{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "paq2r1XYDIhd", "outputId": "255e692d-6b36-46bb-8246-92fd4478a98f" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sat Dec 9 05:11:21 2023 \n", "+---------------------------------------------------------------------------------------+\n", "| NVIDIA-SMI 535.104.05 Driver Version: 535.104.05 CUDA Version: 12.2 |\n", "|-----------------------------------------+----------------------+----------------------+\n", "| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |\n", "| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\n", "| | | MIG M. |\n", "|=========================================+======================+======================|\n", "| 0 NVIDIA RTX A5000 Off | 00000000:3E:00.0 Off | Off |\n", "| 30% 31C P8 16W / 230W | 10MiB / 24564MiB | 0% Default |\n", "| | | N/A |\n", "+-----------------------------------------+----------------------+----------------------+\n", " \n", "+---------------------------------------------------------------------------------------+\n", "| Processes: |\n", "| GPU GI CI PID Type Process name GPU Memory |\n", "| ID ID Usage |\n", "|=======================================================================================|\n", "+---------------------------------------------------------------------------------------+\n" ] } ], "source": [ "!nvidia-smi" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "id": "9gqBrXdeBF3q" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "^C\n", "\u001b[31mERROR: Operation cancelled by user\u001b[0m\u001b[31m\n", "\u001b[0m^C\n", "\u001b[31mERROR: Operation cancelled by user\u001b[0m\u001b[31m\n", "\u001b[0m" ] } ], "source": [ "!pip install -Uqqq pip --progress-bar off\n", "!pip install -qqq bitsandbytes==0.39.0 --progress-bar off\n", "!pip install -qqq torch==2.0.1 --progress-bar off\n", "!pip install -qqq -U git+https://github.com/huggingface/transformers.git@e03a9cc --progress-bar off\n", "!pip install -qqq -U git+https://github.com/huggingface/peft.git@42a184f --progress-bar off\n", "!pip install -qqq -U git+https://github.com/huggingface/accelerate.git@c9fbb71 --progress-bar off\n", "!pip install -qqq datasets==2.12.0 --progress-bar off\n", "!pip install -qqq loralib==0.1.1 --progress-bar off\n", "!pip install -qqq einops==0.6.1 --progress-bar off" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "eYecceK4SaBu" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "===================================BUG REPORT===================================\n", "Welcome to bitsandbytes. For bug reports, please run\n", "\n", "python -m bitsandbytes\n", "\n", " and submit this information together with your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n", "================================================================================\n", "bin /opt/conda/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cuda121.so\n", "CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching in backup paths...\n", "CUDA SETUP: CUDA runtime path found: /usr/local/cuda/lib64/libcudart.so\n", "CUDA SETUP: Highest compute capability among GPUs detected: 8.6\n", "CUDA SETUP: Detected CUDA version 121\n", "CUDA SETUP: Loading binary /opt/conda/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cuda121.so...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.10/site-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/usr/local/nvidia/lib64'), PosixPath('/usr/local/nvidia/lib')}\n", " warn(msg)\n", "/opt/conda/lib/python3.10/site-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: /usr/local/nvidia/lib:/usr/local/nvidia/lib64 did not contain ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] as expected! Searching further paths...\n", " warn(msg)\n", "/opt/conda/lib/python3.10/site-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('ssh-rsa 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 shanjay@LAPTOP-Q1PG3AE7')}\n", " warn(msg)\n", "/opt/conda/lib/python3.10/site-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('//g.notebooksg.jarvislabs.net'), PosixPath('https')}\n", " warn(msg)\n", "/opt/conda/lib/python3.10/site-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('module'), PosixPath('//matplotlib_inline.backend_inline')}\n", " warn(msg)\n" ] } ], "source": [ "import json\n", "import os\n", "from pprint import pprint\n", "\n", "import bitsandbytes as bnb\n", "import pandas as pd\n", "import torch\n", "import torch.nn as nn\n", "\\\n", "import transformers\n", "from datasets import load_dataset\n", "from huggingface_hub import notebook_login\n", "from peft import (\n", " LoraConfig,\n", " PeftConfig,\n", " PeftModel,\n", " get_peft_model,\n", " prepare_model_for_kbit_training,\n", ")\n", "from transformers import (\n", " AutoConfig,\n", " AutoModelForCausalLM,\n", " AutoTokenizer,\n", " BitsAndBytesConfig,\n", ")\n", "\n", "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 145, "referenced_widgets": [ "ef751e27c2a849dfa4dd61dcb4089a5c", "3aa625ac4fb44271bdda10bfc0fdd27e", "3029a8d4a3044e6da9e6c75100b71742", "81795f1cada142a79a7b23bd8093a610", "3a08090f5ce7461aa86a3f84ebbeee57", "bed9e120a3c44a3c969377ab8876ef08", "619587447c234b81b72635ef9e5f2ef8", "d36eedfeeb6e40b09faa9353a6002ede", "8915172b1a3d414ebbc52a88a7b5ca7f", "6c5eff9d76114920a0ac3202003a99ae", "6007fa409a224cdab342c3380fda21a4", "252e53a4b5334897a5e6700427246c12", "cc315951f7d84d4295f4429bdf537abb", "db28d213677b44dd9dbe378c1ec7c129", "4e7207976a3f47aa83b00954c934a009", "2e9523fa798744f2866098ea8743b5c2", "96463aef5d3a46b19303988834d900f7", "90cd1f298801417ba2a9b457b6f7945c", "17bea48ace15491abf06bbd6b17344c4", "f9a1bf2023f44f4eba85d07fc5b88888", "4591f16c3ee343309e061f78d4916e1f", "3ebde0fc16f945bbb89b07256ac13a4d", "9f0bc371b7de4afe9bbf92177d23120d", "0694e4c434f44167a89b961e7552c806", "887b081f640e426084b62701308068a0", "8eaf5f82b827405d90a45c2e66daa407", "a35bcce3165740efadd0c5c3ac0a35e2", "b9c713d1cfd44f698e0b2c724e7e7f9b", "39ed97c21ddc407380fc679be34dfbb2", "48412f9d96a14b8d86720c346916db77", "8d8e9b64c465454593dd49b98c080d27", "be065982c5714ea3b1dd7927b7ad328e" ] }, "id": "kuBn1UxsNzLs", "outputId": "57827b6e-fdba-4450-8c58-506b6041c1c1" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "04d2bc67be3a43ef89a7cb52ae3f2b18", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value='
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questionanswer
0Problem:\\nI am trying to clean up a Excel file...import pandas as pd\\nimport numpy as np\\n\\ndf ...
1Problem:\\nI've a data frame that looks like th...import pandas as pd\\n\\ndf = pd.DataFrame({'use...
2Problem:\\nI need to rename only the first colu...import pandas as pd\\n\\n\\ndf = pd.DataFrame([[1...
3Problem:\\nI am trying to extract rows from a P...import pandas as pd\\nimport io\\n\\ndata = io.St...
4Problem:\\nI've a data frame that looks like th...import pandas as pd\\n\\ndf= pd.DataFrame({'user...
\n", "" ], "text/plain": [ " question \\\n", "0 Problem:\\nI am trying to clean up a Excel file... \n", "1 Problem:\\nI've a data frame that looks like th... \n", "2 Problem:\\nI need to rename only the first colu... \n", "3 Problem:\\nI am trying to extract rows from a P... \n", "4 Problem:\\nI've a data frame that looks like th... \n", "\n", " answer \n", "0 import pandas as pd\\nimport numpy as np\\n\\ndf ... \n", "1 import pandas as pd\\n\\ndf = pd.DataFrame({'use... \n", "2 import pandas as pd\\n\\n\\ndf = pd.DataFrame([[1... \n", "3 import pandas as pd\\nimport io\\n\\ndata = io.St... \n", "4 import pandas as pd\\n\\ndf= pd.DataFrame({'user... " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.DataFrame(data).head()" ] }, { "cell_type": "markdown", "metadata": { "id": "D2CSAa1zZNsu" }, "source": [ "## Load Falcon Model & Tokenizer" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 508, "referenced_widgets": [ "7a30dba72f1b4a3e8c0202bcfa827c99", "2af372df6d114a609e94002a9c61e2be", "0cde557a86384dc8925304b5f3aa899d", "c9199c709b874b64a40c16f28677acdc", "ebd6abe9635c48878c60e8c4c3b00ccc", "770ef860f79e48c98799a95f92861fe6", "9b5f4a732bb848c7ad5c494f657ca9a1", "338418a8c6184c90a986d8bafbb99ae5", "5703f7c64d9e445088215a7f68651848", "350788f2e97a44679a2dbc421e05d3be", "a4083090ccaf4fcfb8dd8de44ff089f0", "a40826eb48f746baa0baaf44f0a2fa65", "8d2b9023fab24ef083fa71a6a191fe98", "da2252e479854020b017f41db862faec", "15a99daba4574000948dc23e50418baa", "76b65d63f8c14274a8b8c87e18a9040d", 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"2174990acb36412198f6c539c191784b", "9a1068850fed491b93b9e645268ae927", "837bee5e8166401bb592b1c8f9ae042e", "6f35cbdc3137402491245be993db5583", "28e3a24723e74d50ae07b8086334453a", "58f7004a8469422cb971939e24bf65ef", "8eb143d4142d4e17bdfefebeed040a13", "3bf50ffb9dd4424490d5a3db36dcaffc", "5384397dd70f4108b6d535d9bae4a62e", "c484fb65a5eb477cb8c7f51d9d050b84", "aa3b67b5faf44eb394cd52bdfa6b34cd", "efd679cd11064fc1ba1b8303f53e81a6", "0e47d2babc7b400391bbe738c8b4e90a", "5994a27148ec417da709ae592d8bd5f4", "fac4c6370cea48599aec3092f5c87897", "8441d394971e4ebca7d06d78491e005f", "67f8fd1436a84374927f01c5359706d6", "f6a5caa875134818a2e03eb2998bd2a7", "3207d18fe8864510a5c9149e82c5b47b", "8ba5b2f1874443208d1e29b9b57f1ce7", "7a0b594559ee47bda4a03d9d8538a7a8", "b23efe7e794a46c08dd2f1bc631a106f", "cb13185d1d90428397ccd98aa8b06c1d", "dcf264f2f1b84ba5b284f7e5e1a5d4bb", "aa7182da38004d928b55a130ef4c2366", "628642a8c650457c8ee35938825a04c5", "e7dd49abfc334c0d9aa55725e8b0077f", "b747a98035f14be48c6e456945a03a92", "ea1c1e36682947fc9ce2befd6c90a160", "eae767bf52ae47069d63bd090e2d601b", "94e1e12f06c64b74b38314b1633f1fd6", "d7cb8cf78fc34c81b9534b62ff02da95", "002286b5155b496b8856722f50bf5ba9", "87fef40ab9444e7ca7c546a3303731b9", "ef31c21cc0a84965b164d880d9ce5fff", "264bba281850425a9a056858eb945803", "98a2a777c2594194bb720fb8d7c44ecb", "1bd7f694755b4241b4aa68c83e1cbdf0", "05618d7ac8a34128ab706808f4d14b6b", "d6b40d82bc5f4f158e56355cd95d7a72", "9902379e2fd348a3bec0dfa50238c31e", "64f6d1d7bd954ae382b13b59d93ba721", "3e3125c20c514567941d02ab9bfc601f", "52e32f85524e4cce9d4d2f2edd7c031c", "f02b59d8a21b49eb99eaa44f9f4a0ccc", "3bea4cd67dd64ca699131b572d54f7a9", "22c1db1e86f548a4926ac63669ee3eac", "dba3e0fd019244a7a79456393fcbef6a", "1b4cfaef95bf49da803811763a8a1159", "9d5cfcfc08c14845bc5b98c21faf2110", "0e3254712184494180b0e7594ddb997f", "b502b53b83f14c0da20df486b11e274a", "b95f1c1dd02d48df8f57062c81142d39", "6066bf647cbc4ed2bcb54f0f781db88c", "bbb1d029fb8e4f69aecb1fb230f54ae9", "ec8cff90f44749cab30f572d572aa9a2", "cf450e7e3996418fa2bf14c5cec3ab1f", "d86ed085c063432bb436d6aeac6fdae1", "367242861be34f1eb616734bee15197a", "e0883684fe3f409cabb6cf3284272837", "55fd1a6ff7b64cf4bc43272ce63ffddd", "7d66e5e238a84866843f62667a13d65c", "8df7124e706d41548633feb76835430f", "df8924ed79054b83a42117f8777e870a", "34987daa3c1344e083ba771cd88f8bc9", "b543bcb21aa3494c9a144eb274696b34", "dbbff13897654eb2aaf26d7025594200", "67fd9bf037c94cdcb114e9d34e2f0d18", "ddeb3b0cb0a04fbd9ef1eb177925e207", "69e75a39a4874dc5aaf5bcab8f231f39", "d49e468caaf14b81990c20050481e67a", "e95c6ee3e1c24b7787d51efaf1001813", "552aa8d1e8cb40e8aa5d6f6df19876ef", "01cbabc3607e4ab5bc3750a96bb9d4bd", "592b77ecd05f423b8ab133214f0c77b7", "750ee77cecef4deb9e2999f55018d00f", "5e2daaa6b53945a1a4c0b72f35af8e95", "9100b981f25846e8868fff1273a5406f", "e93cdfb17d8d476fb001adc16c7268c3", "c32c71ce066f4eb48d13a72554c5814c", "d18f1172093f45b9b8f3089c85caf70f", "5157085005b4452f81d889683f36fab4", "fa014781493444e1be78a225b690d7cb", "93184b1011cb40bca829d564b94c0f41", "92988e0473fa4d69aa338e2bfdde6c09", "6c6d420877774fe2bd2935bc5774d20d", "cf5f6c3d02f946698da1a0ddff3e137f", "b42a6a3fc94d44afbc3b3b680632b997", "aef9a07d10334e64bcf8c69823f204a3" ] }, "id": "w0C7RtlnDDMG", "outputId": "e08aa0d1-c38a-434c-af2f-b1197cf636d6" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "fd885c17078a4d299834081f0c985de1", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Loading checkpoint shards: 0%| | 0/6 [00:00: How can I create a dataframe?\n", ":\n" ] } ], "source": [ "prompt = f\"\"\"\n", ": How can I create a dataframe?\n", ":\n", "\"\"\".strip()\n", "print(prompt)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "id": "JtkUhj4LJqf7" }, "outputs": [], "source": [ "generation_config = model.generation_config\n", "generation_config.max_new_tokens = 200\n", "generation_config.temperature = 0.7\n", "generation_config.top_p = 0.7\n", "generation_config.num_return_sequences = 1\n", "generation_config.pad_token_id = tokenizer.eos_token_id\n", "generation_config.eos_token_id = tokenizer.eos_token_id" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "MALtvmMAKo8p", "outputId": "3bc6e3c5-dab6-41f5-83eb-d1978ee7f5f0" }, "outputs": [ { "data": { "text/plain": [ "GenerationConfig {\n", " \"_from_model_config\": true,\n", " \"bos_token_id\": 32013,\n", " \"eos_token_id\": 32014,\n", " \"max_new_tokens\": 200,\n", " \"pad_token_id\": 32014,\n", " \"temperature\": 0.7,\n", " \"top_p\": 0.7,\n", " \"transformers_version\": \"4.30.0.dev0\"\n", "}" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "generation_config" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "kSiNe3tdPVxQ", "outputId": "ee675dc5-8f36-4bbf-f623-5f8d2987a2c9" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ": How can I create a dataframe?\n", ":<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\n", "CPU times: user 26.3 s, sys: 215 ms, total: 26.5 s\n", "Wall time: 26.6 s\n" ] } ], "source": [ "%%time\n", "device = \"cuda:0\"\n", "\n", "encoding = tokenizer(prompt, return_tensors=\"pt\").to(device)\n", "with torch.inference_mode():\n", " outputs = model.generate(\n", " input_ids=encoding.input_ids,\n", " attention_mask=encoding.attention_mask,\n", " generation_config=generation_config,\n", " )\n", "print(tokenizer.decode(outputs[0], skip_special_tokens=True))" ] }, { "cell_type": "markdown", "metadata": { "id": "ueJ2Vz4jZZiD" }, "source": [ "## Build HuggingFace Dataset" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 169, "referenced_widgets": [ "b611de9b3e514011a088f58ff31ce350", "8ca71f36e58041f79ed82e6e1f07ff4a", "8e755a4e8aa444df834a366fe9260ed2", "b19a05190437458aaedf7fc61145a00f", "3a495ad4428d438283522039656eaef2", "56f6933f28d349888dac2b73921782a8", "4c0a6d19f68e49529840b5c80ae78d76", "ba812d07977e43358a5130c2af70687e", "f6896448f0dc42a8be0fb25b2b94f83c", "60bff524329a4c9882500b7f31ac0c37", "9928a41f91044722b8dd2389e81276a5", "e51206049b1545d683fe28c540426ea2", "89635c3f4fc343298d2c58888e9cd980", "67e6e4e612b9421aa4e186a206d457a2", "92930f5ac19f4f54a3a9423cbb6979a9", "1b7742e7a7504078a8b68a6bd0c521ed", "13263d7a39864fb194a6c91b5a48e802", "39358e67401245a2bcc2994b9cac4d6c", "f7caa8e7df79436faaf136432c901cf9", "1032e908681f40aa8d566897d51b6598", "c7580381d0414cd09e40ffc184f09f5e", "02cee7026f9746248a4cb0f69fca33d9", "470e6c9204834004bd7acaf2a204412c", "0ab12caa6f0b4301990689fc1eaf50e6", "400d9309e2454c51b1102efd420faf55", "96c76412d4894173a2156af39218f5ec", "44fba98889f94e33b79c4afbfe82526b", "a24c5875f13c4d9d88dbbd4e13b92d38", "eb53f7c4414f41b794368ed81d4a93be", "46cdfc23171f4092ada064b4d09807d0", "62ced5c0ecee49d799120d7510101b7b", "d3f53b523df64cd4b6d7aa82f7a52fde", "327510df2cda41e686006e78df7c3754", "a24c987360d44833a1a4413bcf18a746", "3b6c71d7208b41f5a75e2e6e02e19716", "70769033cdf2477685f759456bc1517f", "2a43bf59688d4882b5aefe836f23a72e", "ebb88a5641454ab4ab8a160a004d242b", "7ce1191146a54e5f963257ce403042d6", "f74a6600a8384d0f9fe25537d4c9804c", "b3402c4fbaa7440680d314e7bf13f96a", "e4044dd7d4e14911b2db2386cc6b3de4", "dd5cadc44ff6427ea44c548129904a36", "59a22587291a494abfe00edbf9538bc9" ] }, "id": "7IB_p05RZDf5", "outputId": "1498b8c2-181e-4331-8618-44047e1957dd" }, "outputs": [], "source": [ "data = load_dataset(\"json\", data_files=\"ds1000-train-cleaned.json\")" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "v_FAbolgZRu4", "outputId": "af0f6188-65f3-4b8a-b078-9f5d2dd409e4" }, "outputs": [ { "data": { "text/plain": [ "DatasetDict({\n", " train: Dataset({\n", " features: ['answer', 'question'],\n", " num_rows: 897\n", " })\n", "})" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "bYEo913waIyw", "outputId": "72b2407b-9b4a-4b07-a5d8-e9f0ede040a2" }, "outputs": [], "source": [ "# data[\"train\"][0]" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "id": "NuYZwgpeZMG3" }, "outputs": [], "source": [ "def generate_prompt(data_point):\n", " return f\"\"\"\n", ": {data_point[\"question\"]}\n", ": {data_point[\"answer\"]}\n", "\"\"\".strip()\n", "\n", "\n", "def generate_and_tokenize_prompt(data_point):\n", " full_prompt = generate_prompt(data_point)\n", " tokenized_full_prompt = tokenizer(full_prompt, padding=True, truncation=True)\n", " return tokenized_full_prompt" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 17, "referenced_widgets": [ "d7f66a3d4cca48888cfff3143945e652", "3d229bccf7624f08a4c78ad213355de7", "25e0fc2e4bf141eba59811abf2e1c282", "1f42b0cc249f4cb3b5bfa6f247e50fc6", "922e425bc49c43f781667f77b0b29d12", "6cccb84976b2446b88df1cc4667ea943", "b2097e685da149188c4f4d5608f9c0be", "fc9ca28bf7414551810fc681fbba3bbf", "537ad938b14f4a9abc01fdd747ab2682", "7c3e56df2e654cc5889d4f5ec3d54917", "bf18c5ef06744f4cbc7c3448af17985e" ] }, "id": "SoyzUmMNY7fN", "outputId": "453ba6d0-38e9-45b9-b2f6-a2085b425c99" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9fe9ca8fcf9b4825b9a262fda9ae9286", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Map: 0%| | 0/897 [00:00\n", " \n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%load_ext tensorboard\n", "%tensorboard --logdir experiments/runs\n", "%reload_ext tensorboard" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "G0tXhYO5ea5l", "outputId": "00c73bb3-0e4a-45d3-c235-9088b79477de", "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "You're using a LlamaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n", "/opt/conda/lib/python3.10/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n", " warnings.warn(\n" ] }, { "data": { "text/html": [ "\n", "
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StepTraining Loss
113.098800
213.048300
313.306200
413.201500
512.833000
611.263900
711.254500
89.762500
98.756900
107.434900
116.682500
125.917600
135.254400
145.121800
154.021000
163.738400
173.626200
183.464700
193.121000
202.591900
212.441300
222.239800
231.953600
241.947700
252.155100
261.642400
271.526800
281.712300
291.177600
301.286600
311.244600
321.238200
331.431700
341.178200
351.132500
360.744700
371.574000
381.171600
391.036600
401.207000
411.098000
420.682900
431.237700
441.030800
451.009700
460.952200
471.284700
480.950900
491.146300
501.043400
511.105700
521.178600
530.713800
541.203200
550.783500
560.894600
571.049400
580.886800
590.774900
600.735200
610.829800
620.985800
630.860800
641.090300
650.968900
660.766600
671.027200
681.426600
690.817600
700.954100
710.943500
721.138100
730.955000
741.066400
750.714300
761.025000
770.689200
780.783400
790.781600
800.838400
810.731500
820.901400
830.802100
841.113400
850.755600
860.845900
871.089000
881.094800
891.035400
900.824700
910.899000
920.897400
931.172800
941.036700
950.878500
960.890800
970.872300
980.854800
991.054900
1001.077300
1010.820400
1021.013900
1030.903800
1041.006800
1051.019600
1060.922600
1070.787000
1080.783300
1090.599800
1100.853000
1110.788900
1120.677800
1131.074000
1140.813000
1151.067300
1160.730000
1170.936800
1180.852000
1190.769600
1200.876800
1210.965800
1221.302800
1230.890500
1240.844400
1250.914500
1260.831100
1271.012000
1280.933600
1290.750600
1300.695000
1310.944900
1320.907700
1330.851100
1340.911900
1350.914100
1360.922600
1370.921800
1380.580200
1390.880200
1400.689700
1410.662000
1421.098300
1430.895300
1440.875000
1451.023400
1460.606500
1470.867700
1481.160800
1490.899800
1500.679200
1510.701300
1521.261700
1530.837900
1540.955800
1551.104200
1560.684500
1570.828300
1580.778700
1590.596200
1600.691600

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "TrainOutput(global_step=160, training_loss=1.8560316052287817, metrics={'train_runtime': 518.0598, 'train_samples_per_second': 1.235, 'train_steps_per_second': 0.309, 'total_flos': 5380710900916224.0, 'train_loss': 1.8560316052287817, 'epoch': 0.71})" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "training_args = transformers.TrainingArguments(\n", " per_device_train_batch_size=1,\n", " gradient_accumulation_steps=4,\n", " num_train_epochs=1,\n", " learning_rate=2e-4,\n", " fp16=True,\n", " save_total_limit=3,\n", " logging_steps=1,\n", " output_dir=OUTPUT_DIR,\n", " max_steps=160,\n", " optim=\"paged_adamw_8bit\",\n", " lr_scheduler_type=\"cosine\",\n", " warmup_ratio=0.05,\n", " report_to=\"tensorboard\",\n", ")\n", "\n", "trainer = transformers.Trainer(\n", " model=model,\n", " train_dataset=data,\n", " args=training_args,\n", " data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),\n", ")\n", "model.config.use_cache = False\n", "trainer.train()" ] }, { "cell_type": "markdown", "metadata": { "id": "XEMYxmFFZevu" }, "source": [ "## Save Trained Model" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "id": "__D-YiF4i4E-" }, "outputs": [], "source": [ "model.save_pretrained(\"trained-model\")" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 240, "referenced_widgets": [ "1765001f2e8a45789c303bf25802ec26", "b9342727e5b04a31a44a2bb39eb9b972", "fb9cd1d7b199467ca84fdee089c3e616", "cc2c910395a14a1b876e76a9f1967159", "e6870d171e2e4f49b29940a584f8234f", "6a21b3db09d043a09364c677e380fd1a", "b47756ecdc5544fb881978069650dff7", "8623daa5b6104e01aa049cce796d57a5", "6a369f47577d4a1ab89fc34e893295e3", "ec6c022167c549949cc2d4261e6b388a", "defa462b8d0c425b8a3c5c10d3824449", "ed46a43aa3b74664b7be0c5393af55c7", "ea756bf0eec44265a894ca2c007ecdd3", "6e2ec6222dbc4dafbbe3379d89e74a33", "05c0d40b5c01447790d0ceccebc03d0f", "3e72185f6ba44316818d958ff112e6a5", "535b91b499d64a19a74ad64b96ff520b", "b20a32370ca94e4490ec2541fedf4629", "c770c0437f13471092d8b7bd92169f27", "19e71863a0384ec8851bde3abbb3fa68", "d0d5e191b9ea48fc83e8d14eef28807e", "55aa6981a6304872b253b5452dd0399f", "7cf8792fe92346e4be71b1cc0707c40f", "95ea93ef06164a6792df160fc5a856e1", "2c8a23857bfb41a28a4dcbaf360c5152", "d81c35bd6c964b8396a93ae5ce9b7035", "5f5378a779524d05bd130689944face8", "7a12995dd12f4aa8ac273ed26fe8c774", "e5a51c252b4642828528866ef7a71f61", "996b7bc4804747bc8e3d3bff8c09bc4e", "ef942a8f706549fd9d0ff97019b23d07", "1f5ec464af694baeb020c677c9804203", "05b4e2dcdb774de3847a1af4328a2d1e" ] }, "id": "SBTcZs_EODfg", "outputId": "4727fb36-a7a7-4f27-f6c7-ce93ced846d9" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.10/site-packages/huggingface_hub/_commit_api.py:282: UserWarning: About to update multiple times the same file in the same commit: 'adapter_model.bin'. This can cause undesired inconsistencies in your repo.\n", " warnings.warn(\n", "/opt/conda/lib/python3.10/site-packages/huggingface_hub/_commit_api.py:282: UserWarning: About to update multiple times the same file in the same commit: 'adapter_config.json'. This can cause undesired inconsistencies in your repo.\n", " warnings.warn(\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f63eb70685e141d9844ff71b1ff5d54b", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Upload 2 LFS files: 0%| | 0/2 [00:00: How can I create a dataframe?\n", ": import pandas as pd\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", "\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", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "CPU times: user 26.2 s, sys: 127 ms, total: 26.4 s\n", "Wall time: 26.5 s\n" ] } ], "source": [ "%%time\n", "prompt = f\"\"\"\n", ": How can I create a dataframe?\n", ":\n", "\"\"\".strip()\n", "\n", "encoding = tokenizer(prompt, return_tensors=\"pt\").to(DEVICE)\n", "with torch.inference_mode():\n", " outputs = model.generate(\n", " input_ids=encoding.input_ids,\n", " attention_mask=encoding.attention_mask,\n", " generation_config=generation_config,\n", " )\n", "print(tokenizer.decode(outputs[0], skip_special_tokens=True))" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "id": "ZxDcYoX9Tor2" }, "outputs": [], "source": [ "def generate_response(question: str) -> str:\n", " prompt = f\"\"\"\n", ": {question}\n", ":\n", "\"\"\".strip()\n", " encoding = tokenizer(prompt, return_tensors=\"pt\").to(DEVICE)\n", " with torch.inference_mode():\n", " outputs = model.generate(\n", " input_ids=encoding.input_ids,\n", " attention_mask=encoding.attention_mask,\n", " generation_config=generation_config,\n", " )\n", " response = tokenizer.decode(outputs[0], skip_special_tokens=True)\n", "\n", " assistant_start = \":\"\n", " response_start = response.find(assistant_start)\n", " return response[response_start + len(assistant_start) :].strip()" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "5cao6k4nWdht", "outputId": "624d4145-4d16-467d-deff-fb1993673737" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "n>: How to merge two dataframes?\n", ": import pandas as pd\n", "\n", "\n", "\n", "\n", "\n", "df1 = pd.DataFrame({'A': ['A', 'B', 'C', 'D'],\n", " 'B': [1, 2, 3, 4]})\n", "df2 = pd.DataFrame({'A': ['A', 'B', 'C', 'E'],\n", " 'B': [1, 2, 3, 5]})\n", "# merge df1 and df2 on column 'A'\n", "# SOLUTION START\n", "\n", ": import pandas as pd\n", "\n", "\n", "\n", "\n", "\n", "df1 = pd.DataFrame({'A': ['A', 'B', 'C', 'D'],\n", " 'B': [1, 2, 3, 4]})\n", "df2 = pd.DataFrame({'A': ['A', 'B', 'C', '\n" ] } ], "source": [ "prompt = \"How to merge two dataframes?\"\n", "print(generate_response(prompt))" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "C-INBeW-WmBS", "outputId": "1aa200f2-2d04-41b1-bb27-40ab74ccabff" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "n>: given two arrays name=['joy','shan'], roll_no=[1,2]. put these array in a dataframe ?\n", ": import pandas as pd\n", "\n", "\n", "name = ['joy', 'shan']\n", "roll_no = [1, 2]\n", "df = pd.DataFrame({'name': name, 'roll_no': roll_no})\n", "print(df)\n" ] } ], "source": [ "prompt = \"given two arrays name=['joy','shan'], roll_no=[1,2]. put these array in a dataframe ?\"\n", "print(generate_response(prompt))" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "O-UNWcvAVaRu", "outputId": "c0cd09ff-87dc-4dae-e5ee-d0f9f9fb230e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "n>: Problem:\n", "I have the following dataframe:\n", "index = range(14)\n", "data = [1, 0, 0, 2, 0, 4, 6, 8, 0, 0, 0, 0, 2, 1]\n", "df = pd.DataFrame(data=data, index=index, columns = ['A'])\n", "\n", "\n", "How can I fill the zeros with the maximun between previous and posterior non-zero value using pandas? Is there a fillna that is not just for \"NaN\"?. \n", "The output should look like:\n", " A\n", "0 1\n", "1 2\n", "2 2\n", "3 2\n", "4 4\n", "5 4\n", "6 6\n", "7 8\n", "8 8\n", "9 8\n", "10 8\n", "11 8\n", "12 2\n", "13 1\n", "\n", ": import pandas as pd\n", "\n", "\n", "index = range(14)\n", "data = [1, 0, 0, 2, 0, 4, 6, 8, 0, 0, 0, 0, 2, 1]\n", "df = pd.DataFrame(data=data, index=index, columns = ['A'])\n", "def g(df):\n", " df['A'] = df['A'].replace(0, np.nan)\n", " df['A'] = df['A'].fillna(method='ffill')\n", " df['A'] = df['A'].fillna(method='bfill')\n", " return df\n", "\n", "df = g(df.copy())\n", "result = df\n", "print(result)\n", ": import pandas as pd\n", "import numpy as np\n", "\n", "\n", "index = range(14)\n", "data\n" ] } ], "source": [ "prompt = \"\"\"Problem:\n", "I have the following dataframe:\n", "index = range(14)\n", "data = [1, 0, 0, 2, 0, 4, 6, 8, 0, 0, 0, 0, 2, 1]\n", "df = pd.DataFrame(data=data, index=index, columns = ['A'])\n", "\n", "\n", "How can I fill the zeros with the maximun between previous and posterior non-zero value using pandas? Is there a fillna that is not just for \"NaN\"?. \n", "The output should look like:\n", " A\n", "0 1\n", "1 2\n", "2 2\n", "3 2\n", "4 4\n", "5 4\n", "6 6\n", "7 8\n", "8 8\n", "9 8\n", "10 8\n", "11 8\n", "12 2\n", "13 1\n", "\"\"\"\n", "\n", "print(generate_response(prompt))" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "n>: Problem:\n", "i got an issue over ranking of date times. Lets say i have following table.\n", "ID TIME\n", "01 2018-07-11 11:12:20\n", "01 2018-07-12 12:00:23\n", "01 2018-07-13 12:00:00\n", "02 2019-09-11 11:00:00\n", "02 2019-09-12 12:00:00\n", "\n", "\n", "and i want to add another column to rank the table by time for each id and group. I used \n", "df['RANK'] = data.groupby('ID')['TIME'].rank(ascending=True)\n", "\n", "\n", "but get an error:\n", "'NoneType' object is not callable\n", "\n", "\n", "If i replace datetime to numbers, it works.... any solutions?\n", "\n", ": import pandas as pd\n", "\n", "\n", "df = pd.DataFrame({'ID': ['01', '01', '01', '02', '02'],\n", " 'TIME': ['2018-07-11 11:12:20', '2018-07-12 12:00:23', '2018-07-13 12:00:00', '2019-09-11 11:00:00', '2019-09-12 12:00:00']})\n", "def g(df):\n", " df['TIME'] = pd.to_datetime(df['TIME'])\n", " df['RANK'] = df.groupby('ID')['TIME'].rank(ascending=True\n" ] } ], "source": [ "prompt = \"\"\"Problem:\n", "i got an issue over ranking of date times. Lets say i have following table.\n", "ID TIME\n", "01 2018-07-11 11:12:20\n", "01 2018-07-12 12:00:23\n", "01 2018-07-13 12:00:00\n", "02 2019-09-11 11:00:00\n", "02 2019-09-12 12:00:00\n", "\n", "\n", "and i want to add another column to rank the table by time for each id and group. I used \n", "df['RANK'] = data.groupby('ID')['TIME'].rank(ascending=True)\n", "\n", "\n", "but get an error:\n", "'NoneType' object is not callable\n", "\n", "\n", "If i replace datetime to numbers, it works.... any solutions?\n", "\"\"\"\n", "print(generate_response(prompt))" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "n>: can you plot all types of plots in matplotlib?\n", ": import matplotlib.pyplot as plt\n", "\n", "x = [1, 2, 3, 4, 5]\n", "y = [1, 2, 3, 4, 5]\n", "\n", "# plot all types of plots in matplotlib\n", "# SOLUTION START\n", "\n", ": import matplotlib.pyplot as plt\n", "\n", "x = [1, 2, 3, 4, 5]\n", "y = [1, 2, 3, 4, 5]\n", "\n", "# plot all types of plots in matplotlib\n", "plt.plot(x, y, label=\"plot\")\n", "plt.scatter(x, y, label=\"scatter\")\n", "plt.bar(x, y, label=\"bar\")\n", "plt.hist(x, y, label=\"hist\")\n", "plt.boxplot(\n" ] } ], "source": [ "prompt = \"can you plot all types of plots in matplotlib?\"\n", "print(generate_response(prompt))" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "from huggingface_hub import HfApi\n", "api = HfApi()" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f3fd19094f47468996a3819a6e2ce4fd", "version_major": 2, "version_minor": 0 }, "text/plain": [ "pytorch_model.bin: 0%| | 0.00/4.33G [00:00
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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