{ "cells": [ { "cell_type": "code", "source": [ "#!pip install -q -U bitsandbytes\n", "!pip install -q -U git+https://github.com/huggingface/transformers.git\n", "#!pip install -q -U git+https://github.com/huggingface/peft.git\n", "!pip install -q -U git+https://github.com/huggingface/accelerate.git\n", "!pip install -q -U datasets\n", "!pip install xformers" ], "metadata": { "id": "j1_S64SuG_Gn", "outputId": "7eeb7930-ed61-4df6-ea35-cf482f3ecfad", "colab": { "base_uri": "https://localhost:8080/" } }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K 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torch==2.0.1->xformers) (3.12.2)\n", "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch==2.0.1->xformers) (4.7.1)\n", "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch==2.0.1->xformers) (1.12)\n", "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch==2.0.1->xformers) (3.1)\n", "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch==2.0.1->xformers) (3.1.2)\n", "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch==2.0.1->xformers) (2.0.0)\n", "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch==2.0.1->xformers) (3.27.2)\n", "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch==2.0.1->xformers) (16.0.6)\n", "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch==2.0.1->xformers) (2.1.3)\n", "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch==2.0.1->xformers) (1.3.0)\n", "Installing collected packages: xformers\n", "Successfully installed xformers-0.0.21\n" ] } ] }, { "cell_type": "code", "source": [ "from huggingface_hub import notebook_login\n", "\n", "notebook_login()" ], "metadata": { "id": "ejRqY95eG_Js", "outputId": "a52946df-f96e-4a90-97e0-d42b27e21dba", "colab": { "base_uri": "https://localhost:8080/", "height": 145, "referenced_widgets": [ "cd666e115ca949a6a0828fbabae7d592", "05fe078374924fbc914de938abd34375", "393e108b388d48188ad86cc657e563a1", "c3b1e9055a044b5b9a851d21cc776e7b", "3825bcd53a4e43359240a1f29d9b6590", "72e5ca4f630c49ba872206af6a9ffd09", "f8aae38607414753a7f3f3a524744872", "dff3b24b7a82494192279d8e409a5d4f", "27a0600d2f4247b6842f21de84e4b5e9", "90b7f6b78a8e41ac9d24adc66dde0c9f", "0ed85f1babff431e9658cbb23065b84c", "af367683d85147c3abd9db2c07050307", "507e2602a5214608b780169b1faaad31", "0f03692191c248eaae5a9692b2582377", "7c177077979d4ceeb0048bfffaaaea4b", "efd0359c0efc4a2ab6f8557bfddb9491", "fd3e47df2da742688671ce614ea18135", "a7af54439e6d47e99512da1ba1a6adee", "6acd2d9142584277b64acb4c99226c8a", "86e974bb792b442b9cd450524039263b", "798d5eca9e714fa39914f2ee00d02cfa", "5bee398314df4b34b1897b219216c130", "e6f56b79264f4977b470558549828584", "21b1df2da90f4036b97eb5d48ad52d33", "c89cf76ade8640eb960adadf53d24218", "5e7e6fe63e6f4060960fd1dec1c98f90", "1d031df99b6941a69837ce1866bcc277", "b834ee4a8aea4eea899abf58ca0c0c82", "36d9fe597e0a4a49a369d696ef32cfbf", "709bf7486193439480d32b9716dc7dba", "7b26418601e7408296c137c66f579fd3", "e5df4b0ef8a54c4d8d6e925764224d5c" ] } }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "VBox(children=(HTML(value='
0:\n", " train_val = data[\"train\"].train_test_split(\n", " test_size=VAL_SET_SIZE, shuffle=True, seed=42\n", " )\n", " train_data = train_val[\"train\"].shuffle().map(generate_and_tokenize_prompt)\n", " val_data = train_val[\"test\"].shuffle().map(generate_and_tokenize_prompt)\n", "else:\n", " train_data = data[\"train\"].shuffle().map(generate_and_tokenize_prompt)\n", " val_data = None" ], "metadata": { "id": "p9tarFwQHw4h", "outputId": "8cb495cf-2253-48f5-dec6-24442f201b00", "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ "adcf3fb6dea248f1a1a089c7520700b5", "c6e85c979ca748dbaf4043836f1bbd12", "8b691145942c4b37bc273d3fde66fe09", "7846682ea36549a4a8bd30a4fd3435a8", "6d427b3c8b4a4c3e9aafaf6c3cc20ae3", "f5091a2b191b442aa0b366e93f97230f", "3b11074e05c74d65b955899118e8e00b", "1358d228a90e46fda2e1e5eaee5cb926", "be09e6c9005342dfb8272ae95adf11f5", "6c2f10edec034c80941331817d1bd473", "aa7af5ddb7314d4c8d37947dcb157e82", "ae9fa936117c424eb8732accb60b8106", "0218498da7b54374940882044cc1daf1", "49f5fed8ddb24b5c9258b74b9abe7301", "74936c7124274150b7be99c61265361e", "f374cee81d7442349849aac44cabdd8a", "5feb5bde56fe4c2ca656698f0531f4fc", "1c0efa5990e549628d737495f4028bfb", "aae0ca32c68f4f6b9a73d3b54664f460", "49b2620ec7d54089879cee20c45eca39", "df6ded6f0b604d9b8a97c67ad86b5bda", "ca68dfe2b1ef412e8ed903afea79ad16" ] } }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Map: 0%| | 0/1769 [00:00" ], "text/html": [ "\n", "
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StepTraining Loss
105.385800

" ] }, "metadata": {} }, { "output_type": "error", "ename": "KeyboardInterrupt", "evalue": "ignored", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\\n ========== train...========= \\n\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m 1554\u001b[0m \u001b[0mhf_hub_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menable_progress_bars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1555\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1556\u001b[0;31m return inner_training_loop(\n\u001b[0m\u001b[1;32m 1557\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1558\u001b[0m \u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36m_inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m 1836\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1837\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccelerator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccumulate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1838\u001b[0;31m \u001b[0mtr_loss_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1839\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1840\u001b[0m if (\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtraining_step\u001b[0;34m(self, model, inputs)\u001b[0m\n\u001b[1;32m 2681\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2682\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_loss_context_manager\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2683\u001b[0;31m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2684\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2685\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_gpu\u001b[0m \u001b[0;34m>\u001b[0m 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to be fixed and made cleaner later.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1499\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1500\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1502\u001b[0m \u001b[0;31m# Do not call functions when jit is 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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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