{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "JeByetuw4Z8p", "outputId": "fde61f6f-ed78-4f1a-beb0-dd367acaf5b6" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.11/dist-packages (0.27.1)\n", "Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (2.2.2)\n", "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from huggingface_hub) (3.16.1)\n", "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.11/dist-packages (from huggingface_hub) (2024.10.0)\n", "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.11/dist-packages (from huggingface_hub) (24.2)\n", "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.11/dist-packages (from huggingface_hub) (6.0.2)\n", "Requirement already satisfied: requests in /usr/local/lib/python3.11/dist-packages (from huggingface_hub) (2.32.3)\n", "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.11/dist-packages (from huggingface_hub) (4.67.1)\n", "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.11/dist-packages (from huggingface_hub) (4.12.2)\n", "Requirement already satisfied: numpy>=1.23.2 in /usr/local/lib/python3.11/dist-packages (from pandas) (1.26.4)\n", "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas) (2.8.2)\n", "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas) (2024.2)\n", "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas) (2024.2)\n", "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)\n", "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface_hub) (3.4.1)\n", "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface_hub) (3.10)\n", "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface_hub) (2.3.0)\n", "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface_hub) (2024.12.14)\n" ] } ], "source": [ "!pip install huggingface_hub pandas" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 17, "referenced_widgets": [ "2421cfa9a9144c41bc5f224c881301bf", "2761891d1c634d9f8bf27dd0aab76eaf", "6dae21739f5a4bfdac454802312bf683", "a28f00af23d645e8b1fc0b354c4d5119", "c2967e079a2b488bb1d71043a3c41420", "15129e0a895d44b4958218da9acb66b6", "8fc3cca3ac724bddad7614d14d25d6db", "8552c7e81b594cd9a40f2b8944d0105d", "2fb9e64285ed4505b0113af49608c069", "9a030c7fb9c34f41930935fdabc60231", "cdf0c23a21aa4366830bfb4853075190", "3d8eeec9495141d9bcacbc37af3193a8", "8cf98f5d58b440cc97c721e36caa247b", "598daf89a59a4b39af012af9ffbd0820", "0a459ed31e524c838772f1c9e54c72a5", "e532ff104dfd44288064f4e67f17478f", "10961dbed61e4967bfd1d819c6480cf0", "fd61e8c07d5741e1b03c75c32541f4d9", "fc2140881d1f4a2a92aee7d48174c7f4", "9d275d17747d479e9ec9d65d44432759" ] }, "id": "-KBQcoiuz-0C", "outputId": "84a00434-1838-4180-ced9-715d8f61b18f" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "VBox(children=(HTML(value='
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wheels for collected packages: flash-attn\n", " Building wheel for flash-attn (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for flash-attn: filename=flash_attn-2.7.3-cp311-cp311-linux_x86_64.whl size=191363917 sha256=b1243e9b86687348a5ab03a073abacdf8e3d5e9e4b7e5326a183f47348c5dfba\n", " Stored in directory: /root/.cache/pip/wheels/d0/a3/f9/48d2706cb2eac05ec0dc144bf6954fe47bb3c2cd0de280765e\n", "Successfully built flash-attn\n", "Installing collected packages: ninja, flash-attn\n", "Successfully installed flash-attn-2.7.3 ninja-1.11.1.3\n" ] } ], "source": [ "! pip install unsloth --upgrade --no-cache-dir\n", "import torch\n", "if torch.cuda.get_device_capability()[0] >= 8:\n", " !pip install --no-deps packaging ninja einops \"flash-attn>=2.6.3\"" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 609, "referenced_widgets": [ "08f688c5642d4d32a732a07dc73d3172", "9d58340a16f241fda2151b9c16a58f8a", 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Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "0it [00:00, ?it/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "08f688c5642d4d32a732a07dc73d3172" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "🦥 Unsloth Zoo will now patch everything to make training faster!\n", "==((====))== Unsloth 2025.1.6: Fast Gemma2 patching. Transformers: 4.47.1.\n", " \\\\ /| GPU: NVIDIA A100-SXM4-40GB. Max memory: 39.564 GB. Platform: Linux.\n", "O^O/ \\_/ \\ Torch: 2.5.1+cu121. CUDA: 8.0. CUDA Toolkit: 12.1. Triton: 3.1.0\n", "\\ / Bfloat16 = TRUE. FA [Xformers = 0.0.29.post1. FA2 = True]\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 4$ जो संतुष्ट करते हैं\\n\\[\\... \n", "\n", " Output LANG \\\n", "0 import math \\r\\ndef find_Index(n): \\r\\n x =... en \n", "1 D en \n", "2 C en \n", "3 To find the average number of billboards seen ... en \n", "4 B en \n", "... ... ... \n", "33767 5 en \n", "33768 The text does not specify how often resolution... en \n", "33769 B en \n", "33770 दिन 1: विक्टोरिया मेमोरियल, सेंट पॉल का कैथेड्... hi \n", "33771 दी गई समीकरण से,\\n\\[\\sqrt{x + 4 \\sqrt{x - 4}} ... hi \n", "\n", " type subject_name topic; subject \n", "0 NaN NaN NaN NaN \n", "1 NaN Surgery NaN NaN \n", "2 NaN NaN NaN NaN \n", "3 NaN NaN NaN NaN \n", "4 NaN NaN NaN NaN \n", "... ... ... ... ... \n", "33767 NaN NaN NaN NaN \n", "33768 NaN NaN NaN NaN \n", "33769 NaN NaN NaN NaN \n", "33770 NaN NaN NaN NaN \n", "33771 Intermediate Algebra NaN NaN NaN \n", "\n", "[33772 rows x 7 columns]" ], "text/html": [ "\n", "
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........................
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33769Elizabeth Freeman was born about 1742 to Afric...BenNaNNaNNaNNaN
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\\u092e\\u0940\\u0921\\u093f\\u092f\\u092e \\u0915\\u0935\\u0930\\u0947\\u091c \\u0939\\u0948 \\u091c\\u094b \\u0921\\u093e\\u0930\\u094d\\u0915 \\u0938\\u0930\\u094d\\u0915\\u0932\\u094d\\u0938 \\u0915\\u094b \\u091b\\u093f\\u092a\\u093e\\u0928\\u0947 \\u092e\\u0947\\u0902 \\u0905\\u091a\\u094d\\u091b\\u0940 \\u0924\\u0930\\u0939 \\u0938\\u0947 \\u0915\\u093e\\u092e \\u0915\\u0930\\u0924\\u0940 \\u0939\\u0948\\u0964\\\"\",\n \"\\u092f\\u0926\\u093f \\u0926\\u094b\\u0928\\u094b\\u0902 \\u092a\\u093e\\u0938\\u0947 $1$ \\u0938\\u0947 $20$ \\u0924\\u0915 \\u0915\\u0947 \\u0928\\u0902\\u092c\\u0930 \\u0935\\u093e\\u0932\\u0947 \\u0939\\u094b\\u0924\\u0947, \\u0924\\u094b \\u0939\\u092e \\u0928\\u093f\\u092e\\u094d\\u0928\\u0932\\u093f\\u0916\\u093f\\u0924 \\u0924\\u0930\\u0940\\u0915\\u094b\\u0902 \\u0938\\u0947 $24$ \\u0915\\u093e \\u092f\\u094b\\u0917 \\u092a\\u094d\\u0930\\u093e\\u092a\\u094d\\u0924 \\u0915\\u0930 \\u0938\\u0915\\u0924\\u0947 \\u0925\\u0947: \\n\\\\begin{align*}\\n4&+20\\\\\\\\\\n5&+19\\\\\\\\\\n6&+18\\\\\\\\\\n& \\\\ \\\\, \\\\vdots \\\\\\\\\\n18&+6\\\\\\\\\\n19&+5\\\\\\\\\\n20&+4\\n\\\\end{align*} \\u092f\\u0939 \\u0915\\u0941\\u0932 $20-4+1=17$ \\u0924\\u0930\\u0940\\u0915\\u0947 \\u0939\\u0948\\u0902\\u0964 \\u0939\\u093e\\u0932\\u093e\\u0902\\u0915\\u093f, \\u092a\\u0939\\u0932\\u0947 \\u092a\\u093e\\u0938\\u0947 \\u092e\\u0947\\u0902 $20$ \\u0915\\u093e \\u091a\\u0947\\u0939\\u0930\\u093e \\u0928\\u0939\\u0940\\u0902 \\u0939\\u0948, \\u0907\\u0938\\u0932\\u093f\\u090f \\u0939\\u092e\\u0947\\u0902 $20+4$ \\u0915\\u0947 \\u0930\\u094b\\u0932 \\u0915\\u0930\\u0928\\u0947 \\u0915\\u0940 \\u0938\\u0902\\u092d\\u093e\\u0935\\u0928\\u093e \\u0915\\u094b \\u0939\\u091f\\u093e\\u0928\\u093e \\u0939\\u094b\\u0917\\u093e\\u0964 \\u0907\\u0938\\u0915\\u0947 \\u0905\\u0932\\u093e\\u0935\\u093e, \\u0926\\u0942\\u0938\\u0930\\u0947 \\u092a\\u093e\\u0938\\u0947 \\u092e\\u0947\\u0902 $9$ \\u0915\\u093e \\u091a\\u0947\\u0939\\u0930\\u093e \\u0928\\u0939\\u0940\\u0902 \\u0939\\u0948, \\u0907\\u0938\\u0932\\u093f\\u090f \\u0939\\u092e\\u0947\\u0902 $15+9$ \\u0915\\u0947 \\u0930\\u094b\\u0932 \\u0915\\u0930\\u0928\\u0947 \\u0915\\u0940 \\u0938\\u0902\\u092d\\u093e\\u0935\\u0928\\u093e \\u0915\\u094b \\u092d\\u0940 \\u0939\\u091f\\u093e\\u0928\\u093e \\u0939\\u094b\\u0917\\u093e\\u0964 \\u0907\\u0938\\u0938\\u0947 $17-2=15$ \\u0938\\u0902\\u092d\\u093e\\u0935\\u093f\\u0924 \\u0924\\u0930\\u0940\\u0915\\u0947 \\u092c\\u091a\\u0947 \\u0939\\u0948\\u0902 $24$ \\u0930\\u094b\\u0932 \\u0915\\u0930\\u0928\\u0947 \\u0915\\u0947 \\u0932\\u093f\\u090f\\u0964 \\u0915\\u0941\\u0932 $20\\\\cdot 20=400$ \\u0938\\u0902\\u092d\\u093e\\u0935\\u093f\\u0924 \\u0930\\u094b\\u0932 \\u0939\\u0948\\u0902, \\u0907\\u0938\\u0932\\u093f\\u090f \\u0905\\u0902\\u0924\\u093f\\u092e \\u0938\\u0902\\u092d\\u093e\\u0935\\u0928\\u093e \\u0939\\u0948: $$\\\\frac{15}{400}=\\\\boxed{\\\\frac{3}{80}}.$$\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"LANG\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"hi\",\n \"en\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"type\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 7,\n \"samples\": [\n \"Intermediate Algebra\",\n \"Number Theory\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"subject_name\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 21,\n \"samples\": [\n \"Surgery\",\n \"Psychiatry\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"topic;\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 97,\n \"samples\": [\n \"Astrophysics\",\n \"Algebra\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"subject\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 57,\n \"samples\": [\n \"us_foreign_policy\",\n \"high_school_world_history\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 25 } ], "source": [ "df = df_train.copy()\n", "df" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "id": "zYNCBXnxEvGg" }, "outputs": [], "source": [ "from datasets import Dataset\n", "df_train['Output'] = df_train['Output'].astype(str)\n", "dataset = Dataset.from_pandas(df_train)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "id": "orRdPSsSE7u2" }, "outputs": [], "source": [ "alpaca_prompt = \"\"\"### INPUT: {} RESPONSE: {}\"\"\"" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "id": "3fax1umiFFG6", "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "d8bddcc19a294f8eadb2ec08932face4", "822d28f4ced345519184455b4111bd25", "3025bd4f97bc4abdae1130f237725f86", "11bf3001561e4a168586894a747e618e", "d3be6b94dfe44f5aa5df46237e497732", "14af3f13a3054327b65fb6c6ce11bef6", "e29684e634664f42968f508eeccdd13a", "5db87b1ff0c54e4bb6b3a0e1613ee6d7", "609d57d1180a4829bb0b6a884647cf62", "34cbf7eb619f4747a3bc4cf163edaa0b", "718f76df263a40e19e2fd48aae6f6c6e" ] }, "outputId": "5a05de2d-695b-4e05-efbe-d0b660e27e7e" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Map: 0%| | 0/33772 [00:00" ], "text/html": [ "\n", "
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StepTraining Loss
10001.109300
20001.022100

" ] }, "metadata": {} } ], "source": [ "trainer_stats = trainer.train()" ] }, { "cell_type": "markdown", "metadata": { "id": "xj6XdJWNF6Pb" }, "source": [ "#**MODEL SAVING**" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "id": "ULl7losCFj9L" }, "outputs": [], "source": [ "!wait" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 533, "referenced_widgets": [ "fc8d49b7f7fa46d5bffac0d4591a2fb3", "5d50482eae954256b8810f5f68ce282f", "d3eec490362446948471316f49d40395", "41e103732216432fa35aba373732d49f", "f7834cc170454c9eb649369d9d66dc27", "15efb64d6bfd4ed99d297b86562237d8", "4a66ffae89bf478094d0fc502d74c790", "77c5c517a3c34ba0b3c577d21239b26a", "6defda284abd4b3495fea860d50072e6", "abd5c28eae8341a6849b0689a0a651ed", "ff97c8e7cf55442d97486db4ae386c2d", "8cac1e5604854b7680596913bb44ce55", "efd12182263a4830831ffa2a05b1f861", "906f2b7484664daebcecbfc83f486958", "fef74e1a49004a90864ef09adb1b3e80", "86e7960e21af4509b442938f6ac1959c", "e277e5c6edc94fed9bdefe297bbcba88", "63b878ef09ee4e318ae1c00526c0c4bb", "07d3ad2afc804864bcdf07ab92aa0886", "389bb24b86014dacb7d17bd8063f6fc0", "4334f0155de2466b89881166376023a3", "b376a415f7ac4fa0aeeb17006ab9dc23", "4452bf8258bd474c8331452867b6b261", "e54f6c84ab514df6960ef51d4c86a3b6", "0092174d808c4fdab9179a312ced02a7", "3d78d7a6b94e44078a6bacf562474d7b", "becdebcdb6af435793deacf6ae27c25d", "c8b3ee6292ff4747aa47e76349c81377", "1a1a10dcb7d4464c8bfb9e2d5862f97d", "f3334639ad154f1791fcdee59ebb3a50", "ce947567e41549179da455f26c063f02", "c26be0d639694d80897e20f2a54500d4", "18c93e34d6834839bc770f8fe2412bb1", "69e6a443d2114e52be185af5929ba94b", "bd71131a20f84067aeb0bc5ffc547e23", "0a9eaae6a6c14dc3aed02e4fc9918fa2", "a8a399f12b264ead8011ad6518aa74f4", "9a0f61c98fef4e99bcd5dc0e37c1dbda", "70b6d3f86272419bb08ea754158fef11", "284ac30b298e4308a42d5d0278fe8087", "711c401a92f24cba9d583554770f85e9", "cd4288ec433a48adb06dc1cd4c0ec49e", "7b86c2659479439689e667f790f66696", "d3932d1f31034a538488eb0a9ebcd7fd", "e8060a5a831a4732837387a83fe233b8", "8fef6350d80d4d84ab42ad174e8ce36f", "22cc3bb5e7704d5c908b3b0a195bc831", "a24a27c579364776923862e52b19cc07", "447b687dc469441dbdde9b5848dbce83", "377edac8efd346f082ed1a2afa61c6a5", "b3f6c9de387244e8898ebba3a364ead2", "b050503e320f476a9aacc853fafa8adc", "763849d7a0de4de399bb3fe031216657", "f5e1f07c5e4846658e333e23599266cd", "fabeb1fa98db45f4af94f85140526db9", "2070f3a57e3c4ae08ad57c4233cf16c1", "b208364fb3264357aeabb99454d27480", "269b1548b066423f9aeb003f0aa47edd", "6705723ce8aa4121af49a17c7c2a1c9d", "0d93f63b257b44adb897ebe23b6f875a", "3c72365bb0174f9789d7f7b2deb70b59", "67584143096d41b38d4f7ab3f92db3aa", "2529063c45504b5d885b828a3d616271", "e75234ebddac4639a139325b7cb0b2f3", "b8d63ae359124f5ca95b9bea25c80cc6", "3f5fd2ca812149a8bfad68a630fd6cb0", "721d4ec0cb644e5aab4bf5ce07381343", "341a2211913b418e9f4dfe5dc079036a", "d934798a903b478592717c366d3ed641", "d5775b066d1e47658ae95d5312b318ea", "757184dede5f4e97b308450ea1793220", "dc7b089120ed417096539f3705b252ca", "8141ec5872624f8499b2f8c0cd8e6b1d", "350d3ae6c5bb4748b4cbb1b2b43afb2a", "5952d32741ee48aaaf81181bcb85a6e3", "3b8ce9fbfffe409f9fc93be9f5196a72", "8b0d10ed292d46238caa529e42a81006", "68469fa201ab41b0b3cffc8939fec0f7", "6c9e64b7eaf04a648f1f34723cc2311a", "95a73264888a49daacd6a92cc8f5e7d1", "0e5adda2074842f29028319579ba252f", "a9d09fd3141c48ee86de9f74e35e6535", "735ec7ef25ab4631ab78c5599ece3eff", "3e39e1843d8e4216ba629ba710e4a16d", "10b1b331b4254f0dbd6ab7e19a018b6b", "41867ea7c1194499801d3ce70c19bcea", "66a4facfd96d445dac46c52a9311e734", "a5092594fb234ee6a02826856cfa7ae5" ] }, "id": "cEGk9Cm1FmoH", "outputId": "d17aa22c-56be-4b6e-8a9c-3f95d1ba8b4f" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Unsloth: You are pushing to hub, but you passed your HF username = DrishtiSharma.\n", "We shall truncate DrishtiSharma/HINDI-GEMMA-9B-A30 to HINDI-GEMMA-9B-A30\n", "Unsloth: Kaggle/Colab has limited disk space. We need to delete the downloaded\n", "model which will save 4-16GB of disk space, allowing you to save on Kaggle/Colab.\n", "Unsloth: Will remove a cached repo with size 18.5G\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Unsloth: Merging 4bit and LoRA weights to 16bit...\n", "Unsloth: Will use up to 48.85 out of 83.48 RAM for saving.\n", "Unsloth: Saving model... This might take 5 minutes ...\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ " 62%|██████▏ | 26/42 [00:00<00:00, 69.78it/s]\n", "We will save to Disk and not RAM now.\n", "100%|██████████| 42/42 [00:11<00:00, 3.72it/s]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Unsloth: Saving tokenizer..." ] }, { "output_type": "display_data", "data": { "text/plain": [ "tokenizer.model: 0%| | 0.00/4.24M [00:00> THE CSV SAVED IN YOUR RUNTIME" ] }, { "cell_type": "code", "source": [ "from google.colab import runtime\n", "runtime.unassign()" ], "metadata": { "id": "brN9DtmHhYPS" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [], "metadata": { "id": "3BcP1kfkhcFb" }, "execution_count": null, "outputs": [] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "A100", "machine_shape": "hm", "provenance": [] }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.12" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "2421cfa9a9144c41bc5f224c881301bf": { "model_module": "@jupyter-widgets/controls", "model_name": "VBoxModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "VBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "VBoxView", "box_style": "", "children": [], "layout": "IPY_MODEL_8fc3cca3ac724bddad7614d14d25d6db" } }, "2761891d1c634d9f8bf27dd0aab76eaf": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_8552c7e81b594cd9a40f2b8944d0105d", "placeholder": "​", "style": "IPY_MODEL_2fb9e64285ed4505b0113af49608c069", "value": "


Copy a token from your Hugging Face\ntokens page and paste it below.
Immediately click login after copying\nyour token or it might be stored in plain text in this notebook file.
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