMMLU300.2759480.00.00.2240520.00.00.2759480.00.00.2240520.00.0
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14042 rows × 17 columns

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" ], "text/plain": [ " Input Output \\\n", "1262 सौना का उपयोग करना जिसे \"सौना स्नान\" या सौना ब... B \n", "1202 सौना का उपयोग करना जिसे \"सौना स्नान\" या सौना ब... A \n", "1289 सौना का उपयोग करना जिसे \"सौना स्नान\" या सौना ब... A \n", "1246 सौना का उपयोग करना जिसे \"सौना स्नान\" या सौना ब... D \n", "1358 सौना का उपयोग करना जिसे \"सौना स्नान\" या सौना ब... D \n", "... ... ... \n", "2293 समीकरण −47 = g + 24 हल करें। ### A) -71 B) -2... B \n", "2112 Compute 22 / 2 + 9. ### A) 10 B) 11 C) 20 D) ... C \n", "2087 −4 + ( −3 )= ### A) −7 B) −1 C) 1 D) 7 ### M... A \n", "2983 एंटीमोनी का प्रतीक … है ### A) W B) Sb C) Fe ... B \n", "1190 (1+i)^10 = ### A) 1 B) i C) 32 D) 32i ### MCQ... D \n", "\n", " Sub-Domain Dataset tok A1 A2 A3 B1 B2 \\\n", "1262 college_medicine MMLU 2679 0.109689 0.0 0.0 0.471531 0.0 \n", "1202 college_medicine MMLU 2645 0.415894 0.0 0.0 0.180747 0.0 \n", "1289 college_medicine MMLU 2630 0.924142 0.0 0.0 0.075858 0.0 \n", "1246 college_medicine MMLU 2549 0.000000 0.0 0.0 0.908185 0.0 \n", "1358 college_medicine MMLU 2546 0.000000 0.0 0.0 0.345918 0.0 \n", "... ... ... ... ... ... ... ... ... \n", "2293 elementary_mathematics MMLU 36 0.312355 0.0 0.0 0.384705 0.0 \n", "2112 elementary_mathematics MMLU 34 0.234273 0.0 0.0 0.663913 0.0 \n", "2087 elementary_mathematics MMLU 33 0.397120 0.0 0.0 0.489103 0.0 \n", "2983 high_school_chemistry MMLU 33 0.322490 0.0 0.0 0.602491 0.0 \n", "1190 college_mathematics MMLU 30 0.275948 0.0 0.0 0.224052 0.0 \n", "\n", " B3 C1 C2 C3 D1 D2 D3 \n", "1262 0.0 0.252392 0.0 0.0 0.166388 0.0 0.0 \n", "1202 0.0 0.222612 0.0 0.0 0.180747 0.0 0.0 \n", "1289 0.0 0.000000 0.0 0.0 0.000000 0.0 0.0 \n", "1246 0.0 0.000000 0.0 0.0 0.091815 0.0 0.0 \n", "1358 0.0 0.228043 0.0 0.0 0.426040 0.0 0.0 \n", "... ... ... ... ... ... ... ... \n", "2293 0.0 0.135749 0.0 0.0 0.167192 0.0 0.0 \n", "2112 0.0 0.000000 0.0 0.0 0.101814 0.0 0.0 \n", "2087 0.0 0.113777 0.0 0.0 0.000000 0.0 0.0 \n", "2983 0.0 0.075019 0.0 0.0 0.000000 0.0 0.0 \n", "1190 0.0 0.275948 0.0 0.0 0.224052 0.0 0.0 \n", "\n", "[14042 rows x 17 columns]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 23, "id": "z1sa1MNJ2Fo8", "metadata": { "id": "z1sa1MNJ2Fo8" }, "outputs": [], "source": [ "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']" ] }, { "cell_type": "code", "execution_count": 24, "id": "c1bm0UDqohyP", "metadata": { "id": "c1bm0UDqohyP" }, "outputs": [], "source": [ "df['ANS'] = df[['A', 'B', 'C', 'D']].idxmax(axis=1)" ] }, { "cell_type": "code", "execution_count": 25, "id": "jHJ5DCl4JwXl", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 565 }, "id": "jHJ5DCl4JwXl", "outputId": "998e5319-5e24-40dc-d135-711b3cfaf587" }, "outputs": [ { "data": { "text/html": [ "
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InputOutputSub-DomainDatasettokA1A2A3B1B2...C2C3D1D2D3ABCDANS
1262सौना का उपयोग करना जिसे \"सौना स्नान\" या सौना ब...Bcollege_medicineMMLU26790.1096890.00.00.4715310.0...0.00.00.1663880.00.00.1096890.4715310.2523920.166388B
1202सौना का उपयोग करना जिसे \"सौना स्नान\" या सौना ब...Acollege_medicineMMLU26450.4158940.00.00.1807470.0...0.00.00.1807470.00.00.4158940.1807470.2226120.180747A
1289सौना का उपयोग करना जिसे \"सौना स्नान\" या सौना ब...Acollege_medicineMMLU26300.9241420.00.00.0758580.0...0.00.00.0000000.00.00.9241420.0758580.0000000.000000A
1246सौना का उपयोग करना जिसे \"सौना स्नान\" या सौना ब...Dcollege_medicineMMLU25490.0000000.00.00.9081850.0...0.00.00.0918150.00.00.0000000.9081850.0000000.091815B
1358सौना का उपयोग करना जिसे \"सौना स्नान\" या सौना ब...Dcollege_medicineMMLU25460.0000000.00.00.3459180.0...0.00.00.4260400.00.00.0000000.3459180.2280430.426040D
5938यह प्रश्न निम्नलिखित जानकारी से संबंधित है।\\nस...Ahigh_school_world_historyMMLU18680.7368450.00.00.1714070.0...0.00.00.0000000.00.00.7368450.1714070.0917480.000000A
5874यह प्रश्न निम्नलिखित जानकारी से संबंधित है।\\nस...Bhigh_school_world_historyMMLU18330.0000000.00.01.0000000.0...0.00.00.0000000.00.00.0000001.0000000.0000000.000000B
12115साल 1993 में, एक पशुपालक किसान को 20 एकड़ के स...Aprofessional_lawMMLU18210.0000000.00.00.1678410.0...0.00.00.1106470.00.00.0000000.1678410.7215120.110647C
10876एक भूमि-विकास कंपनी के पास दक्षिणपश्चिम में 40...Cprofessional_lawMMLU18090.2439100.00.00.4556840.0...0.00.00.0000000.00.00.2439100.4556840.3004060.000000B
11923साल 1993 में, एक ज़मींदार के पास एक अंगूर के ब...Cprofessional_lawMMLU17870.0000000.00.00.2431370.0...0.00.00.1974110.00.00.0000000.2431370.5594520.197411C
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10 rows × 22 columns

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" ], "text/plain": [ " Input Output \\\n", "1262 सौना का उपयोग करना जिसे \"सौना स्नान\" या सौना ब... B \n", "1202 सौना का उपयोग करना जिसे \"सौना स्नान\" या सौना ब... A \n", "1289 सौना का उपयोग करना जिसे \"सौना स्नान\" या सौना ब... A \n", "1246 सौना का उपयोग करना जिसे \"सौना स्नान\" या सौना ब... D \n", "1358 सौना का उपयोग करना जिसे \"सौना स्नान\" या सौना ब... D \n", "5938 यह प्रश्न निम्नलिखित जानकारी से संबंधित है।\\nस... A \n", "5874 यह प्रश्न निम्नलिखित जानकारी से संबंधित है।\\nस... B \n", "12115 साल 1993 में, एक पशुपालक किसान को 20 एकड़ के स... A \n", "10876 एक भूमि-विकास कंपनी के पास दक्षिणपश्चिम में 40... C \n", "11923 साल 1993 में, एक ज़मींदार के पास एक अंगूर के ब... C \n", "\n", " Sub-Domain Dataset tok A1 A2 A3 B1 \\\n", "1262 college_medicine MMLU 2679 0.109689 0.0 0.0 0.471531 \n", "1202 college_medicine MMLU 2645 0.415894 0.0 0.0 0.180747 \n", "1289 college_medicine MMLU 2630 0.924142 0.0 0.0 0.075858 \n", "1246 college_medicine MMLU 2549 0.000000 0.0 0.0 0.908185 \n", "1358 college_medicine MMLU 2546 0.000000 0.0 0.0 0.345918 \n", "5938 high_school_world_history MMLU 1868 0.736845 0.0 0.0 0.171407 \n", "5874 high_school_world_history MMLU 1833 0.000000 0.0 0.0 1.000000 \n", "12115 professional_law MMLU 1821 0.000000 0.0 0.0 0.167841 \n", "10876 professional_law MMLU 1809 0.243910 0.0 0.0 0.455684 \n", "11923 professional_law MMLU 1787 0.000000 0.0 0.0 0.243137 \n", "\n", " B2 ... C2 C3 D1 D2 D3 A B C \\\n", "1262 0.0 ... 0.0 0.0 0.166388 0.0 0.0 0.109689 0.471531 0.252392 \n", "1202 0.0 ... 0.0 0.0 0.180747 0.0 0.0 0.415894 0.180747 0.222612 \n", "1289 0.0 ... 0.0 0.0 0.000000 0.0 0.0 0.924142 0.075858 0.000000 \n", "1246 0.0 ... 0.0 0.0 0.091815 0.0 0.0 0.000000 0.908185 0.000000 \n", "1358 0.0 ... 0.0 0.0 0.426040 0.0 0.0 0.000000 0.345918 0.228043 \n", "5938 0.0 ... 0.0 0.0 0.000000 0.0 0.0 0.736845 0.171407 0.091748 \n", "5874 0.0 ... 0.0 0.0 0.000000 0.0 0.0 0.000000 1.000000 0.000000 \n", "12115 0.0 ... 0.0 0.0 0.110647 0.0 0.0 0.000000 0.167841 0.721512 \n", "10876 0.0 ... 0.0 0.0 0.000000 0.0 0.0 0.243910 0.455684 0.300406 \n", "11923 0.0 ... 0.0 0.0 0.197411 0.0 0.0 0.000000 0.243137 0.559452 \n", "\n", " D ANS \n", "1262 0.166388 B \n", "1202 0.180747 A \n", "1289 0.000000 A \n", "1246 0.091815 B \n", "1358 0.426040 D \n", "5938 0.000000 A \n", "5874 0.000000 B \n", "12115 0.110647 C \n", "10876 0.000000 B \n", "11923 0.197411 C \n", "\n", "[10 rows x 22 columns]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head(10)" ] }, { "cell_type": "code", "execution_count": 26, "id": "7o2o5J9Kotv_", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "7o2o5J9Kotv_", "outputId": "81ff0e77-1ac1-46cc-a4a9-f9d229ae5c7c" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 0.3366\n" ] } ], "source": [ "accuracy = (df['Output'] == df['ANS']).mean()\n", "print(f\"Accuracy: {accuracy:.4f}\")" ] }, { "cell_type": "code", "execution_count": 27, "id": "BycMopmbIwlE", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 241 }, "id": "BycMopmbIwlE", "outputId": "8bf427cf-de2a-4109-e410-b6a2c5dd3ddb" }, "outputs": [ { "data": { "text/plain": [ "ANS\n", "C 5420\n", "A 4304\n", "B 3371\n", "D 947\n", "Name: count, dtype: int64" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['ANS'].value_counts()" ] }, { "cell_type": "code", "execution_count": 28, "id": "29c_ZVaRo2O2", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "29c_ZVaRo2O2", "outputId": "12c0d17a-5f0c-4b76-e6f1-69495948ab9e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sub-domain: abstract_algebra, Accuracy: 0.3600\n", "Sub-domain: anatomy, Accuracy: 0.3481\n", "Sub-domain: astronomy, Accuracy: 0.2895\n", "Sub-domain: business_ethics, Accuracy: 0.4000\n", "Sub-domain: clinical_knowledge, Accuracy: 0.3925\n", "Sub-domain: college_biology, Accuracy: 0.3333\n", "Sub-domain: college_chemistry, Accuracy: 0.2900\n", "Sub-domain: college_computer_science, Accuracy: 0.3100\n", "Sub-domain: college_mathematics, Accuracy: 0.1800\n", "Sub-domain: college_medicine, Accuracy: 0.3699\n", "Sub-domain: college_physics, Accuracy: 0.2059\n", "Sub-domain: computer_security, Accuracy: 0.4200\n", "Sub-domain: conceptual_physics, Accuracy: 0.3660\n", "Sub-domain: econometrics, Accuracy: 0.2807\n", "Sub-domain: electrical_engineering, Accuracy: 0.4276\n", "Sub-domain: elementary_mathematics, Accuracy: 0.2857\n", "Sub-domain: formal_logic, Accuracy: 0.2698\n", "Sub-domain: global_facts, Accuracy: 0.3300\n", "Sub-domain: high_school_biology, Accuracy: 0.3161\n", "Sub-domain: high_school_chemistry, Accuracy: 0.3202\n", "Sub-domain: high_school_computer_science, Accuracy: 0.3800\n", "Sub-domain: high_school_european_history, Accuracy: 0.4182\n", "Sub-domain: high_school_geography, Accuracy: 0.4343\n", "Sub-domain: high_school_government_and_politics, Accuracy: 0.3005\n", "Sub-domain: high_school_macroeconomics, Accuracy: 0.3077\n", "Sub-domain: high_school_mathematics, Accuracy: 0.2852\n", "Sub-domain: high_school_microeconomics, Accuracy: 0.2983\n", "Sub-domain: high_school_physics, Accuracy: 0.2252\n", "Sub-domain: high_school_psychology, Accuracy: 0.3468\n", "Sub-domain: high_school_statistics, Accuracy: 0.2083\n", "Sub-domain: high_school_us_history, Accuracy: 0.3578\n", "Sub-domain: high_school_world_history, Accuracy: 0.4008\n", "Sub-domain: human_aging, Accuracy: 0.4439\n", "Sub-domain: human_sexuality, Accuracy: 0.2824\n", "Sub-domain: international_law, Accuracy: 0.4298\n", "Sub-domain: jurisprudence, Accuracy: 0.4537\n", "Sub-domain: logical_fallacies, Accuracy: 0.2945\n", "Sub-domain: machine_learning, Accuracy: 0.2679\n", "Sub-domain: management, Accuracy: 0.3010\n", "Sub-domain: marketing, Accuracy: 0.5598\n", "Sub-domain: medical_genetics, Accuracy: 0.4000\n", "Sub-domain: miscellaneous, Accuracy: 0.4163\n", "Sub-domain: moral_disputes, Accuracy: 0.3526\n", "Sub-domain: moral_scenarios, Accuracy: 0.2458\n", "Sub-domain: nutrition, Accuracy: 0.3497\n", "Sub-domain: philosophy, Accuracy: 0.3248\n", "Sub-domain: prehistory, Accuracy: 0.3395\n", "Sub-domain: professional_accounting, Accuracy: 0.3227\n", "Sub-domain: professional_law, Accuracy: 0.2966\n", "Sub-domain: professional_medicine, Accuracy: 0.2831\n", "Sub-domain: professional_psychology, Accuracy: 0.3725\n", "Sub-domain: public_relations, Accuracy: 0.3273\n", "Sub-domain: security_studies, Accuracy: 0.3714\n", "Sub-domain: sociology, Accuracy: 0.3234\n", "Sub-domain: us_foreign_policy, Accuracy: 0.5000\n", "Sub-domain: virology, Accuracy: 0.2952\n", "Sub-domain: world_religions, Accuracy: 0.4971\n" ] } ], "source": [ "for sd, group in df.groupby('Sub-Domain'):\n", " accuracy = (group['Output'] == group['ANS']).mean()\n", " print(f\"Sub-domain: {sd}, Accuracy: {accuracy:.4f}\")" ] }, { "cell_type": "code", "execution_count": 29, "id": "m7vhP5Obk-sw", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "m7vhP5Obk-sw", "outputId": "cf25d1de-3abe-467b-93e6-af9b61da5456" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CLICK CTRl+S and wait for 2 sec\n", "NOTEBOOK NAME SHOULD BE : HINDI-LLAMA-3B-A10-MMLU-H.ipynb\n" ] } ], "source": [ "print(\"CLICK CTRl+S and wait for 2 sec\")\n", 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