Created
June 12, 2023 07:24
-
-
Save MichelNivard/eb742eb566075cbab6bd47ca69823bc2 to your computer and use it in GitHub Desktop.
OpenLlama 3b bnb-4bit-training.ipynb
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/MichelNivard/eb742eb566075cbab6bd47ca69823bc2/openllama-3b-bnb-4bit-training.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "XIyP_0r6zuVc" | |
| }, | |
| "source": [ | |
| "# QLoRA 4bit quantization of llama as an R tutor (tidyllama)\n", | |
| "\n", | |
| "this notebook is an adaptation of the standard 4-bit QLoRA example notebook.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "FuXIFTFapAMI", | |
| "outputId": "aeea76ec-e5c6-4481-b7fe-b9f876eabedc" | |
| }, | |
| "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", | |
| " 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", | |
| " 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" | |
| ] | |
| } | |
| ], | |
| "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 datasets\n", | |
| "!pip install -q sentencepiece\n", | |
| "!pip install -q huggingface_hub" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "izhhaZheH4QK", | |
| "outputId": "ac5c3bc7-00d8-40c8-efdb-760546c26915" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Token will not been saved to git credential helper. Pass `add_to_git_credential=True` if you want to set the git credential as well.\n", | |
| "Token is valid (permission: write).\n", | |
| "Your token has been saved to /root/.cache/huggingface/token\n", | |
| "Login successful\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "from huggingface_hub import login\n", | |
| "login(token = \"####\")\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "MJ-5idQwzvg-" | |
| }, | |
| "source": [ | |
| "First let's load the model I swapped in Llama 3b" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [], | |
| "metadata": { | |
| "id": "qiGT5bF6I3eA" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [], | |
| "metadata": { | |
| "id": "U7MVnFsQI3Nm" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "E0Nl5mWL0k2T" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import torch\n", | |
| "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig,LlamaForCausalLM, LlamaTokenizer\n", | |
| "\n", | |
| "model_id = \"openlm-research/open_llama_3b\"\n", | |
| "bnb_config = BitsAndBytesConfig(\n", | |
| " load_in_4bit=True,\n", | |
| " bnb_4bit_use_double_quant=True,\n", | |
| " bnb_4bit_quant_type=\"nf4\",\n", | |
| " bnb_4bit_compute_dtype=torch.bfloat16\n", | |
| ")\n", | |
| "\n", | |
| "tokenizer = LlamaTokenizer.from_pretrained('openlm-research/open_llama_3b')\n", | |
| "model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, device_map={\"\":0})" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "Mp2gMi1ZzGET" | |
| }, | |
| "source": [ | |
| "Then we have to apply some preprocessing to the model to prepare it for training. For that use the `prepare_model_for_kbit_training` method from PEFT." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "a9EUEDAl0ss3" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from peft import prepare_model_for_kbit_training\n", | |
| "\n", | |
| "model.gradient_checkpointing_enable()\n", | |
| "model = prepare_model_for_kbit_training(model)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "gkIcwsSU01EB" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "def print_trainable_parameters(model):\n", | |
| " \"\"\"\n", | |
| " Prints the number of trainable parameters in the model.\n", | |
| " \"\"\"\n", | |
| " trainable_params = 0\n", | |
| " all_param = 0\n", | |
| " for _, param in model.named_parameters():\n", | |
| " all_param += param.numel()\n", | |
| " if param.requires_grad:\n", | |
| " trainable_params += param.numel()\n", | |
| " print(\n", | |
| " f\"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}\"\n", | |
| " )" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "Ybeyl20n3dYH", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "f2ce6dcf-2e22-4577-bdaa-25292d07b318" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "trainable params: 2662400 || all params: 1818384000 || trainable%: 0.1464157185720948\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "from peft import LoraConfig, get_peft_model\n", | |
| "\n", | |
| "config = LoraConfig(\n", | |
| " r=8, \n", | |
| " lora_alpha=32, \n", | |
| " target_modules=[\"q_proj\", \"v_proj\"], \n", | |
| " lora_dropout=0.05, \n", | |
| " bias=\"none\", \n", | |
| " task_type=\"CAUSAL_LM\"\n", | |
| ")\n", | |
| "\n", | |
| "model = get_peft_model(model, config)\n", | |
| "print_trainable_parameters(model)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "FCc64bfnmd3j" | |
| }, | |
| "source": [ | |
| "Let's load the dataset" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "s6f4z8EYmcJ6", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 120, | |
| "referenced_widgets": [ | |
| "a436d872e8f3451c9c762f4e6a4b08b2", | |
| "a5a742c272484bf8909b6d685398f9bd", | |
| "fcb4f7e3371d4112876415b94e0d665d", | |
| "612005326bfe4f39801daa8cc6a8c2ab", | |
| "c770ba2a71f5475c97d65b22c2e912da", | |
| "c19614c2b89d42408b65be0fadecf9fe", | |
| "5e15a7f671a341dabf96978460e3626a", | |
| "c2bf24971c8c44ecb2f24a7c37e3ef3d", | |
| "2474949366634c998e6069192780611a", | |
| "dc1dc5709c174b5583eaf6c9fa2b69e5", | |
| "ddc99c20c36440fa97caecfba7e2179a" | |
| ] | |
| }, | |
| "outputId": "e9d7d53e-a2ee-461e-dba7-31b28b5c0f5c" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "WARNING:datasets.builder:Found cached dataset json (/root/.cache/huggingface/datasets/json/default-626c46e6070edf0e/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4)\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| " 0%| | 0/1 [00:00<?, ?it/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "a436d872e8f3451c9c762f4e6a4b08b2" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "WARNING:datasets.arrow_dataset:Loading cached shuffled indices for dataset at /root/.cache/huggingface/datasets/json/default-626c46e6070edf0e/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4/cache-48ff702d803d163a.arrow\n", | |
| "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/json/default-626c46e6070edf0e/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4/cache-6fe01e90ab42d98a.arrow\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "from datasets import load_dataset, concatenate_datasets\n", | |
| "\n", | |
| "dataset = load_dataset(\"json\", data_dir=\"training_data\")\n", | |
| "#ds2 = load_dataset(\"json\", data_file=\"training_data/training_data.json\")\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "dataset = dataset.shuffle(seed=42)\n", | |
| "\n", | |
| "data = dataset.map(lambda samples: tokenizer(samples['text'],max_length=2048, truncation=True), batched=True)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "_0MOtwf3zdZp" | |
| }, | |
| "source": [ | |
| "Run the cell below to run the training! For the sake of the demo, we just ran it for few steps just to showcase how to use this integration with existing tools on the HF ecosystem." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "jq0nX33BmfaC", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 368 | |
| }, | |
| "outputId": "21b24942-100e-489b-e2cb-3cedad576e1f" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "error", | |
| "ename": "ModuleNotFoundError", | |
| "evalue": "ignored", | |
| "traceback": [ | |
| "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", | |
| "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", | |
| "\u001b[0;32m<ipython-input-1-dc23b3bc1c52>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mtransformers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m# needed for gpt-neo-x tokenizer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m# tokenizer.pad_token = tokenizer.eos_token\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'transformers'", | |
| "", | |
| "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n" | |
| ], | |
| "errorDetails": { | |
| "actions": [ | |
| { | |
| "action": "open_url", | |
| "actionText": "Open Examples", | |
| "url": "/notebooks/snippets/importing_libraries.ipynb" | |
| } | |
| ] | |
| } | |
| } | |
| ], | |
| "source": [ | |
| "import transformers\n", | |
| "\n", | |
| "# needed for gpt-neo-x tokenizer\n", | |
| "# tokenizer.pad_token = tokenizer.eos_token\n", | |
| "\n", | |
| "# needed for llama tokenizer\n", | |
| "tokenizer.add_special_tokens({'pad_token': '[PAD]'})\n", | |
| "\n", | |
| "trainer = transformers.Trainer(\n", | |
| " model=model, \n", | |
| " train_dataset=data['train'],\n", | |
| " args=transformers.TrainingArguments(\n", | |
| " resume_from_checkpoint = True,\n", | |
| " per_gpu_train_batch_size=1,\n", | |
| " gradient_accumulation_steps=12,\n", | |
| " warmup_steps=2,\n", | |
| " num_train_epochs=2,\n", | |
| " learning_rate=1e-5,\n", | |
| " fp16=True,\n", | |
| " logging_steps=40,\n", | |
| " save_total_limit=3,\n", | |
| " output_dir=\"./tidyllama_3b_v2\",\n", | |
| " optim=\"paged_adamw_8bit\",\n", | |
| " push_to_hub=True\n", | |
| " ),\n", | |
| " data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),\n", | |
| ")\n", | |
| "model.config.use_cache = False # silence the warnings. Please re-enable for inference!\n", | |
| "\n", | |
| "trainer.train()\n", | |
| "\n", | |
| "model.config.use_cache = True\n", | |
| "model.push_to_hub(\"tidyllama_3b_v1\")\n" | |
| ] | |
| } | |
| ], | |
| "metadata": { | |
| "accelerator": "GPU", | |
| "colab": { | |
| "machine_shape": "hm", | |
| "provenance": [], | |
| "gpuType": "V100", | |
| "include_colab_link": true | |
| }, | |
| "kernelspec": { | |
| "display_name": "Python 3", | |
| "name": "python3" | |
| }, | |
| "language_info": { | |
| "name": "python" | |
| }, | |
| "widgets": { | |
| "application/vnd.jupyter.widget-state+json": { | |
| "a436d872e8f3451c9c762f4e6a4b08b2": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HBoxModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HBoxModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HBoxView", | |
| "box_style": "", | |
| "children": [ | |
| "IPY_MODEL_a5a742c272484bf8909b6d685398f9bd", | |
| "IPY_MODEL_fcb4f7e3371d4112876415b94e0d665d", | |
| "IPY_MODEL_612005326bfe4f39801daa8cc6a8c2ab" | |
| ], | |
| "layout": "IPY_MODEL_c770ba2a71f5475c97d65b22c2e912da" | |
| } | |
| }, | |
| "a5a742c272484bf8909b6d685398f9bd": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HTMLModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HTMLModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HTMLView", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_c19614c2b89d42408b65be0fadecf9fe", | |
| "placeholder": "", | |
| "style": "IPY_MODEL_5e15a7f671a341dabf96978460e3626a", | |
| "value": "100%" | |
| } | |
| }, | |
| "fcb4f7e3371d4112876415b94e0d665d": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "FloatProgressModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "FloatProgressModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "ProgressView", | |
| "bar_style": "success", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_c2bf24971c8c44ecb2f24a7c37e3ef3d", | |
| "max": 1, | |
| "min": 0, | |
| "orientation": "horizontal", | |
| "style": "IPY_MODEL_2474949366634c998e6069192780611a", | |
| "value": 1 | |
| } | |
| }, | |
| "612005326bfe4f39801daa8cc6a8c2ab": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "HTMLModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_dom_classes": [], | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "HTMLModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/controls", | |
| "_view_module_version": "1.5.0", | |
| "_view_name": "HTMLView", | |
| "description": "", | |
| "description_tooltip": null, | |
| "layout": "IPY_MODEL_dc1dc5709c174b5583eaf6c9fa2b69e5", | |
| "placeholder": "", | |
| "style": "IPY_MODEL_ddc99c20c36440fa97caecfba7e2179a", | |
| "value": " 1/1 [00:00<00:00, 66.10it/s]" | |
| } | |
| }, | |
| "c770ba2a71f5475c97d65b22c2e912da": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "c19614c2b89d42408b65be0fadecf9fe": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "5e15a7f671a341dabf96978460e3626a": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "DescriptionStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "DescriptionStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "description_width": "" | |
| } | |
| }, | |
| "c2bf24971c8c44ecb2f24a7c37e3ef3d": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "2474949366634c998e6069192780611a": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "ProgressStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "ProgressStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "bar_color": null, | |
| "description_width": "" | |
| } | |
| }, | |
| "dc1dc5709c174b5583eaf6c9fa2b69e5": { | |
| "model_module": "@jupyter-widgets/base", | |
| "model_name": "LayoutModel", | |
| "model_module_version": "1.2.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/base", | |
| "_model_module_version": "1.2.0", | |
| "_model_name": "LayoutModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "LayoutView", | |
| "align_content": null, | |
| "align_items": null, | |
| "align_self": null, | |
| "border": null, | |
| "bottom": null, | |
| "display": null, | |
| "flex": null, | |
| "flex_flow": null, | |
| "grid_area": null, | |
| "grid_auto_columns": null, | |
| "grid_auto_flow": null, | |
| "grid_auto_rows": null, | |
| "grid_column": null, | |
| "grid_gap": null, | |
| "grid_row": null, | |
| "grid_template_areas": null, | |
| "grid_template_columns": null, | |
| "grid_template_rows": null, | |
| "height": null, | |
| "justify_content": null, | |
| "justify_items": null, | |
| "left": null, | |
| "margin": null, | |
| "max_height": null, | |
| "max_width": null, | |
| "min_height": null, | |
| "min_width": null, | |
| "object_fit": null, | |
| "object_position": null, | |
| "order": null, | |
| "overflow": null, | |
| "overflow_x": null, | |
| "overflow_y": null, | |
| "padding": null, | |
| "right": null, | |
| "top": null, | |
| "visibility": null, | |
| "width": null | |
| } | |
| }, | |
| "ddc99c20c36440fa97caecfba7e2179a": { | |
| "model_module": "@jupyter-widgets/controls", | |
| "model_name": "DescriptionStyleModel", | |
| "model_module_version": "1.5.0", | |
| "state": { | |
| "_model_module": "@jupyter-widgets/controls", | |
| "_model_module_version": "1.5.0", | |
| "_model_name": "DescriptionStyleModel", | |
| "_view_count": null, | |
| "_view_module": "@jupyter-widgets/base", | |
| "_view_module_version": "1.2.0", | |
| "_view_name": "StyleView", | |
| "description_width": "" | |
| } | |
| } | |
| } | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 0 | |
| } |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment