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October 19, 2022 18:50
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| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/hargup/38e429f0b981c93bb2e899e4f19681c3/ai-sun-tzu.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
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| { | |
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| "id": "H7LoMj4GA4n_" | |
| }, | |
| "source": [ | |
| "# aitextgen — Train a GPT-2 (or GPT Neo) Text-Generating Model w/ GPU\n", | |
| "\n", | |
| "by [Max Woolf](https://minimaxir.com)\n", | |
| "\n", | |
| "*Last updated: May 16th, 2021 (aitextgen v0.5.2)*\n", | |
| "\n", | |
| "Retrain an advanced text generating neural network on any text dataset **for free on a GPU using Colaboratory** using `aitextgen`!\n", | |
| "\n", | |
| "For more about `aitextgen`, you can visit [this GitHub repository](https://github.com/minimaxir/aitextgen) or [read the documentation](https://docs.aitextgen.io/).\n", | |
| "\n", | |
| "\n", | |
| "To get started:\n", | |
| "\n", | |
| "1. Copy this notebook to your Google Drive to keep it and save your changes. (File -> Save a Copy in Drive)\n", | |
| "2. Run the cells below:\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "KBkpRgBCBS2_", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "560cca25-701c-4f27-a941-329bd0b5cd31" | |
| }, | |
| "source": [ | |
| "!pip install -q aitextgen\n", | |
| "\n", | |
| "import logging\n", | |
| "logging.basicConfig(\n", | |
| " format=\"%(asctime)s — %(levelname)s — %(name)s — %(message)s\",\n", | |
| " datefmt=\"%m/%d/%Y %H:%M:%S\",\n", | |
| " level=logging.INFO\n", | |
| " )\n", | |
| "\n", | |
| "from aitextgen import aitextgen\n", | |
| "from aitextgen.colab import mount_gdrive, copy_file_from_gdrive" | |
| ], | |
| "execution_count": 1, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\u001b[K |████████████████████████████████| 572 kB 5.0 MB/s \n", | |
| "\u001b[K |████████████████████████████████| 5.3 MB 42.8 MB/s \n", | |
| "\u001b[K |████████████████████████████████| 87 kB 6.8 MB/s \n", | |
| "\u001b[K |████████████████████████████████| 708 kB 81.1 MB/s \n", | |
| "\u001b[K |████████████████████████████████| 529 kB 83.1 MB/s \n", | |
| "\u001b[K |████████████████████████████████| 7.6 MB 78.4 MB/s \n", | |
| "\u001b[K |████████████████████████████████| 163 kB 96.4 MB/s \n", | |
| "\u001b[?25h Building wheel for aitextgen (setup.py) ... \u001b[?25l\u001b[?25hdone\n", | |
| " Building wheel for fire (setup.py) ... \u001b[?25l\u001b[?25hdone\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "Bj2IJLHP3KwE" | |
| }, | |
| "source": [ | |
| "## GPU\n", | |
| "\n", | |
| "Colaboratory uses a Nvidia P4, an Nvidia T4, an Nvidia P100, or an Nvidia V100. For finetuning GPT-2 124M, any of these GPUs will be fine, but for text generation, a T4 or a P100 is ideal since they have more VRAM. **If you receive a T4 or a V100 GPU, you can enable `fp16=True` during training for faster/more memory efficient training.**\n", | |
| "\n", | |
| "You can verify which GPU is active by running the cell below. If you want to try for a different GPU, go to **Runtime -> Factory Reset Runtime**." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "sUmTooTW3osf", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "cc57f786-7fd5-4650-e026-c7caca7b8255" | |
| }, | |
| "source": [ | |
| "!nvidia-smi" | |
| ], | |
| "execution_count": 2, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Wed Oct 19 18:01:32 2022 \n", | |
| "+-----------------------------------------------------------------------------+\n", | |
| "| NVIDIA-SMI 460.32.03 Driver Version: 460.32.03 CUDA Version: 11.2 |\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 Tesla T4 Off | 00000000:00:04.0 Off | 0 |\n", | |
| "| N/A 43C P8 9W / 70W | 0MiB / 15109MiB | 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", | |
| "| No running processes found |\n", | |
| "+-----------------------------------------------------------------------------+\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "trRhgNvsH4Wn" | |
| }, | |
| "source": [ | |
| "## Loading GPT-2 or GPT Neo\n", | |
| "\n", | |
| "If you're retraining a model on new text, you need to download and load the GPT-2 model into the GPU. \n", | |
| "\n", | |
| "There are several sizes of GPT-2:\n", | |
| "\n", | |
| "* `124M` (default): the \"small\" model, 500MB on disk.\n", | |
| "* `355M` (default): the \"medium\" model, 1.5GB on disk.\n", | |
| "* `774M` (default): the \"large\" model, 3GB on disk.\n", | |
| "\n", | |
| "You can also finetune a GPT Neo model instead, which is more suitable for longer texts and the base model has more recent data:\n", | |
| "\n", | |
| "* `125M`: Analogous to the GPT-2 124M model.\n", | |
| "* `350M`: Analogous to the GPT-2 355M model\n", | |
| "\n", | |
| "The next cell downloads the model and saves it in the Colaboratory VM. If the model has already been downloaded, running this cell will reload it." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "flqSlHjMIeIw", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 373, | |
| "referenced_widgets": [ | |
| "b9b9a8e60c704260a9ce40fcf73a1242", | |
| "d927e338551e43c6a7bcfa954036544d", | |
| "39a9e616fbd943b2aece70d14adf8440", | |
| "300520feda4040e29a039da6b41ec54a", | |
| "bf05e87d4c6d4940b8e8a248e95a3b66", | |
| "f1dfa815216743b38bf36f266acca85b", | |
| "47aa6faae5f644d79fc8c6b27413bb3c", | |
| "1ff7b43d46594e68a9e7a9c0a066e759", | |
| "fac4a200b04e4e5586519770995c38bf", | |
| "d3b899641b304996a9ca65a8479a203e", | |
| "a62a786be7b9438dabbc60c24d9f8d0c", | |
| "cf5cb1044bf545f4953b66b8e1a77304", | |
| "442fe4c33ffd487baf88b0ece7e35828", | |
| "adc8b626d723476a8b21f62c40b2c152", | |
| "5a556be70f8a4670a921b3ddb65f4b2b", | |
| "8b3e358ed53e4799a5f025615c0a343c", | |
| "c3be43896cff478ca76c5b4c8ff3136e", | |
| "780d6efce7ef49bcb9f0aee8fc1abf21", | |
| "3aedd73549f342218ada37b1d52ec61d", | |
| "889f27f6c03042bd991ce971d0cbd3d4", | |
| "7d10e6f1b7f84e6fb4458cf6e4e91761", | |
| "10d0189a4b4142629449ec2d0dce0ef0", | |
| "395cad8e43bd4633a8ed9cf3a411a5a2", | |
| "7040dc8f82f84845a8c41ecfd835cc83", | |
| "9fdcc8c5a83f4081adf18bc86b9df069", | |
| "313821b87d2c4c60bc43d8a09002a48e", | |
| "94c5c7c69d1a4c999a8271fe6f59346b", | |
| "20fd47265c4a49fc89188bea1231754e", | |
| "1fdc455f922741bf8e1028f39c6f204a", | |
| "7b8caa0a726842289fe7565bc6350755", | |
| "5dee6a6a7e344e758c760e3e3c7cf36f", | |
| "1e40630acb7b4a97874cf68a6a555be0", | |
| "0a940233bb4f41a8bacafc7286d28aa4", | |
| "a687c20cad714a2589b45994e9533c6e", | |
| "b4f3d5a731214c92b82d863d2669ee86", | |
| "88aea3a08c824904bd846c892bb9d4c8", | |
| "4711c4366a73416fa26ce1df9dc85a0f", | |
| "fe4088ad5431426f9bc1b0222f0253ab", | |
| "9eb41420176d4ff5bbeded54ef95e707", | |
| "23a3a0311d4e498dbc707e2f5443a83f", | |
| "da8b4106dbc14b8c803522f492b0470d", | |
| "bc1766d69b584e70b8d72f749e3fdf85", | |
| "ef6ce8332877481a9a9a63a97e57a8e6", | |
| "bb6a4620c7e64a208ed31629f1ee8dcf", | |
| "47fbb49c4c944b2d8327649d8e56a977", | |
| "58e687508e5240a99cc20540235f7066", | |
| "64f502804bd4454fb205d93c8c43b376", | |
| "7b59e812d23f419fb8d2dc24a01f7daa", | |
| "9b81269d38764de78ee5d7f81033c356", | |
| "2b6f4ccecc0c4cb8badf695af5126550", | |
| "f0527b93e12e4cc3b9eb15cdf8976b19", | |
| "69ec1e7d4c5e4c449924b552cb9ac936", | |
| "92fd31153c164e1aa310c4ca8e1f1c63", | |
| "c4e44fd9d5e44d13a4db18d4200555c0", | |
| "69e1285ba18144afbfffb456dc4d92f0", | |
| "c8085175efd8447a92b7368af58ea9ae", | |
| "d743f79dfd2d40efac9d288e864a2228", | |
| "e61266f822fd489ea34a7e0475a2105e", | |
| "4a352ae064854712b2076f11c6cc5485", | |
| "169d370d3dee44a084b025aa0108476d", | |
| "30f8249b09aa4f568ed428aa0625dc86", | |
| "b024e4769f964911a1e6434ec3a0d55d", | |
| "ee8f695ee7e649deb375334046e82269", | |
| "b82691a1c7c64ac89b2ef42c364d50c1", | |
| "e08b69050db949f7be8ad5d40646f7c8", | |
| "be14b400964a499d9a64e5cfb2b115cd" | |
| ] | |
| }, | |
| "outputId": "647cd82e-da06-4dc1-83d2-3e2f443e9135" | |
| }, | |
| "source": [ | |
| "# ai = aitextgen(tf_gpt2=\"124M\", to_gpu=True)\n", | |
| "\n", | |
| "# Comment out the above line and uncomment the below line to use GPT Neo instead.\n", | |
| "# Wondering what ohter models do they have\n", | |
| "ai = aitextgen(model=\"EleutherAI/gpt-neo-125M\", to_gpu=True)" | |
| ], | |
| "execution_count": 3, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "INFO:aitextgen:Downloading EleutherAI/gpt-neo-125M model to /aitextgen.\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Downloading: 0%| | 0.00/1.01k [00:00<?, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "b9b9a8e60c704260a9ce40fcf73a1242" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Downloading: 0%| | 0.00/526M [00:00<?, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "cf5cb1044bf545f4953b66b8e1a77304" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "INFO:aitextgen:Using the tokenizer for EleutherAI/gpt-neo-125M.\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Downloading: 0%| | 0.00/560 [00:00<?, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "395cad8e43bd4633a8ed9cf3a411a5a2" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Downloading: 0%| | 0.00/899k [00:00<?, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "a687c20cad714a2589b45994e9533c6e" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Downloading: 0%| | 0.00/456k [00:00<?, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "47fbb49c4c944b2d8327649d8e56a977" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Downloading: 0%| | 0.00/357 [00:00<?, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "c8085175efd8447a92b7368af58ea9ae" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "loading file vocab.json from cache at aitextgen/models--EleutherAI--gpt-neo-125M/snapshots/324e21bd3b56dfecba4308ab6ec147b588df23af/vocab.json\n", | |
| "loading file merges.txt from cache at aitextgen/models--EleutherAI--gpt-neo-125M/snapshots/324e21bd3b56dfecba4308ab6ec147b588df23af/merges.txt\n", | |
| "loading file tokenizer.json from cache at None\n", | |
| "loading file added_tokens.json from cache at None\n", | |
| "loading file special_tokens_map.json from cache at aitextgen/models--EleutherAI--gpt-neo-125M/snapshots/324e21bd3b56dfecba4308ab6ec147b588df23af/special_tokens_map.json\n", | |
| "loading file tokenizer_config.json from cache at aitextgen/models--EleutherAI--gpt-neo-125M/snapshots/324e21bd3b56dfecba4308ab6ec147b588df23af/tokenizer_config.json\n", | |
| "INFO:aitextgen:GPTNeo loaded with 125M parameters.\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "N8KXuKWzQSsN" | |
| }, | |
| "source": [ | |
| "## Mounting Google Drive\n", | |
| "\n", | |
| "The best way to get input text to-be-trained into the Colaboratory VM, and to get the trained model *out* of Colaboratory, is to route it through Google Drive *first*.\n", | |
| "\n", | |
| "Running this cell (which will only work in Colaboratory) will mount your personal Google Drive in the VM, which later cells can use to get data in/out. (it will ask for an auth code; that auth is not saved anywhere)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "puq4iC6vUAHc", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "ff5f88ea-0662-4c29-c08f-b490f07b78d8" | |
| }, | |
| "source": [ | |
| "mount_gdrive()" | |
| ], | |
| "execution_count": 4, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Mounted at /content/drive\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "BT__brhBCvJu" | |
| }, | |
| "source": [ | |
| "## Uploading a Text File to be Trained to Colaboratory\n", | |
| "\n", | |
| "In the Colaboratory Notebook sidebar on the left of the screen, select *Files*. From there you can upload files:\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "Upload **any smaller text file** (for example, [a text file of Shakespeare plays](https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt)) and update the file name in the cell below, then run the cell." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "6OFnPCLADfll" | |
| }, | |
| "source": [ | |
| "file_name = \"book_lionel_giles.txt\"" | |
| ], | |
| "execution_count": 5, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "HeeSKtNWUedE" | |
| }, | |
| "source": [ | |
| "If your text file is large (>10MB), it is recommended to upload that file to Google Drive first, then copy that file from Google Drive to the Colaboratory VM.\n", | |
| "\n", | |
| "Additionally, you may want to consider [compressing the dataset to a cache first](https://docs.aitextgen.io/dataset/) on your local computer, then uploading the resulting `dataset_cache.tar.gz` and setting the `file_name`in the previous cell to that." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "-Z6okFD8VKtS" | |
| }, | |
| "source": [ | |
| "copy_file_from_gdrive(file_name)" | |
| ], | |
| "execution_count": 6, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from google.colab import drive\n", | |
| "drive.mount('/content/drive')" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "AaLGjzvZG22Q", | |
| "outputId": "599b52a5-d2c1-40b8-a1ee-218e8736d2f2" | |
| }, | |
| "execution_count": 7, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "LdpZQXknFNY3" | |
| }, | |
| "source": [ | |
| "## Finetune GPT-2\n", | |
| "\n", | |
| "The next cell will start the actual finetuning of GPT-2 in aitextgen. It runs for `num_steps`, and a progress bar will appear to show training progress, current loss (the lower the better the model), and average loss (to give a sense on loss trajectory).\n", | |
| "\n", | |
| "The model will be saved every `save_every` steps in `trained_model` by default, and when training completes. If you mounted your Google Drive, the model will _also_ be saved there in a unique folder.\n", | |
| "\n", | |
| "The training might time out after 4ish hours; if you did not mount to Google Drive, make sure you end training and save the results so you don't lose them! (if this happens frequently, you may want to consider using [Colab Pro](https://colab.research.google.com/signup))\n", | |
| "\n", | |
| "Important parameters for `train()`:\n", | |
| "\n", | |
| "- **`line_by_line`**: Set this to `True` if the input text file is a single-column CSV, with one record per row. aitextgen will automatically process it optimally.\n", | |
| "- **`from_cache`**: If you compressed your dataset locally (as noted in the previous section) and are using that cache file, set this to `True`.\n", | |
| "- **`num_steps`**: Number of steps to train the model for.\n", | |
| "- **`generate_every`**: Interval of steps to generate example text from the model; good for qualitatively validating training.\n", | |
| "- **`save_every`**: Interval of steps to save the model: the model will be saved in the VM to `/trained_model`.\n", | |
| "- **`save_gdrive`**: Set this to `True` to copy the model to a unique folder in your Google Drive, if you have mounted it in the earlier cells\n", | |
| "- **`fp16`**: Enables half-precision training for faster/more memory-efficient training. Only works on a T4 or V100 GPU.\n", | |
| "\n", | |
| "Here are other important parameters for `train()` that are useful but you likely do not need to change.\n", | |
| "\n", | |
| "- **`learning_rate`**: Learning rate of the model training.\n", | |
| "- **`batch_size`**: Batch size of the model training; setting it too high will cause the GPU to go OOM. (if using `fp16`, you can increase the batch size more safely)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "aeXshJM-Cuaf", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 956, | |
| "referenced_widgets": [ | |
| "dfde7b96aeaf4df6b8bceb0ff2b1fabf", | |
| "ce432e0bb4ad499d934b73053f405d33", | |
| "def96c0c968d4c8f8c67b24dc45e71fd", | |
| "184490d8758d43a09ae4955dae9db452", | |
| "6100519370d548c1b1482bc9d7e73169", | |
| "a54c5369577d497694286a0e3210aa5a", | |
| "a31eb2c4a99742f880af4679624277f0", | |
| "9582cf4c21a54a14982b4d7a3b2ad93b", | |
| "98b45a361470433c8ce21e9308b65fda", | |
| "e68e0ef7f01f46f78731f55d6048ce7b", | |
| "5a55ca0d20f44cfa8da955ab67be0839", | |
| "a6669f768e304a6d91e5b8d7469e23b7", | |
| "4330ce43021640b6825fa3a069d64bcc", | |
| "530cfbe6b4f94a1fbe82c1593766fea5", | |
| "87f2ba55a138478584462362ffc106b1", | |
| "866bd776877149a7a4991bdfc7e9fc9f", | |
| "0dbcead90a804ac68d187d861129a4bd", | |
| "808d638383664996818adb3a78e43d2b", | |
| "565f978e2a694f1ea38dcdf96e7a9f5f", | |
| "c337bc1f3dc848efaefb63f96bb1ccce", | |
| "4d4680ef2407439bb6bc1c75a29c39d3", | |
| "086ca290b7eb47f799eeb78d6ed73efc" | |
| ] | |
| }, | |
| "outputId": "102986a6-12d2-4613-e59b-69a84101a946" | |
| }, | |
| "source": [ | |
| "ai.train(file_name,\n", | |
| " line_by_line=False,\n", | |
| " from_cache=False,\n", | |
| " num_steps=3000,\n", | |
| " generate_every=1000,\n", | |
| " save_every=1000,\n", | |
| " save_gdrive=False,\n", | |
| " learning_rate=1e-3,\n", | |
| " fp16=False,\n", | |
| " batch_size=1, \n", | |
| " )" | |
| ], | |
| "execution_count": 10, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "INFO:aitextgen:Loading text from book_lionel_giles.txt with generation length of 2048.\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| " 0%| | 0/396 [00:00<?, ?it/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "dfde7b96aeaf4df6b8bceb0ff2b1fabf" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "INFO:aitextgen.TokenDataset:Encoding 396 sets of tokens from book_lionel_giles.txt.\n", | |
| "WARNING:aitextgen:pytorch_model.bin already exists in /trained_model and will be overwritten!\n", | |
| "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n", | |
| "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n", | |
| "INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n", | |
| "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n", | |
| "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/configuration_validator.py:259: LightningDeprecationWarning: The `Callback.on_batch_end` hook was deprecated in v1.6 and will be removed in v1.8. Please use `Callback.on_train_batch_end` instead.\n", | |
| " f\"The `Callback.{hook}` hook was deprecated in v1.6 and\"\n", | |
| "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
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| " 0%| | 0/3000 [00:00<?, ?it/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "a6669f768e304a6d91e5b8d7469e23b7" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\u001b[1m1,000 steps reached: saving model to /trained_model\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "A decoder-only architecture is being used, but right-padding was detected! For correct generation results, please set `padding_side='left'` when initializing the tokenizer.\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\u001b[1m1,000 steps reached: generating sample texts.\u001b[0m\n", | |
| "==========\n", | |
| " which will succumb in a thousand li, and do not be unable to make these five dangerous faults. \n", | |
| " When you will launch his plans. \n", | |
| " When you will suffer a chieftains will reach the point that he must seem unable to carry far away from prolonged warfare. \n", | |
| " It is bent only thought is the cost of a kingdom that there is the appearance of handling large masses of handling large masses of handling large masses of the appearance of the art of handling large masses of handling large masses of handling large masses of the enemy. \n", | |
| " The skillful leader of handling large masses of handling large masses of handling large masses of handling large masses of handling large masses of his army. \n", | |
| " Hence, of handling but attacks with the enemy without authority; and though he has learnt the art of handling large masses of handling large masses of strength, and eyes of handling large masses of strength. \n", | |
| " The appearance of handling large masses of handling large masses of handling large masses of signal-in-in-fighting, and camp. \n", | |
| " Now this way, make a country, and distinct; thus,'s spirit of signal-fighting, and men. \n", | |
| " Hence this loss. \n", | |
| " Hence a loss. This is the art of signal-fighting, and on\n", | |
| "==========\n", | |
| "\u001b[1m2,000 steps reached: saving model to /trained_model\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "A decoder-only architecture is being used, but right-padding was detected! For correct generation results, please set `padding_side='left'` when initializing the tokenizer.\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\u001b[1m2,000 steps reached: generating sample texts.\u001b[0m\n", | |
| "==========\n", | |
| ") Disciplined; he captures their men; like the Empire at the Empire says, they will be damped, they will be damped, they will be freely traversed by making no other chieftains will be able to method and Victory of a war, is like unto rolling down. \n", | |
| " Thus it is as the consequences that is as water; fourthly, will have been won, after the enemy that is as the coming battle after the move; fourthly, and battle after the energy developed by good fighting with re-born captain.\n", | |
| " From a position which the enemy is no constant shape, but unable to move to strike at its periods of a position over the enemy is the enemy is the science of a town still remains untaken. \n", | |
| " Therefore the move; if round stone rolled down a mountain thousands of a mountain thousands of a mountain thousands of feet in the bending of feet in the enemy is first in the enemy is a mountain thousands of a mountain thousands of a mountain thousands of a mountain thousands of a mountain thousands of a mountain thousands of feet in the enemy is a clever combatant looks for it is the enemy is first in the enemy is first in the enemy is a mountain thousands of a mountain thousands of a mountain thousands of a clever\n", | |
| "==========\n", | |
| "\u001b[1m3,000 steps reached: saving model to /trained_model\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "A decoder-only architecture is being used, but right-padding was detected! For correct generation results, please set `padding_side='left'` when initializing the tokenizer.\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\u001b[1m3,000 steps reached: generating sample texts.\u001b[0m\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "INFO:pytorch_lightning.utilities.rank_zero:`Trainer.fit` stopped: `max_steps=3000` reached.\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "==========\n", | |
| " the enemy, but tell them.\n", | |
| " These six sovereign, and the enemy out baits to draw near a high column, and black), yet if our men to an ambuscade. \n", | |
| " When the enemy is coming. \n", | |
| " When the enemy is to begin by drinking themselves, the sign that is strong and kills its cattle for food, and when the enemy's numbers, and when the enemy's numbers, and when the enemy's numbers, and when the enemy's plans are the enemy's numbers, and when the enemy's numbers, and does not hang their cooking-pots over the enemy's numbers, showing that they will not return to their tents, you may know that they are determined to fight to the death. \n", | |
| " The sight of men whispering together in the death. \n", | |
| " Too frequent rewards signify that the enemy is the enemy is determined to the enemy is the enemy is at the enemy is a position is determined to the enemy is at the enemy is determined to the enemy is to the enemy is to the enemy is at the enemy is to the enemy is at the enemy is as the enemy is at the enemy is a position on the enemy is to the enemy is to the enemy is to the enemy is not return to the enemy is at the\n", | |
| "==========\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "INFO:aitextgen:Saving trained model pytorch_model.bin to /trained_model\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "qQJgV_b4bmzd" | |
| }, | |
| "source": [ | |
| "You're done! Feel free to go to the **Generate Text From The Trained Model** section to generate text based on your retrained model." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "pel-uBULXO2L" | |
| }, | |
| "source": [ | |
| "\n", | |
| "## Load a Trained Model\n", | |
| "\n", | |
| "If you already had a trained model from this notebook, running the next cell will copy the `pytorch_model.bin` and the `config.json`file from the specified folder in Google Drive into the Colaboratory VM. (If no `from_folder` is specified, it assumes the two files are located at the root level of your Google Drive)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "!cp -r trained_model /content/drive/MyDrive/" | |
| ], | |
| "metadata": { | |
| "id": "p3zV5kieR1JL" | |
| }, | |
| "execution_count": 11, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "DCcx5u7sbPTD" | |
| }, | |
| "source": [ | |
| "from_folder = \"trained_model\"\n", | |
| "\n", | |
| "for file in [\"pytorch_model.bin\", \"config.json\"]:\n", | |
| " if from_folder:\n", | |
| " copy_file_from_gdrive(file, from_folder)\n", | |
| " else:\n", | |
| " copy_file_from_gdrive(file)" | |
| ], | |
| "execution_count": 12, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "RTa6zf3e_9gV" | |
| }, | |
| "source": [ | |
| "The next cell will allow you to load the retrained model + metadata necessary to generate text." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "-fxL77nvAMAX", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "c121e706-20f9-4dcf-f236-e9eaa246f1a3" | |
| }, | |
| "source": [ | |
| "ai = aitextgen(model_folder=\".\", to_gpu=True)" | |
| ], | |
| "execution_count": 13, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "INFO:aitextgen:Loading model from provided weights and config in /..\n", | |
| "INFO:aitextgen:GPTNeo loaded with 125M parameters.\n", | |
| "INFO:aitextgen:Using the default GPT-2 Tokenizer.\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "ClJwpF_ACONp" | |
| }, | |
| "source": [ | |
| "## Generate Text From The Trained Model\n", | |
| "\n", | |
| "After you've trained the model or loaded a retrained model from checkpoint, you can now generate text.\n", | |
| "\n", | |
| "**If you just trained a model**, you'll get much faster training performance if you reload the model; the next cell will reload the model you just trained from the `trained_model` folder." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "mSvHhTuHJc-Q", | |
| "outputId": "1fbbd9e3-18bc-4f8b-c4a0-95cb4f51e6b5" | |
| }, | |
| "source": [ | |
| "ai = aitextgen(model_folder=\"trained_model\", to_gpu=True)" | |
| ], | |
| "execution_count": 14, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "INFO:aitextgen:Loading model from provided weights and config in /trained_model.\n", | |
| "INFO:aitextgen:GPTNeo loaded with 125M parameters.\n", | |
| "INFO:aitextgen:Using the default GPT-2 Tokenizer.\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "3cd0RGDbJiDp" | |
| }, | |
| "source": [ | |
| "`generate()` without any parameters generates a single text from the loaded model to the console." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "4RNY6RBI9LmL", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "c0a61f32-490d-42b4-fbaa-cb45e9c8d6de" | |
| }, | |
| "source": [ | |
| "ai.generate()" | |
| ], | |
| "execution_count": 15, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "A decoder-only architecture is being used, but right-padding was detected! For correct generation results, please set `padding_side='left'` when initializing the tokenizer.\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "to let them be substituted for the cowardly to take the enemy, then, is to augment one's fate, then, let them be victory, then, is the sovereign. \n", | |
| " The art of armies is not to besiege walled cities by Stratagem\n", | |
| " The rule is the nation shall be avoided. The preparation of mantlets, movable shelters, are not lengthy campaigns. \n", | |
| " Sun Tzu said: In the practical art of war, the enemy's country whole months; and the worst policy of mounds over which are not the enemy's country whole months; he is the worst policy of mounds over against the field.\n", | |
| " The general, they pass is the acme of excellence consists in order to shatter and the field; the sovereign, are not to shatter and the enemy's country whole months; he is the junction of the worst policy of war, are not to besiege walled cities if it is, are the worst policy of war, \" The general to besiege walled cities if it can possibly be avoided. The preparation of mantlets, movable shelters, and various implements of mantlets, will take up three whole months; and the piling up of mounds over against the ground.\n", | |
| " The general to the field and\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "oF4-PqF0Fl7R" | |
| }, | |
| "source": [ | |
| "If you're creating an API based on your model and need to pass the generated text elsewhere, you can do `text = ai.generate_one()`\n", | |
| "\n", | |
| "You can also pass in a `prompt` to the generate function to force the text to start with a given character sequence and generate text from there (good if you add an indicator when the text starts).\n", | |
| "\n", | |
| "You can also generate multiple texts at a time by specifing `n`. You can pass a `batch_size` to generate multiple samples in parallel, giving a massive speedup (in Colaboratory, set a maximum of 50 for `batch_size` to avoid going OOM).\n", | |
| "\n", | |
| "Other optional-but-helpful parameters for `ai.generate()` and friends:\n", | |
| "\n", | |
| "* **`min length`**: The minimum length of the generated text: if the text is shorter than this value after cleanup, aitextgen will generate another one.\n", | |
| "* **`max_length`**: Number of tokens to generate (default 256, you can generate up to 1024 tokens with GPT-2 and 2048 with GPT Neo)\n", | |
| "* **`temperature`**: The higher the temperature, the crazier the text (default 0.7, recommended to keep between 0.7 and 1.0)\n", | |
| "* **`top_k`**: Limits the generated guesses to the top *k* guesses (default 0 which disables the behavior; if the generated output is super crazy, you may want to set `top_k=40`)\n", | |
| "* **`top_p`**: Nucleus sampling: limits the generated guesses to a cumulative probability. (gets good results on a dataset with `top_p=0.9`)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "8DKMc0fiej4N", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "69d382d5-5803-43ac-eaa3-9fe643730d5f" | |
| }, | |
| "source": [ | |
| "ai.generate(n=5,prompt=\"China:\")" | |
| ], | |
| "execution_count": 16, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\u001b[1mChina:\u001b[0m not coming, but are five primary colors (1) Which of war the Moral Law causes, to Earth; (2) insubordination; (3) collapse; (4) ruin; (4) ruin; (5) ground; (5) disorganization; (4) ground; (5) disorganization; (4) rout. \n", | |
| " Other conditions being equal, if one force is hurled against another ten times its size, the former.\n", | |
| " When the former.\n", | |
| " When the common soldiers are too strong and the result will be bad. When the officers of a feeling of resentment, the officers too weak, the honor which is prepared for the soldiers too strong and the soldiers too weak, the result is insubordination. \n", | |
| " When the officers are likely to defeat. \n", | |
| " When the officers are able to defeat, and their officers are angry and insubordinate, and you fail to defeat, and their officers and they will fail to defeat, and their officers and men, and you fail to defeat, and their lives are not coming, and their kingdoms. \n", | |
| " When you fail to defeat, we may angry and do not go after you are angry and the enemy fail. \n", | |
| " If you can do not\n", | |
| "==========\n", | |
| "\u001b[1mChina:\u001b[0m this is to be quiet and black), yet no sign of a great object be tasted. \n", | |
| " Thus they will be tasted. \n", | |
| " But when the enemy is to Earth. \n", | |
| " In battle, that the enemy from the enemy is skillful fighter is sure to Earth. \n", | |
| " You can be to secure victory. \n", | |
| " Indirect tactics, not more than five give rise to begin anew; if not, then you must not, not, not lengthy campaigns. \n", | |
| " Thus you must not, but not, then you must not, not, not, to protect his country and conquer without coveting fame and do good service for his sovereign, is the jewel of the jewel of the jewel of the jewel of the jewel of the jewel of the jewel of the jewel of the jewel of the jewel of the jewel of the jewel of the jewel of the jewel of the jewel of the jewel of the jewel of the jewel of the jewel of the jewel of the jewel of the jewel of the jewel of the jewel of the jewel of the jewel of the jewel of the kingdom.\n", | |
| " Regard your soldiers as your children, and they will follow you into the jewel of the jewel of the jewel of the jewel of the jewel of the deepest valleys; look upon them\n", | |
| "==========\n", | |
| "\u001b[1mChina:\u001b[0m these two sovereign. \n", | |
| " The natural formation of both in order to the left hand. \n", | |
| " The natural formation of the left hand. So much for the rule is the shuai-jan is the left hand, yet these are not carry them will be terrible in turn his men and drums, but indirect; if an egg--that is the combinations of the State. \n", | |
| " In the State exchequer causes, that the State exchequer causes the hand. \n", | |
| " In war, but always equally matched, and do not always insists on the fact that the State will be quiet and black), yet if the hand, but always on the enemy's attack, but always equally predominant; if well supplied with food, he prevents the enemy is the enemy. \n", | |
| " In attacking, to go after him out, the enemy. \n", | |
| " In battle, the enemy. \n", | |
| " In all and safety on the enemy. \n", | |
| " Hence the enemy. \n", | |
| " There are not expected. \n", | |
| " Thus the enemy's not allow the enemy's provisions is the enemy's one's own strength. \n", | |
| " If the enemy's must hasten to be sure to be sure to secure victory, and not more than which there are not lengthy spies are not lengthy campaigns\n", | |
| "==========\n", | |
| "\u001b[1mChina:\u001b[0m this is skillful in winning with ease. \n", | |
| " The clever fighter is such as the four seasons make way for wisdom nor practices them, they will not more than five give rise to strike at its course. \n", | |
| " The general who advances without head; yet if its head; if the coming. \n", | |
| " There are not will not more than can cause will not lengthy operations in turn. \n", | |
| " There are not lengthy campaigns. \n", | |
| " Sun Tzu said: (blue, will take the danger. \n", | |
| " Thus the good general receives his sovereign, and concentrated his forces, collects his army and does not encamp.\n", | |
| " When in the sovereign, do not encamp. \n", | |
| " When in turning the sovereign.\n", | |
| " The general receives his army and circuitouszu said: Whoever, do good. \n", | |
| " Having collected an army and concentrated his forces, do not to take the sovereign. \n", | |
| " Having collected an army and concentrated his forces, but does not to the sovereign, do not to flag; and concentrated his forces, he must not, comes tactical dispositions, comes tactical dispositions, comes, comes tactical maneuvering, than which there is not understand these, does not, than which there is not be able to make the enemy's weapons will follow him.\n", | |
| "==========\n", | |
| "\u001b[1mChina:\u001b[0m (1) Dispersive ground; (2) facile ground; (3) contentious ground; (4) open ground. \n", | |
| " Ground which are not interfered with by which forms the enemy would suffice against a plot. \n", | |
| " Hence, and straightforwardness. \n", | |
| " Surviving spies. \n", | |
| " If you fail. \n", | |
| " When the enemy, when the enemy is sure to prepare against defeat. \n", | |
| " When the enemy means of a defeat the enemy means taking the enemy means of mind. \n", | |
| " When the enemy means that they will surely be maintained than those of their kingdoms. \n", | |
| " Without they will surely be victorious army is, we may concentrate from the enemy. \n", | |
| " If you may concentrate all points, but is sure to secure victory gained you fight and crush an enemy and not to protect ourselves; if not to defeat. \n", | |
| " If you fight and the enemy and do good fighter is open to defeat. \n", | |
| " If you fail to defeat. \n", | |
| " If you may know the enemy is open to defeat, but are in a defeat. \n", | |
| " Hence the enemy is within the enemy is destined to defeat, but is destined to defeat. \n", | |
| " Hence the enemy is fight and conquer without, but not the enemy. \n", | |
| "IV.\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "zjjEN2Tafhl2" | |
| }, | |
| "source": [ | |
| "For bulk generation, you can generate a large amount of texts to a file and sort out the samples locally on your computer. The next cell will generate `num_files` files, each with `n` texts and whatever other parameters you would pass to `generate()`. The files can then be downloaded from the Files sidebar!\n", | |
| "\n", | |
| "You can rerun the cells as many times as you want for even more generated texts!" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "Fa6p6arifSL0", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 269, | |
| "referenced_widgets": [ | |
| "ee63ddfcbfbd4c48a2966db4d07f184a", | |
| "3fcbe957b7204f5aa99934e7a5116c21", | |
| "b5de91230a1044788be4a9f48cc3b5e8", | |
| "ad426bffdf3d47cba83daec88607bfe9", | |
| "850bcbe4ae3f4e9f971792f6c885414e", | |
| "51f5883f16db45d190de9e0a557d0a6f", | |
| "642fa5ea58404abab6469413090385d8", | |
| "1cee3388e058427896508f2f90acb9ac", | |
| "3a9ae7b0de3c4e138a21dbcc1150c29c", | |
| "71a8cf5be7b74e0f95e8eec71772a38f", | |
| "7369990701ea46cda3970ec3fb40b925", | |
| "d183fab3d32d4a6a94f84cb449a02b28", | |
| "27be887aa31e4d2a9a76a145e7b140c6", | |
| "67f9fee6f51f4b51a8403878cc921587", | |
| "f159e9bb6f2b4e9891bed8313961672a", | |
| "f89aff29fcd5406285c43f07850ce58c", | |
| "76beb03f94ca4f87912be6f4badaf8e3", | |
| "638d2bf983444b89b79c345efadae406", | |
| "457f5ca0cc884a18b15e6cb284bf6517", | |
| "11e45481a82e49389cb487250ba92f5e", | |
| "45c508cfeb2548c99c977911667055aa", | |
| "be9790b1f33443b4b55fad7b78b11cdc", | |
| "09fc0bc7233b4e36a69bcc28e239b25b", | |
| "724586c117ae4d13a1760e7741b68a39", | |
| "653c346b750940e68c722491d8101728", | |
| "8c01205f7b39430d99a162288a0ccfba", | |
| "584e2b86973a415380a3fd2190398dbd", | |
| "41f3491469b04a5a87cfe4d104b41d73", | |
| "74d17b5fa96746d88e746d08e4faf064", | |
| "d57a95c7571040c79ef5156696a7c121", | |
| "ddb29e9fcef54fe09f49af7c8397a7c7", | |
| "5b8a5165466c4e0892cbf197996bf59f", | |
| "8fe6543709c946609c9bcc2646bb5d69", | |
| "906eb8a6e4264489b0e6950ed910d3f0", | |
| "3cbd739d71e54e85a4cca0369265371e", | |
| "dc7fbd8d142a41c4936925f334584e78", | |
| "d5c84d01f588480bbdcaeec6d425307a", | |
| "8811fe9cd71649b0a04068cbd84ef4ee", | |
| "d55c61794c70460ba19c2301f7c046f1", | |
| "0dfd8ae37c4649f6bf1fe8af2e86b2fd", | |
| "9eeb8c8628164c1cabe4cb8f5fa74024", | |
| "3cb466b27e754948883bbe4390dc9140", | |
| "0b9d95d9984e4d5299adeeb47fed85ec", | |
| "a47ff7fe2277458a96ea38f3245d15c1", | |
| "ac30f315de7f4321a2df87b06edb4545", | |
| "a50bbe10f3904b5993bd05e2f2796c12", | |
| "03604a9412304597a82792727a85a601", | |
| "c2e79b4e633345188bb6291109123242", | |
| "71c98ddef3534734a61ba6d30c5a0232", | |
| "07dd40a45b4c4f41b69ce01a186786ce", | |
| "84bd43320a634fbfa65181bc710ea225", | |
| "66a1966ec80241bba9fdb4e26ca0e610", | |
| "d32944d7ffc6487b80b1d7dc66551de4", | |
| "3b518431fdbf41cb986fb7e835d035ec", | |
| "a432a9b687e246dab6a9a94556120965" | |
| ] | |
| }, | |
| "outputId": "5e5a5925-96ad-4e91-93b8-4365a38486bc" | |
| }, | |
| "source": [ | |
| "num_files = 5\n", | |
| "\n", | |
| "for _ in range(num_files):\n", | |
| " ai.generate_to_file(n=1000,\n", | |
| " batch_size=50,\n", | |
| " prompt=\"ROMEO:\",\n", | |
| " max_length=256,\n", | |
| " temperature=1.0,\n", | |
| " top_p=0.9)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "INFO:aitextgen:Generating 1,000 texts to ATG_20221019_033249_55698324.txt\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| " 0%| | 0/1000 [00:00<?, ?it/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "ee63ddfcbfbd4c48a2966db4d07f184a" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "INFO:aitextgen:Generating 1,000 texts to ATG_20221019_033459_85957735.txt\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| " 0%| | 0/1000 [00:00<?, ?it/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "d183fab3d32d4a6a94f84cb449a02b28" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "INFO:aitextgen:Generating 1,000 texts to ATG_20221019_033707_24659432.txt\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| " 0%| | 0/1000 [00:00<?, ?it/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "09fc0bc7233b4e36a69bcc28e239b25b" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "INFO:aitextgen:Generating 1,000 texts to ATG_20221019_033915_47747389.txt\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| " 0%| | 0/1000 [00:00<?, ?it/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "906eb8a6e4264489b0e6950ed910d3f0" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "INFO:aitextgen:Generating 1,000 texts to ATG_20221019_034123_65646627.txt\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| " 0%| | 0/1000 [00:00<?, ?it/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "ac30f315de7f4321a2df87b06edb4545" | |
| } | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "wmTXWNUygS5E" | |
| }, | |
| "source": [ | |
| "# LICENSE\n", | |
| "\n", | |
| "MIT License\n", | |
| "\n", | |
| "Copyright (c) 2020-2021 Max Woolf\n", | |
| "\n", | |
| "Permission is hereby granted, free of charge, to any person obtaining a copy\n", | |
| "of this software and associated documentation files (the \"Software\"), to deal\n", | |
| "in the Software without restriction, including without limitation the rights\n", | |
| "to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n", | |
| "copies of the Software, and to permit persons to whom the Software is\n", | |
| "furnished to do so, subject to the following conditions:\n", | |
| "\n", | |
| "The above copyright notice and this permission notice shall be included in all\n", | |
| "copies or substantial portions of the Software.\n", | |
| "\n", | |
| "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n", | |
| "IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n", | |
| "FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n", | |
| "AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n", | |
| "LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n", | |
| "OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n", | |
| "SOFTWARE." | |
| ] | |
| } | |
| ] | |
| } |
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