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Immediately click login after copying\nyour token or it might be stored in plain text in this notebook file.
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"markdown", + "source": [ + "#Create instructions fine-tuning dataset for LLAMA 2 lora tuning\n", + "\n", + "we will use machine learning topics available in PDF for context.\n", + "utilize openai 3.5 turbo to generate json records for 2 instructions fine-tuning tasks\n", + "\n", + "1. summarization\n", + "2. creating multiple choice questions (MCQ)\n", + "\n", + "\n", + "final dataset is stored in hugging face.\n", + "\n", + "MCQ only in nested dictionary format. see MCQ expected output json format sample below\n", + "[apailang/dataset_mcq_table](https://huggingface.co/datasets/apailang/dataset_mcq_table)\n", + "\n", + "and\n", + "\n", + "summarization and mcq in dinctionary string format. see Summarization expected output json format sample below\n", + "[apailang/mini-dataset](https://huggingface.co/datasets/apailang/mini-dataset)\n", + "---\n", + "\n", + "\n", + "\n", + "####Summarization expected output json format.\n", + "\n", + "\n", + "```\n", + "\n", + "\n", + "[{\n", + " \"instruction\": \"Summarize the concept and applications of AI\",\n", + "\n", + " \"input_content\": \"AI refers to the development of computer systems that can perform tasks that would normally require human intelligence. Some examples of AI applications .....\",\n", + "\n", + " \"expected_output\": \"AI, or artificial intelligence, involves the creation of computer systems capable of performing tasks that typically require human intelligence. Examples of AI applications include speech recognition systems used in self-driving cars, security systems, and medical imaging. AI algorithms are also used for personalized recommendations on e-commerce sites and streaming services, predictive maintenance in various industries, medical diagnosis systems, autonomous vehicles, fraud detection in financial institutions, image recognition in photo organization and security systems, natural language processing in chatbots and language translation systems, and predictive analytics in healthcare and marketing.\"\n", + " }]\n", + "```\n", + "\n", + "\n", + "####MCQ expected output json format.\n", + "\n", + "\n", + "\n", + "```\n", + "[\n", + " {\n", + " \"instruction\": \"Create an MCQ on the structure of artificial neural networks\",\n", + " \"input_content\": \"Artificial neural networks are built on the principles of the structure and operation of human neurons.......\",\n", + "\n", + " \"expected_output\": {\n", + " \"question\": \"What is the structure of an artificial neural network?\",\n", + " \"options\": {\n", + " \"A\": \"It consists of input layers and hidden layers only\",\n", + " \"B\": \"It consists of input layers, hidden layers, and output layers\",\n", + " \"C\": \"It consists of input layers, hidden layers, output layers, and artificial neurons\",\n", + " \"D\": \"It consists of input layers, hidden layers, output layers, artificial neurons, and weighted connections\"\n", + " },\n", + " \"correct_answer\": \"C\",\n", + " \"explanation\": \"An artificial neural network consists of input layers, hidden layers, output layers, and artificial neurons. The input layer receives data from the outside world, the hidden layers process and compute the weighted total, and the output layer provides the final response. Artificial neurons are the units within the network, and the connections between neurons are weighted to optimize the impact of inputs from the preceding layer.\"\n", + " }\n", + " }\n", + "]\n", + "```\n", + "\n", + "\n", + "\n", + "\n", + "---\n", + "\n" + ], + "metadata": { + "id": "iYeJEWUdAsVk" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install openai==0.28\n", + "!pip install PyPDF2\n", + "!pip install --upgrade tiktoken\n", + "!pip install -q datasets transformers sentence_transformers faiss-gpu huggingface_hub" + ], + "metadata": { + "id": "9s76sLpIfeUK" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "###import libraries" + ], + "metadata": { + "id": "_SZpV4KiAMd8" + } + }, + { + "cell_type": "code", + "source": [ + "# Import the required libraries\n", + "import openai\n", + "import PyPDF2\n", + "\n", + "import json\n", + "import pandas as pd\n", + "from datasets import Dataset, DatasetDict, concatenate_datasets, load_dataset\n", + "\n", + "from transformers import AutoTokenizer\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "from sentence_transformers import SentenceTransformer\n", + "import faiss\n", + "from tqdm.autonotebook import tqdm\n", + "import numpy as np" + ], + "metadata": { + "id": "m8Du-508eujO" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from huggingface_hub import notebook_login\n", + "notebook_login()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 145, + "referenced_widgets": [ + "aad9177750614696ae2cbc05084ca901", + "683e84ac0d30498680a705564f1b8fd0", + "5d28187910324c76b603f28caeac8834", + "a7794614c3aa4b10a468437e265b7a87", + "a23d8258a10141889cf520ae33e9a5a6", + "6a0cac18ca21476991c265ef20a32edb", + "5d0bf9b3947640c6abf6a9bbef95d444", + "6dee3243999141efabfbe3adaf9037b6", + "b1c3daa957c84294bae86e4ad6706756", + "204fed5d2e954d1482303016792791a2", + "7879690e674d4d92bc2391571fa57868", + "2e38bdd8bd1a4d1f83a69f59d4072f6e", + "c1e7fe0e092e4b4db19d322f5a338cc3", + "dc129a2f7bf14b34bb24b121f1f88a76", + "214dbc6b261b4c3fa9d19228dce132ce", + "7c0a38bb3bd64ed38831141661ebea1c", + "464829ba3cdb469a95a0f4a9089d75bf", + "2db0f2ff3d484d778c397625e60b23a5", + "fa4c40830976491fa9d3175617e296b5", + "096c770097c147f2b989e300327fcf35", + "bea3753676194ce199cbac34437fe02c", + "63ac6dcce5ad41eab94c677e4ced1739", + "96c1efa0809e4e5dbf4e01459ad9874d", + "30485ee212864fe8bf4df32915d9bd05", + "0ac44a37168b4130bab4438776df3e97", + "747988d1b2244e9496e9e2565d6e98c6", + "c99133095ad544a891d3f1f26c21870a", + "2bb9477a98c347e8830827ef6a39b9b4", + "65bfc48f5abe4f94aab1b833f0381b8b", + "24628bf215594e24a22364137a0ed28b", + "06e9ba8e64704c1abbcfd34230e77eea", + "4ba8236cc5834185a2c652472b3a8abc" + ] + }, + "id": "W2AwoT_mWCT0", + "outputId": "6c3b17bf-214f-484c-c83a-c6009f677efd" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "VBox(children=(HTML(value='